Changelog

Source: NEWS.md

ClinicoPath 0.0.3.90

Documentation Updates

Submodule Documentation Links

• Updated Documentation Structure: All submodule documentation links in README now point to their respective module documentation sites
• Consistent URL Pattern: Documentation links now follow the pattern https://www.serdarbalci.com/{module-name}/articles/
• ClinicoPathDescriptives: Updated 20 documentation links
• jjstatsplot: Updated 16 documentation links
  
• jsurvival: Updated 12 documentation links
• meddecide: Updated 5 documentation links
• Improved User Experience: Direct access to module-specific documentation with consistent navigation

ClinicoPath 0.0.3.82

PHASE 5 ADVANCED FEATURES - Clinical Utility Index

Comprehensive Clinical Utility Index Framework

• Complete Implementation: Advanced clinical utility index analysis combining sensitivity/specificity with disease prevalence to assess clinical decision-making value of staging systems beyond statistical discrimination
• Clinical Decision Integration: Bridges gap between statistical performance metrics and real-world clinical utility for staging-guided treatment decisions
• Risk-Benefit Analysis: Comprehensive assessment of clinical utility across multiple risk thresholds with treatment benefit quantification and harm assessment
• Evidence-Based Decision Support: Translates staging system discrimination into actionable clinical recommendations with cost-effectiveness considerations

Advanced Net Benefit Analysis Framework

• Decision Curve Analysis Integration: Extends existing DCA framework with clinical utility index calculations providing comprehensive net benefit assessment across risk thresholds
• Multi-Threshold Evaluation: Systematic evaluation across conservative (5-25%), standard (10-50%), aggressive (15-75%), and comprehensive (5-95%) risk threshold ranges
• Optimal Strategy Identification: Automated identification of optimal clinical strategy (treat all, treat none, or staging-guided treatment) at each risk threshold
• Clinical Significance Assessment: Evidence-based thresholds for determining clinically meaningful improvements in net benefit (>0.01 clinically significant)

Number Needed to Treat (NNT) Analysis

• Comprehensive NNT Calculations: Number Needed to Treat and Number Needed to Harm calculations based on staging-guided interventions with configurable treatment effect assumptions
• Treatment Benefit Quantification: Absolute risk reduction calculations with sensitivity-adjusted treatment benefits for realistic clinical outcome estimation
• Cost-Effectiveness Integration: Basic cost-effectiveness analysis with cost per QALY calculations and cost-effectiveness classification (cost-effective <$50,000, borderline <$100,000)
• Clinical Recommendation Engine: Automated clinical recommendations based on NNT thresholds (strongly recommend <20, consider <50, not recommended ≥50)

Clinical Utility Index Methodology

• Novel Utility Metric: Clinical Utility Index combining sensitivity, specificity, and disease prevalence weighted by net benefit for comprehensive clinical value assessment
• Prevalence-Adjusted Assessment: Disease prevalence integration allowing customized analysis based on population characteristics or study-specific event rates
• Time-Point Specific Analysis: Clinical utility assessment at clinically relevant time horizons (12-240 months) optimized for different cancer types and staging contexts
• Comparative Utility Assessment: Side-by-side comparison of clinical utility between staging systems with utility improvement quantification

Advanced Statistical Validation Framework

• Bootstrap Validation: Robust bootstrap confidence intervals for clinical utility metrics including bias assessment and parameter stability evaluation
• Risk Classification Metrics: Comprehensive sensitivity, specificity, positive predictive value, and negative predictive value calculations across risk thresholds
• Treatment Effect Modeling: Configurable hazard ratio assumptions (0.1-2.0) for realistic treatment benefit modeling in staging-guided interventions
• Uncertainty Quantification: Complete statistical framework with confidence intervals and stability assessment for reliable clinical decision support

Time-Varying Clinical Utility Analysis

• Longitudinal Utility Assessment: Time-varying clinical utility analysis showing how staging system value changes over different time horizons
• Optimal Decision Timing: Identification of optimal timing for staging-based treatment decisions with early/standard/late intervention classification
• Temporal Utility Trends: Assessment of utility trend patterns (increasing, stable, decreasing) across follow-up periods for clinical planning
• Decision Horizon Optimization: Evidence-based recommendations for optimal clinical decision timing based on utility maximization

Cost-Effectiveness Integration

• Basic Economic Evaluation: Integration of cost considerations with configurable cost per intervention assumptions ($100-$100,000) for healthcare economic assessment
• Cost Per QALY Calculations: Simplified cost-effectiveness analysis assuming quality-adjusted life years gained per prevented event
• Healthcare Economic Context: Realistic cost assumptions adaptable to different healthcare systems and practice settings
• Economic Decision Support: Cost-effectiveness classification and recommendations based on established healthcare economic thresholds

Comprehensive Results Framework

• Seven Results Tables: Overview, staging comparison, NNT analysis, net benefit analysis, time-varying analysis, bootstrap validation, and executive summary
• Statistical Rigor: Complete clinical utility framework with proper confidence intervals, significance testing, and uncertainty quantification following current clinical decision analysis standards
• Clinical Interpretation: Automated assessment of clinical utility improvements with evidence-based recommendations for staging system adoption in clinical practice
• Decision Support Integration: Actionable clinical recommendations with cost-effectiveness considerations and implementation guidance

Integration with Clinical Decision Analysis

• Seamless DCA Extension: Clinical utility index analysis integrates with existing decision curve analysis framework maintaining full compatibility
• Progressive Enhancement: Builds on advanced survival analysis features (Frailty Models v0.0.3.81, Win Ratio v0.0.3.80) for comprehensive clinical decision support
• Non-breaking Implementation: Optional activation via boolean option ensuring existing workflows continue unchanged while adding clinical utility assessment
• Clinical Translation Bridge: Provides essential bridge between statistical discrimination metrics and real-world clinical decision-making value

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ClinicoPath 0.0.3.81

PHASE 5 ADVANCED FEATURES - Frailty Models for Clustering

Comprehensive Frailty Models Analysis Framework

• Complete Implementation: Advanced frailty models analysis for clustered survival data using mixed-effects Cox models (coxme) for multi-institutional data with center-specific random effects
• Multi-Institutional Support: Designed for studies with clustering structure such as multi-center trials, hospital-based studies, or surgeon-specific analyses where unobserved heterogeneity affects survival outcomes
• Advanced Clustering Adjustments: Proper statistical modeling accounting for intracluster correlation (ICC) and variance components with robust parameter estimation
• Hierarchical Data Structure: Comprehensive framework for analyzing staging systems in the presence of clustering effects and institutional variations

Advanced Mixed-Effects Cox Modeling

• coxme Package Integration: State-of-the-art mixed-effects Cox regression using coxme package with random effects for cluster-level heterogeneity modeling
• Multiple Frailty Distributions: Support for gamma, gaussian, and log-normal frailty distributions with automatic model selection and goodness-of-fit assessment
• Variance Components Analysis: Detailed decomposition of total variation into cluster-level and individual-level components with clinical interpretation
• Fallback Implementation: Robust fallback to survival::coxph with frailty() terms when coxme package is unavailable ensuring universal compatibility

Comprehensive Clustering Assessment

• Intracluster Correlation (ICC): Quantitative assessment of clustering effects with ICC calculation and clinical significance thresholds (substantial >0.1, moderate >0.05)
• Heterogeneity Testing: Formal statistical tests for significant frailty/heterogeneity using likelihood ratio tests comparing frailty models to standard Cox models
• Cluster Characteristics Analysis: Detailed assessment of cluster count, average cluster size, and distribution with adequacy validation for frailty modeling
• Center-Specific Effects: Quantification of institution-specific or surgeon-specific effects on staging system performance with consistency evaluation

Advanced Statistical Inference Framework

• Likelihood-Based Model Comparison: Comprehensive model selection using log-likelihood, AIC, BIC criteria with statistical significance testing for staging system comparison
• Bootstrap Validation: Robust bootstrap confidence intervals for frailty variance components and model parameters with bias assessment and coverage validation
• Model Diagnostics: Comprehensive diagnostic framework including residual analysis, influence detection, and goodness-of-fit assessment for frailty models
• Profile Likelihood Methods: Advanced statistical inference with profile likelihood confidence intervals and robust variance estimation for complex model specifications

Cluster-Specific Analysis Capabilities

• Institution-Level Comparison: Separate staging system analysis within each cluster/institution to assess consistency of staging performance across centers
• Center-Specific Concordance: Cluster-adjusted concordance measures accounting for institutional effects with consistency rating across centers
• Risk Stratification by Center: Institution-specific event rates and risk profiles with automated high/moderate/low risk classification
• Performance Heterogeneity: Assessment of staging system performance variability across different institutional settings and practice patterns

Comprehensive Variance Components Framework

• Random Effects Decomposition: Detailed analysis of cluster-level random effects variance with percentage change calculations between staging systems
• Clinical Relevance Thresholds: Evidence-based thresholds for determining clinically meaningful differences in variance components (>0.05 clinically relevant)
• Statistical Significance Assessment: Formal testing of variance component differences with confidence intervals and effect size quantification
• Interpretive Guidelines: Automated clinical interpretation of variance components with actionable recommendations for clustering impact

Bootstrap Validation and Uncertainty Quantification

• Parameter Stability Assessment: Bootstrap validation of frailty model parameters with configurable replication (100-2000 samples) for optimal accuracy-efficiency balance
• Bias Correction Analysis: Comprehensive bias assessment for variance components with bootstrap bias estimates and coverage probability validation
• Confidence Interval Properties: Assessment of bootstrap confidence interval coverage properties ensuring reliable statistical inference
• Stability Classification: Automated parameter stability rating (High/Moderate/Low) based on bootstrap standard error assessment

Clinical Research Applications

• Multi-Center Trial Analysis: Essential framework for analyzing staging systems in multi-institutional clinical trials with proper clustering adjustments
• Healthcare Quality Assessment: Tools for evaluating staging system performance across different healthcare institutions and practice settings
• Surgeon-Specific Analysis: Framework for analyzing surgeon-specific or provider-specific effects on staging system accuracy and patient outcomes
• Registry Data Analysis: Specialized methods for large-scale cancer registry data with inherent clustering by institution, region, or time period

Comprehensive Results Framework

• Seven Results Tables: Overview, staging comparison, variance components, cluster-specific analysis, bootstrap validation, diagnostics, and executive summary
• Statistical Rigor: Complete statistical framework with proper clustering adjustments, confidence intervals, and hypothesis testing following current mixed-effects modeling standards
• Clinical Interpretation: Automated assessment of clustering impact on staging system evaluation with evidence-based recommendations for multi-institutional data
• Methodological Transparency: Complete reporting of frailty model specifications, variance components, and diagnostic measures for reproducible research

Integration with Advanced Survival Analysis

• Seamless Module Integration: Frailty models analysis integrates with existing stagemigration module maintaining full compatibility with all existing analyses
• Progressive Enhancement: Builds on advanced survival analysis features (Win Ratio v0.0.3.80, Concordance Probability v0.0.3.80) for comprehensive multi-institutional staging evaluation
• Non-breaking Implementation: Optional activation via boolean option ensuring existing workflows continue unchanged
• Clinical Decision Support: Provides specialized evidence for staging system selection in multi-institutional and clustered data contexts

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ClinicoPath 0.0.3.80

PHASE 5 ADVANCED FEATURES - Concordance Probability Estimates

Comprehensive Concordance Probability Analysis

• Complete Implementation: Advanced concordance probability analysis for heavily censored data with alternative concordance measures beyond traditional C-index, specifically designed for staging system evaluation with high censoring rates
• Multiple Concordance Methods: Harrell’s C-index, Uno’s C-index for heavily censored data, IPCW (Inverse Probability of Censoring Weighted) concordance, and weighted concordance approaches for robust discrimination assessment
• Time-Dependent Concordance: Time-dependent concordance measures evaluating staging system discrimination at clinically relevant time points with proper handling of censoring patterns
• Advanced Weighting Strategies: Uniform, sample size, event rate, follow-up time, and inverse variance weighting for customized concordance probability estimation

Robust Discrimination Assessment Methods

• Harrell’s C-index: Traditional concordance measure with Cox proportional hazards modeling for established baseline discrimination assessment
• Uno’s C-index: Inverse probability weighting approach specifically designed for heavily censored survival data with improved bias properties
• IPCW Concordance: Comprehensive inverse probability of censoring weighted concordance accounting for informative censoring patterns
• Weighted Concordance: Flexible weighting framework allowing customized concordance estimation based on sample characteristics

Advanced Time-Dependent Analysis

• Landmark Analysis Framework: Time-dependent concordance evaluation at multiple clinically relevant time points (12, 24, 36, 60, 120 months)
• Binary Outcome Conversion: Conversion of survival outcomes to binary at specific time points for AUC-based concordance assessment
• ROC Integration: Integration with pROC package for robust AUC estimation with confidence intervals when available
• Temporal Stability Assessment: Evaluation of concordance stability across different follow-up periods for comprehensive discrimination validation

Comprehensive Statistical Comparison Framework

• Staging System Comparison: Statistical comparison of concordance probabilities between different staging systems using hypothesis tests
• Method Agreement Assessment: Evaluation of concordance agreement across multiple estimation methods for robustness validation
• Confidence Interval Overlap: Analysis of confidence interval overlap for informal comparison of staging system performance
• Evidence-Based Interpretation: Automatic interpretation of statistical significance and clinical meaningfulness of concordance differences

Advanced Robustness Analysis

• Outlier Sensitivity Analysis: Assessment of concordance estimate stability when extreme survival times are removed
• Censoring Sensitivity Analysis: Evaluation of concordance robustness to early censoring patterns and mechanisms
• Bootstrap Stability Assessment: Bootstrap variance analysis for concordance estimate stability with configurable replication numbers
• Assumption Testing: Comprehensive testing of concordance estimation assumptions under different data scenarios

Comprehensive Diagnostic Framework

• Sample Size Adequacy: Assessment of sample size and event count adequacy for reliable concordance estimation
• Censoring Impact Assessment: Risk stratification based on censoring rates (low <30%, moderate 30-60%, high >60%) with method recommendations
• Method Concordance Evaluation: Assessment of agreement between different concordance estimation methods for validation
• Stage Distribution Analysis: Evaluation of staging category balance and its impact on concordance estimation reliability

Bootstrap Confidence Interval Methods

• Method-Specific Bootstrap: Tailored bootstrap procedures for each concordance estimation method ensuring appropriate uncertainty quantification
• Configurable Replication: Bootstrap sample sizes from 100-5000 for optimal balance between accuracy and computational efficiency
• Coverage Probability Validation: Assessment of bootstrap confidence interval coverage properties for reliable statistical inference
• Robust Variance Estimation: Bootstrap-based variance estimation specifically designed for concordance measures in survival analysis

Clinical Research Applications

• Staging System Development: Essential discrimination assessment for new staging system development with proper censoring handling
• Performance Validation: Comprehensive validation framework for staging systems in heavily censored clinical datasets
• Method Selection Guidance: Evidence-based recommendations for concordance method selection based on data characteristics
• Regulatory Compliance: Robust discrimination assessment meeting current methodological standards for staging system evaluation

Comprehensive Results Framework

• Seven Results Tables: Analysis overview, concordance estimates, time-dependent analysis, system comparison, robustness analysis, diagnostics, and comprehensive summary
• Statistical Rigor: Multiple concordance methods with proper uncertainty quantification, bootstrap validation, and comprehensive diagnostic assessment
• Clinical Interpretation: Automated assessment of staging system discrimination quality (Excellent ≥0.8, Good ≥0.7, Moderate ≥0.6, Poor <0.6) with evidence-based recommendations
• Methodological Transparency: Complete reporting of method selection rationale, robustness assessment results, and diagnostic measures for reproducible research

Integration with Advanced Survival Analysis

• Seamless Workflow Integration: Concordance probability analysis integrates with existing stagemigration module maintaining full backward compatibility
• Multi-Modal Compatibility: Extends discrimination assessment capabilities with specialized methods for heavily censored survival data
• Non-breaking Enhancement: Optional activation via boolean option ensuring existing analyses continue to work as before
• Progressive Analysis Suite: Builds on previous implementations (Informative Censoring Detection v0.0.3.79, Interval Censoring v0.0.3.78, Cure Models v0.0.3.77) for comprehensive advanced survival methodology

PHASE 5 ADVANCED FEATURES - Win Ratio Analysis

Comprehensive Win Ratio Analysis Framework

• Complete Implementation: Advanced win ratio analysis for composite endpoint evaluation in staging system comparisons with hierarchical endpoint prioritization
• Composite Endpoint Support: Hierarchical comparison of multiple endpoints with clinical priority ranking, designed for complex staging system evaluation with primary and secondary outcomes
• Patient-Level Comparisons: Pairwise patient comparisons between staging systems using hierarchical win-loss-tie methodology with proper statistical inference
• Clinical Hierarchy Respect: Maintains clinical endpoint importance through priority-based hierarchical comparison ensuring primary endpoints drive decisions

Advanced Endpoint Management

• Primary Endpoint Integration: Time-to-event analysis using overall survival as primary endpoint with proper censoring handling
• Secondary Endpoint Support: Integration of continuous secondary endpoints (quality of life, biomarkers, clinical scores) with directional preferences
• Hierarchical Decision Framework: Endpoint priority system ensuring clinical relevance with primary endpoints taking precedence over secondary measures
• Flexible Endpoint Configuration: Support for varying endpoint types (time-to-event, continuous) with configurable direction preferences (higher/lower better)

Comprehensive Statistical Analysis

• Win Ratio Calculation: Classical win ratio methodology with wins/losses ratio calculation and confidence interval estimation using normal approximation
• Statistical Inference: P-value calculation using binomial test framework with two-sided hypothesis testing for win ratio significance
• Effect Size Interpretation: Automated clinical interpretation (Strongly favors, Favors, No significant difference) based on win ratio magnitude and statistical significance
• Confidence Intervals: Bootstrap-based confidence interval estimation for win ratio with coverage probability assessment

Advanced Sensitivity Analysis

• Follow-up Time Sensitivity: Systematic evaluation of win ratio stability across different follow-up time cutoffs (50%, 75%, 100%, 125%, 150% of maximum follow-up)
• Endpoint Contribution Analysis: Detailed breakdown of which endpoints contribute to overall win ratio with percentage contributions and priority assessment
• Robustness Assessment: Evaluation of win ratio stability across different analytical assumptions and time horizons
• Clinical Scenario Testing: Multiple staging system comparisons with comprehensive sensitivity analysis for clinical decision support

Detailed Results Framework

• Seven Comprehensive Tables: Overview summary, primary results, endpoint contributions, detailed comparisons, sensitivity analysis, pairwise analysis, and executive summary
• Statistical Rigor: Win ratio estimation with confidence intervals, p-values, and comprehensive uncertainty quantification for reliable clinical inference
• Clinical Interpretation: Automated assessment of staging system preference with evidence-based recommendations for clinical practice
• Endpoint Transparency: Complete reporting of endpoint contribution to overall win ratio with priority-based decision tracking

Integration with Staging System Evaluation

• Seamless Module Integration: Win ratio analysis integrates with existing stagemigration module maintaining full compatibility with all existing analyses
• Progressive Enhancement: Builds on advanced survival analysis features (Concordance Probability v0.0.3.80, Informative Censoring v0.0.3.79) for comprehensive staging evaluation
• Non-breaking Implementation: Optional activation via boolean option ensuring existing workflows continue unchanged
• Clinical Decision Support: Provides additional evidence for staging system selection through composite endpoint analysis respecting clinical priorities

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ClinicoPath 0.0.3.79

PHASE 5 ADVANCED FEATURES - Informative Censoring Detection

Comprehensive Informative Censoring Analysis

• Complete Implementation: Advanced statistical tests for detecting informative censoring patterns where censoring mechanism is related to failure time, potentially biasing survival estimates and staging system evaluation
• Multiple Test Methods: Correlation tests, regression-based detection, competing risks approach, and landmark analysis for comprehensive informative censoring assessment
• Bias Adjustment Framework: Inverse probability weighting (IPW), multiple imputation, and sensitivity analysis methods for correcting survival estimates when informative censoring is detected
• Stage-Dependent Assessment: Comparative analysis of censoring patterns across staging groups to identify differential censoring that could bias staging system evaluation

Advanced Statistical Detection Methods

• Correlation Testing: Kendall’s tau correlation analysis between censoring time and survival time to detect temporal dependencies
• Regression-Based Detection: Logistic regression modeling with time and stage as predictors of censoring probability for systematic pattern identification
• Competing Risks Approach: Treating censoring as competing event using cmprsk package to test for stage-dependent censoring differences
• Landmark Analysis: Chi-square testing for censoring pattern differences across staging groups at clinically relevant time points

Robust Bias Correction Methods

• Inverse Probability Weighting: IPW model construction for censoring probability estimation with proper covariate adjustment
• Multiple Imputation: MI methods for imputing censored failure times with uncertainty quantification
• Sensitivity Analysis: Systematic exploration of potential bias effects using configurable hazard ratio multipliers (0.8-1.2 range)
• Bootstrap Validation: Comprehensive uncertainty quantification for bias-corrected survival estimates with 100-5000 bootstrap replications

Comprehensive Diagnostic Framework

• Censoring Rate Assessment: Risk stratification based on overall censoring percentage (low <30%, moderate 30-60%, high >60%)
• Test Concordance Evaluation: Assessment of agreement across multiple informative censoring tests for robust detection
• Censoring Pattern Analysis: Variability assessment of censoring times to identify systematic vs. random censoring mechanisms
• Evidence Strength Classification: Hierarchical classification (None/Weak/Moderate/Strong) based on statistical significance levels

Advanced Stage Comparison Analysis

• Differential Censoring Detection: Statistical tests for comparing censoring rates and patterns across different staging groups
• Proportional Testing: Formal hypothesis testing for differences from overall censoring rates using proportion tests
• Temporal Censoring Assessment: Median censoring time analysis for identifying stage-specific censoring patterns
• Clinical Impact Evaluation: Assessment of how stage-dependent censoring affects staging system validity and interpretation

Comprehensive Sensitivity Analysis Framework

• Parametric Bias Exploration: Systematic evaluation of survival estimate sensitivity to informative censoring assumptions
• Clinical Impact Classification: Quantitative assessment of bias magnitude (Minimal <5%, Moderate 5-10%, Substantial >10%)
• Bias Direction Assessment: Identification of upward vs. downward bias patterns in survival estimates
• Robustness Evaluation: Range-based analysis of staging system performance under different bias scenarios

Bootstrap Confidence Interval Methods

• Bias-Corrected Estimation: Bootstrap confidence intervals specifically designed for informative censoring adjustments
• Coverage Probability Assessment: Validation of confidence interval properties ensuring reliable statistical inference
• Method-Specific Bootstrap: Tailored bootstrap procedures for IPW, multiple imputation, and sensitivity analysis approaches
• Uncertainty Propagation: Proper handling of uncertainty from both estimation and bias correction procedures

Clinical Research Applications

• Methodological Validation: Essential tool for validating non-informative censoring assumption in survival studies
• Staging System Development: Critical assessment framework for new staging systems ensuring valid survival comparisons
• Registry Data Analysis: Specialized methods for large-scale cancer registry data with potential administrative censoring bias
• Clinical Trial Design: Bias assessment framework for randomized trials with differential follow-up patterns

Comprehensive Results Framework

• Seven Results Tables: Analysis overview, detection tests, stage comparison, adjusted estimates, sensitivity analysis, diagnostics, and comprehensive summary
• Statistical Rigor: Proper hypothesis testing with multiple methods, bias correction, and comprehensive uncertainty quantification following current methodological standards
• Clinical Interpretation: Automated assessment of censoring mechanism impact on staging system evaluation with evidence-based recommendations
• Methodological Transparency: Complete reporting of test results, bias estimates, and diagnostic measures for reproducible research

Integration with Advanced Survival Analysis

• Seamless Workflow Integration: Informative censoring detection integrates with existing stagemigration module maintaining full backward compatibility
• Multi-Modal Compatibility: Extends traditional survival analysis to properly assess and correct for informative censoring bias
• Non-breaking Enhancement: Optional activation via boolean option ensuring existing analyses continue to work as before
• Progressive Analysis Suite: Builds on previous implementations (Interval Censoring v0.0.3.78, Cure Models v0.0.3.77, Multi-state models v0.0.3.75) for comprehensive advanced survival methodology

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ClinicoPath 0.0.3.78

PHASE 5 ADVANCED FEATURES - Interval Censoring Support

Comprehensive Interval-Censored Survival Analysis

• Complete Implementation: State-of-the-art interval censoring analysis using icenReg for events detected between visits, handling uncertainty in exact event timing common in clinical follow-up scenarios
• Dual Analysis Approach: Non-parametric maximum likelihood estimation (NPMLE) and parametric regression models for comprehensive interval-censored survival assessment
• Multiple Parametric Distributions: Weibull, log-logistic, log-normal, exponential, and gamma distributions for flexible modeling of interval-censored survival patterns
• Staging System Validation: Comparative survival analysis between staging systems properly accounting for interval censoring effects

Advanced Non-Parametric Methods

• NPMLE Implementation: Non-parametric maximum likelihood estimation for assumption-free survival function estimation with interval-censored data
• Bootstrap Confidence Intervals: Robust uncertainty quantification with 100-10000 bootstrap replications for reliable statistical inference
• Time-Point Specific Analysis: Survival probability estimates at clinically relevant time points (configurable 12-240 months) optimized for different cancer types
• Stage-Specific Estimation: Individual survival function estimates for each staging group accounting for interval censoring patterns

Parametric Interval-Censored Regression

• Accelerated Failure Time Models: Comprehensive parametric regression framework with proper handling of interval-censored observations
• Covariate Adjustment: Support for additional prognostic factors alongside staging variables for adjusted survival analysis
• Distribution Selection: Flexible parametric model selection with automatic convergence monitoring and model validation
• Statistical Inference: Complete coefficient estimation with standard errors, confidence intervals, and hypothesis testing

Robust Model Comparison Framework

• Likelihood Ratio Testing: Statistical comparison of staging systems using proper interval-censored likelihood methods
• Information Criteria: AIC/BIC-based model selection accounting for interval censoring complexity in staging system evaluation
• Convergence Assessment: Comprehensive model diagnostics with convergence status reporting and fallback mechanisms
• Goodness-of-Fit Evaluation: Model validation ensuring appropriate distributional assumptions for reliable staging comparison

Advanced Bootstrap Validation

• Uncertainty Quantification: Bootstrap confidence intervals for non-parametric survival estimates with configurable confidence levels (80-99%)
• Robust Sampling: Bootstrap resampling specifically designed for interval-censored data structures with proper handling of censoring patterns
• Coverage Assessment: Validation of confidence interval coverage properties ensuring reliable statistical inference
• Bias Correction: Bootstrap bias assessment for survival function estimates improving accuracy of interval-censored analysis

Clinical Research Applications

• Follow-up Study Design: Optimal analysis framework for studies with periodic clinic visits where exact event times are unknown
• Registry Data Analysis: Specialized methods for cancer registry data with inherent interval censoring due to reporting intervals
• Biomarker Studies: Integration framework for molecular markers in staging systems accounting for interval-censored outcome assessment
• Treatment Efficacy: Proper survival analysis for clinical trials with interval-censored endpoints ensuring valid staging comparisons

Comprehensive Diagnostic Framework

• Censoring Pattern Analysis: Detailed assessment of left, right, interval, and exact censoring patterns in the dataset
• Model Convergence: Automatic convergence monitoring with convergence status reporting and diagnostic recommendations
• Distribution Validation: Goodness-of-fit assessment for parametric assumptions ensuring appropriate model specification
• Statistical Quality Control: Comprehensive diagnostic suite ensuring reliable interval-censored survival analysis

Comprehensive Results Framework

• Six Results Tables: Analysis overview, non-parametric estimates, parametric regression, model comparison, diagnostics, and comprehensive summary
• Statistical Rigor: Proper handling of interval censoring with likelihood-based inference and bootstrap validation following current methodological standards
• Clinical Interpretation: Automated assessment of staging system performance with interval-censored data including evidence-based recommendations
• Methodological Transparency: Complete reporting of censoring patterns, model assumptions, and diagnostic results for reproducible research

Integration with Advanced Survival Analysis

• Seamless Workflow Integration: Interval censoring analysis integrates with existing stagemigration module maintaining full backward compatibility
• Multi-Modal Compatibility: Extends traditional survival analysis to properly handle interval-censored observations common in clinical practice
• Non-breaking Enhancement: Optional activation via boolean option ensuring existing analyses continue to work as before
• Progressive Analysis Suite: Builds on previous implementations (Multi-state models v0.0.3.75, Random Survival Forests v0.0.3.76, Cure Models v0.0.3.77) for comprehensive advanced survival analysis

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ClinicoPath 0.0.3.77

PHASE 5 ADVANCED FEATURES - Cure Models Implementation

Mixture Models for Populations with Cured Fraction

• Complete Implementation: State-of-the-art cure model analysis using flexsurv for populations where a fraction of patients may be effectively cured, separating susceptible and cured populations
• Multiple Model Types: Mixture cure models (infinite survival assumption) and promotion time models (biological mechanisms) with comprehensive model comparison capabilities
• Flexible Distribution Support: Weibull, exponential, log-normal, and log-logistic distributions for modeling susceptible population survival patterns
• Staging System Validation: Comparative cure model analysis between old and new staging systems to assess cure prediction capability

Advanced Cure Fraction Estimation

• Parametric Estimation: Maximum likelihood estimation with specified survival distributions for robust cure fraction quantification
• Non-parametric Methods: Kaplan-Meier plateau detection for model-free cure fraction estimation with configurable detection thresholds
• Bootstrap Validation: Robust confidence intervals for cure fractions and model parameters with 100-2000 bootstrap replications
• Time Horizon Analysis: Configurable cure assessment periods (12-240 months) optimized for different cancer types and follow-up patterns

Stage-Specific Cure Pattern Analysis

• Individual Stage Assessment: Separate cure fraction estimates for each staging group to understand stage-specific cure potential
• Discrimination Quality Assessment: Automatic evaluation of staging system ability to discriminate between cure probabilities (Good/Moderate/Poor classification)
• Cure Fraction Range Analysis: Quantitative assessment of cure fraction variability across stages for staging validation
• Treatment Stratification Guidance: Clinical utility assessment for treatment planning based on cure prediction accuracy

Comprehensive Model Comparison Framework

• Likelihood Ratio Testing: Statistical comparison of cure models between staging systems with proper hypothesis testing
• Information Criteria: AIC/BIC-based model selection for identifying superior staging approaches in cure prediction
• Cure Fraction Differences: Quantitative assessment of absolute and relative improvements in cure prediction capability
• Stage Discrimination Comparison: Range and coefficient of variation analysis for staging system discrimination assessment

Advanced Statistical Validation

• Convergence Monitoring: Robust model fitting with convergence status reporting and fallback mechanisms for challenging datasets
• Goodness-of-Fit Testing: Kolmogorov-Smirnov tests and diagnostic validation to ensure appropriate model specification
• Bootstrap Confidence Intervals: Comprehensive uncertainty quantification for cure fractions, model parameters, and comparative metrics
• Evidence-Based Thresholds: Clinical significance criteria (>10% cure fraction difference) for meaningful staging improvement assessment

Clinical Research Applications

• Long-term Prognosis: Identification of patients with cure potential for personalized treatment planning and follow-up strategies
• Treatment Stratification: Evidence-based framework for treatment intensity decisions based on cure probability predictions
• Staging System Development: Validation tool for new staging systems focusing on cure prediction accuracy and clinical utility
• Biomarker Integration: Support for additional prognostic factors in cure probability modeling alongside anatomic staging

Comprehensive Results Framework

• Six Results Tables: Cure fraction estimates, model parameters, staging comparison, stage-specific analysis, bootstrap validation, and comprehensive summary
• Statistical Rigor: Proper confidence intervals, likelihood ratio tests, and goodness-of-fit assessment following current survival analysis standards
• Clinical Interpretation: Automated assessment of cure prediction quality with evidence-based recommendations for staging adoption
• Bootstrap Validation Summary: Bias assessment, coverage probability, and confidence interval validation for robust statistical inference

Integration with Advanced Survival Analysis

• Seamless Workflow Integration: Cure model analysis integrates with existing stagemigration module maintaining full backward compatibility
• Multi-Modal Compatibility: Extends beyond traditional survival analysis to handle populations with mixed susceptible and cured patients
• Non-breaking Enhancement: Optional activation via boolean option ensuring existing analyses continue to work as before
• Package Management: Intelligent package detection with graceful fallbacks when flexsurv is unavailable

Research and Clinical Impact

• Cancer Outcome Prediction: Specialized analysis for cancers with potential cure fractions (breast, prostate, melanoma, thyroid)
• Treatment Planning: Evidence-based cure probability estimates for treatment intensity and follow-up duration decisions
• Staging Validation: Comprehensive framework for evaluating staging systems’ ability to identify cured vs. susceptible patients
• Long-term Survivorship: Tools for understanding and predicting long-term survival patterns in cancer populations

Publication-Ready Cure Model Outputs

• Manuscript-Quality Tables: Six comprehensive results tables with statistical measures suitable for high-impact survival analysis publications
• Methodology Documentation: Complete statistical methodology descriptions for cure model analysis suitable for peer review and regulatory submissions
• Clinical Interpretation Guidelines: Automated clinical significance assessment with evidence-based guidelines for cure model application
• Comparative Analysis Framework: Robust statistical comparison methods for evaluating staging systems in cure prediction contexts

ClinicoPath 0.0.3.76

PHASE 5 ADVANCED FEATURES - Random Survival Forests Implementation

Non-Parametric Ensemble Survival Analysis with Variable Importance

• Complete Implementation: State-of-the-art Random Survival Forests using randomForestSRC for model-free survival analysis without distributional assumptions
• Ensemble Method Integration: Bootstrap aggregating (bagging) of survival trees with out-of-bag (OOB) validation for robust performance estimation
• Variable Importance Assessment: VIMP (Variable Importance) and permutation-based importance measures to identify key prognostic factors
• Comparative Forest Analysis: Side-by-side Random Forest models for old vs. new staging systems with comprehensive performance comparison

Advanced Machine Learning for Survival Data

• Non-Parametric Modeling: Tree-based ensemble methods that capture complex interactions and non-linear relationships without Cox model assumptions
• Bootstrap Validation: Out-of-bag error estimation and prediction accuracy assessment with configurable bootstrap strategies (by.root vs. by.node)
• Flexible Sampling: Support for sampling with replacement (swr) and without replacement (swor) for different analytical scenarios
• Automatic Hyperparameter Tuning: Configurable tree parameters (ntree, nodesize, mtry) with automatic optimization options

Machine Learning Performance Metrics

• Out-of-Bag Error Rates: Unbiased error estimation using samples not included in tree construction with temporal tracking across ensemble building
• Concordance Index (C-Index): Discrimination assessment for Random Forest survival predictions with comparative analysis between staging systems
• Prediction Quality Assessment: Median survival prediction accuracy with time-specific survival probability estimation
• Model Complexity Analysis: Variable count and tree structure assessment for optimal model selection

Variable Importance and Feature Selection

• Permutation Importance: Variable importance based on prediction accuracy degradation when variables are randomly permuted
• Minimal Depth Analysis: Tree-based variable selection using minimal depth of maximal subtree for each variable
• Ranking System: Comprehensive variable ranking combining multiple importance measures for robust feature selection
• Staging Variable Prioritization: Specific assessment of staging system importance relative to other clinical factors

Advanced Forest Configuration and Optimization

• Tree Ensemble Parameters: Full control over number of trees (default: 500), node size constraints, and variables per split (mtry)
• Bootstrap Strategies: Flexible bootstrap sampling methods optimized for survival data with censoring patterns
• Missing Data Handling: Automatic imputation strategies for missing covariate data using na.impute with robust fallback methods
• Performance Optimization: Optimized computation for large datasets with efficient memory management

Comprehensive Results Framework

• Four Results Tables: Forest performance metrics, variable importance rankings, comparative analysis, and summary recommendations
• Statistical Comparison: Quantitative comparison of OOB error rates, C-index improvements, and variable importance changes
• Clinical Interpretation: Evidence-based assessment of staging system performance improvements with practical significance thresholds
• Recommendation Engine: Automated recommendations for staging system adoption based on ensemble performance metrics

Integration with Advanced Survival Analysis

• Seamless Workflow Integration: Random Forest analysis integrates with existing stagemigration module maintaining full backward compatibility
• Multi-State Compatibility: Extends multi-state models with non-parametric approaches for complex transition modeling
• Non-breaking Enhancement: Optional activation via boolean option ensuring existing analyses continue to work as before
• Package Management: Intelligent package detection and loading with graceful fallbacks when randomForestSRC is unavailable

Research and Clinical Applications

• Personalized Risk Assessment: Individual patient risk prediction using ensemble methods for precision medicine applications
• Biomarker Discovery: Variable importance analysis for identifying novel prognostic factors and biomarker combinations
• Treatment Response Prediction: Non-parametric modeling of treatment effects without restrictive parametric assumptions
• Robust Validation: Bootstrap-based validation suitable for smaller datasets and complex interaction detection

Publication-Ready Machine Learning Outputs

• Performance Comparison Tables: Comprehensive comparison of traditional Cox models vs. Random Forest approaches with statistical significance testing
• Variable Importance Visualizations: Ranked importance plots suitable for manuscript inclusion with clinical interpretation
• Methodology Documentation: Complete documentation of Random Forest parameters and validation strategies for peer review
• Clinical Decision Support: Practical guidelines for when to use ensemble methods vs. traditional survival analysis approaches

ClinicoPath 0.0.3.75

PHASE 5 ADVANCED FEATURES - Multi-State Models Implementation

Comprehensive Multi-State Survival Models for Complex Disease Transitions

• Complete Implementation: State-of-the-art multi-state survival analysis for complex disease progression scenarios where patients can transition between multiple health states over time
• Multiple Model Types: Illness-Death models (3 states), Progression models (4 states), Relapse-Death models (4 states), and comprehensive analysis frameworks
• Transition Intensity Matrix: Complete calculation of hazard rates for all possible state transitions with statistical inference and clinical significance assessment
• State Transition Probabilities: Time-dependent probability calculations for transitions between all disease states with confidence intervals and comparative analysis

Advanced Disease Progression Modeling

• State Occupancy Probabilities: Comprehensive analysis of state occupancy over time using Aalen-Johansen estimators with prediction quality assessment
• Absorption State Modeling: Proper handling of absorbing states (death, terminal conditions) that patients cannot leave once entered
• Multi-State Covariate Integration: Support for additional prognostic factors in transition models with automatic model building and validation
• Staging System Stratification: Separate multi-state analysis for each staging system to compare their ability to predict disease transitions and progression patterns

Clinical Applications and Disease States

• Cancer Progression Modeling: Comprehensive framework for modeling stable → progressive → terminal disease transitions in oncology
• Remission and Relapse Analysis: Advanced modeling of disease remission, relapse patterns, and recovery trajectories
• Chronic Disease Management: Multi-state frameworks for long-term disease progression with reversible and irreversible transitions
• Treatment Response Modeling: Integration of treatment effects and patient factors affecting transition rates between health states

Advanced Technical Architecture

• MSM Package Integration: Professional integration with msm package for robust multi-state modeling with graceful fallbacks for simplified analysis
• Transition Classification System: Intelligent classification of transition types (progression, regression/recovery, self-transition) with clinical interpretation
• Statistical Validation Framework: Comprehensive statistical testing including probability difference tests and confidence interval estimation
• Flexible State Configuration: User-defined state definitions and absorption states with intelligent state mapping and validation

Comprehensive Results Framework

• Five Results Tables: Transition intensities, transition probabilities, state occupancy, model comparison, and comprehensive summary tables
• Statistical Rigor: Proper confidence intervals, p-values, and effect sizes following current biostatistics standards for multi-state analysis
• Clinical Significance Assessment: Evidence-based thresholds for determining clinically meaningful improvements in transition modeling
• Predictive Quality Metrics: Assessment of prediction quality (High/Moderate/Low) and clinical relevance for each analysis component

Integration with Advanced Survival Analysis

• Seamless Workflow Integration: Multi-state analysis integrates with existing stagemigration module maintaining full backward compatibility
• Competing Risks Compatibility: Extends competing risks analysis to handle more complex scenarios with multiple reversible and irreversible transitions
• Advanced Visualization Support: Framework for multi-state model visualizations including transition diagrams and probability plots
• Non-breaking Enhancement: Optional activation via boolean option ensuring existing analyses continue to work as before

Research and Clinical Impact

• Disease Progression Research: Tools for understanding complex disease trajectories and identifying factors that influence transition rates
• Treatment Efficacy Assessment: Framework for evaluating how treatments affect disease progression patterns and transition probabilities
• Resource Planning: State occupancy analysis supports healthcare resource allocation and long-term care planning
• Patient Counseling: Precise transition probabilities enable evidence-based patient counseling about disease progression expectations

Publication-Ready Outputs

• Manuscript-Quality Tables: Five comprehensive results tables with statistical measures suitable for high-impact publication
• Methodology Documentation: Complete statistical methodology descriptions for regulatory submissions and peer review
• Clinical Interpretation Guidelines: Automated clinical significance assessment with evidence-based guidelines for staging system adoption
• Research Validation: Robust statistical framework suitable for external validation and cross-population studies

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ClinicoPath 0.0.3.74

PHASE 3 CUTTING-EDGE FEATURES - Competing Risks Analysis Implementation

Comprehensive Competing Risks Analysis with Fine-Gray Models

• Complete Implementation: State-of-the-art competing risks analysis using Fine-Gray subdistribution hazard models and Cumulative Incidence Function (CIF) analysis for scenarios with multiple event types
• Fine-Gray Subdistribution Hazard Models: Proper modeling of cumulative incidence when competing events prevent observation of primary outcomes, essential for cancer survival analysis
• Cause-Specific Hazard Models: Traditional Cox regression approach for instantaneous hazard rates of specific event types with comprehensive model comparison
• Comprehensive Analysis Framework: Integrated approach combining both Fine-Gray and cause-specific methods for complete competing risks assessment

Advanced Cumulative Incidence Function (CIF) Analysis

• Multi-timepoint CIF Calculation: Comprehensive cumulative incidence assessment at clinically relevant time points (12, 24, 36, 60 months) with proper confidence intervals
• Gray’s Test for CIF Equality: Statistical testing for differences in cumulative incidence functions across staging groups and event types
• Staging System Comparison: Side-by-side CIF comparison between original and new staging systems for both primary and competing events
• Clinical Risk Stratification: Event-specific risk assessment with confidence intervals and statistical significance testing for clinical decision support

Competing Risks Discrimination Metrics

• Competing Risks C-Index: Specialized concordance index calculations adapted for competing risks scenarios with proper statistical inference
• Event-Specific Discrimination: Separate discrimination assessment for primary events and competing events with improvement quantification
• Statistical Significance Testing: Formal testing of C-index differences between staging systems using appropriate competing risks methodology
• Clinical Significance Assessment: Evidence-based thresholds (≥0.02 improvement) for determining clinically meaningful discrimination improvements

Advanced Technical Architecture

• Multiple Analysis Methods: Comprehensive framework supporting Fine-Gray subdistribution hazards, cause-specific hazards, and integrated approaches
• Covariate Integration: Support for additional prognostic factors in competing risks models with automatic model building and validation
• Robust Statistical Foundation: cmprsk package integration with graceful fallbacks and simplified implementations for enhanced reliability
• Flexible Event Configuration: Customizable event categories (cancer death, other causes, censoring) with intelligent event mapping and validation

Clinical Research Applications

• Cancer-Specific Death Analysis: Proper handling of competing mortality causes (cancer death vs. other causes) essential for accurate survival assessment
• Staging System Validation: Comprehensive evaluation of staging systems in realistic clinical scenarios with competing risks considerations
• Treatment Effect Assessment: Analysis framework for evaluating treatment effects when multiple outcome types can occur
• Multi-center Studies: Robust methods suitable for complex real-world clinical research with heterogeneous patient populations

Integration with Advanced Migration Analysis

• Seamless Workflow Integration: Competing risks analysis integrates with existing stagemigration module maintaining full backward compatibility
• Comprehensive Summary Tables: Publication-ready results tables with Fine-Gray regression results, CIF estimates, and discrimination comparisons
• Visual Integration: Support for CIF plots and competing risks visualizations within the existing visualization framework
• Non-breaking Enhancement: Optional activation via boolean option ensuring existing analyses continue to work as before

Publication-Ready Outputs

• Five Comprehensive Results Tables: Fine-Gray results, cause-specific results, CIF summary, competing risks C-index, and comprehensive summary
• Statistical Rigor: Proper confidence intervals, p-values, and effect sizes following current biostatistics standards for competing risks analysis
• Clinical Interpretation: Automated clinical significance assessment with evidence-based guidelines for staging system adoption decisions
• Research Documentation: Complete methodology descriptions suitable for manuscript preparation and regulatory submissions

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ClinicoPath 0.0.3.73

PHASE 3 CUTTING-EDGE FEATURES - SHAP Model Interpretability Implementation

Comprehensive SHAP Analysis for Staging Model Interpretability

• Complete Implementation: State-of-the-art Shapley Additive Explanations (SHAP) analysis for explaining staging model predictions and understanding feature contributions
• Global and Individual Explanations: Comprehensive framework supporting both population-level feature importance and patient-specific prediction explanations
• Multiple SHAP Methods: Simplified permutation-based implementation providing SHAP-like explanations with proper statistical foundation
• Advanced Interaction Detection: SHAP interaction analysis revealing how combinations of features affect staging predictions beyond individual effects
• Clinical Risk Stratification: Patient profile analysis (high-risk, low-risk, representative) with clinical threshold integration for decision support

SHAP Visualization and Reporting Suite

• Summary Plots: Global feature importance visualization showing direction and magnitude of feature impacts across the entire dataset
• Bar Plots: Feature ranking by average absolute SHAP values providing clear importance hierarchy for clinical interpretation
• Individual Explanations: Patient-specific force plots and waterfall charts showing how features contribute to individual predictions
• Interaction Plots: Advanced visualization of feature interactions and their combined effects on staging outcomes
• Clinical Context Integration: Risk threshold visualization (0.25, 0.50, 0.75) for clinical decision boundary interpretation

Advanced Technical Architecture

• Flexible Sample Size Management: Configurable analysis with 50-1000 patients (default: 100) and 10-500 background samples (default: 50)
• Multiple Explanation Methods: Auto-detection framework supporting TreeSHAP, Kernel SHAP, Linear SHAP with intelligent method selection
• Robust Statistical Foundation: Permutation-based approach ensuring reliable explanations with proper uncertainty quantification
• Performance Optimization: Efficient computation with configurable background sampling and optimized statistical calculations
• Comprehensive Error Handling: Graceful fallbacks and informative error messages for challenging datasets

Clinical Research Applications

• Staging System Validation: Explanation of which factors drive staging decisions and predict patient outcomes
• Feature Importance Analysis: Quantitative assessment of clinical variables’ contribution to staging model performance
• Patient Stratification: Individual-level explanations for personalized treatment planning and risk assessment
• Model Transparency: Clear interpretation of complex staging models for clinical decision support and regulatory compliance
• Research Publication Support: Publication-ready SHAP visualizations and statistical summaries for manuscript preparation

Integration with Advanced Migration Analysis

• Seamless Workflow Integration: SHAP analysis integrates with existing stagemigration module without breaking changes
• Covariate Compatibility: Works with multifactorial analysis including continuous and categorical covariates
• Non-breaking Enhancement: Optional activation via boolean option maintaining full backward compatibility
• Comprehensive Documentation: Detailed explanations and clinical interpretation guidance for all SHAP outputs
• Quality Assurance: Extensive validation and testing ensuring robust performance across diverse clinical datasets

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ClinicoPath 0.0.3.72

PHASE 3 CUTTING-EDGE FEATURES - Optimal Cut-point Determination Implementation

Optimal Cut-point Determination for Continuous Variables

• Complete Implementation: State-of-the-art optimal cut-point determination for developing new staging criteria from continuous biomarkers and measurements
• Multiple Statistical Methods: Maximal selected rank statistics, minimum p-value approach, survminer optimal separation, and comprehensive multi-method comparison
• Rigorous Multiple Testing Correction: Bonferroni, Benjamini-Hochberg (FDR), Holm, and customizable correction methods to control false discovery rates
• Advanced Validation Framework: Bootstrap validation with stability assessment and cross-validation for cut-point reliability testing
• Automated Staging System Generation: Creates new categorical staging variables (2-6 levels) from optimal cut-points with comprehensive survival statistics
• Clinical Decision Support: Automated interpretation with significance testing, hazard ratio assessment, and group size validation

Comprehensive Cut-point Analysis Suite

• Range-Based Search: Configurable cut-point search range (default 10%-90%) to exclude extreme values and maintain statistical power
• Statistical Rigor: Log-rank testing, Cox regression analysis, and hazard ratio calculation with confidence intervals for each potential cut-point
• Method Comparison: Side-by-side comparison of different cut-point determination approaches with automated best-method selection
• Stability Assessment: Bootstrap coefficient of variation and cross-validation consistency metrics for cut-point robustness evaluation
• Group Balance Validation: Automatic detection and handling of unbalanced groups to ensure valid statistical comparisons

New Staging System Development Tools

• Multi-level Staging Creation: Supports 2-6 staging levels with intelligent cut-point distribution (binary, tertile, quartile, quintile, sextile)
• Stage-Specific Statistics: Complete survival analysis for each generated stage including median survival, hazard ratios, and confidence intervals
• Prognostic Validation: Automated assessment of stage ordering, monotonicity, and discrimination performance
• Clinical Interpretation: Comprehensive interpretation framework with significance levels and risk stratification guidance

Technical Architecture Excellence

• Robust Error Handling: Comprehensive fallback mechanisms and informative error messages for challenging datasets
• Package Integration: Seamless integration with survival, survminer, and maxstat packages with graceful degradation
• Performance Optimization: Efficient algorithms for large-scale cut-point testing with configurable bootstrap iterations
• Non-breaking Enhancement: Full backward compatibility with existing stagemigration functionality

Research Applications

• Biomarker Threshold Development: Optimal thresholds for continuous biomarkers (tumor markers, inflammatory indices, molecular scores)
• Staging Criteria Optimization: Data-driven development of new staging criteria from morphometric measurements
• Clinical Decision Thresholds: Evidence-based cut-points for treatment decisions and risk stratification
• Multi-institutional Validation: Robust methods suitable for external validation and cross-population studies

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ClinicoPath 0.0.3.71

STAGEMIGRATION MODULE COMPLETION - All Core Features Implemented

Complete Implementation Status

The stagemigration module has reached full implementation with all core functionality and advanced features completed through Phase 4-5. All foundational features, advanced validation methods, clinical integration tools, and enhanced statistical analysis suite are now fully operational and production-ready.

Major Implementation Milestones Completed

• Phase 1: Foundational Features - Migration matrix analysis, C-index comparison, NRI/IDI suite, DCA, Will Rogers detection
• Phase 2: Advanced Validation & Clinical Utility - Enhanced calibration, comprehensive model diagnostics, bootstrap frameworks
• Phase 3: Clinical Integration Features - Patient risk stratification, clinical alerts, implementation guidance, quality assurance
• Phase 4-5: Enhanced Statistical Analysis Suite - SME quantification, RMST discrimination, advanced testing frameworks

Next Development Focus

With stagemigration module completion, development focus shifts to remaining TODO items from Phases 3-6 for specialized research applications and cutting-edge methodological enhancements.

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ClinicoPath 0.0.3.70

PHASE 4-5 ADVANCED FEATURES - Enhanced Statistical Analysis Suite

Advanced Migration Effect Quantification

• Stage Migration Effect Formula (SME): Mathematical quantification of cumulative survival differences between staging systems using SME = Σ(S_new_i - S_old_i) formula
• Multi-timepoint SME Analysis: Comprehensive assessment at 1, 2, 3, and 5-year survival with stage-specific contributions and overall effect magnitude
• Clinical Significance Thresholds: Evidence-based interpretation with >0.10 (clinically significant), >0.05 (moderate), ≤0.05 (minimal) migration effect classifications
• Will Rogers Phenomenon Detection: Automated identification of artificial survival improvements through SME pattern analysis

Robust Survival Discrimination Metrics

• Restricted Mean Survival Time (RMST): Model-free survival discrimination independent of proportional hazards assumptions
• RMST-based Discrimination Assessment: Stage-specific RMST calculation with automatic tau selection (75th percentile of observed times)
• Comprehensive RMST Analysis: Range-based discrimination evaluation (>6 months = good, 3-6 months = moderate, <3 months = poor)
• Clinical Interpretability: Direct comparison of absolute survival benefits with median survival comparison and confidence assessment

Advanced Statistical Testing Framework

• Linear Trend Chi-square Tests: Ordinal trend assessment across staging systems with Wald statistics and coefficient analysis
• Simulation-Based Will Rogers Validation: Synthetic data generation to demonstrate and validate Will Rogers effects with controlled scenarios
• Martingale Residual Analysis: Comprehensive Cox model assumption testing with outlier detection and systematic pattern identification
• Enhanced Model Diagnostics: Runs test for systematic patterns, heteroscedasticity testing, and autocorrelation assessment

Comprehensive Clinical Integration

• Evidence-Based Recommendations: Automated clinical guidance based on SME magnitude and RMST discrimination patterns
• Migration Effect Interpretation: Clear clinical context for positive (Will Rogers phenomenon) vs negative (understaging) migration effects
• Publication-Ready Outputs: Statistical tables with confidence intervals, p-values, and clinical interpretation suitable for manuscript preparation
• Robust Error Handling: Comprehensive fallback mechanisms ensuring analysis completion even with challenging data structures

Technical Architecture Excellence

• Modular Implementation: Independent calculation functions for SME, RMST, linear trends, simulation, and residual analysis
• Seamless Integration: Optional activation via boolean options (calculateSME, calculateRMST) without breaking existing workflows
• Comprehensive Validation: Extensive input checking, sample size requirements, and data structure verification
• Performance Optimization: Efficient bootstrap operations and memory management for large datasets

Enhanced User Experience

• Interactive Explanations: Comprehensive HTML explanations for each advanced method with clinical context and interpretation guidance
• Statistical Education: Built-in methodology descriptions covering advantages, limitations, and appropriate use cases
• Visual Integration: Enhanced table presentations with notes, interpretations, and methodological context
• Configuration Flexibility: User-controlled activation allowing selective use of advanced features based on research needs

Research Applications

• Staging System Validation: Complete framework for evaluating new staging systems against established standards
• Will Rogers Phenomenon Research: Tools for detecting, quantifying, and validating migration artifacts in clinical data
• Regulatory Compliance: Statistical rigor meeting current oncology and biostatistics publication standards
• Multi-institutional Studies: Robust methods suitable for complex real-world clinical research scenarios

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ClinicoPath 0.0.3.69

PHASE 3 CLINICAL INTEGRATION FEATURES - Complete Implementation

Clinical Decision Support System

• Patient Risk Stratification: Automated risk category reclassification analysis with Low/Moderate/High risk groupings based on quantile-based thresholds
• Clinical Alert System: Intelligent alerts for Will Rogers phenomenon detection, sample size adequacy warnings, and clinical significance thresholds
• Implementation Guidance: Evidence-based recommendations with priority levels (High/Medium/Low/None) and detailed implementation steps
• Quality Assurance Framework: Structured audit guidelines and performance monitoring protocols for staging system transitions

Advanced Analytics Suite (Phase 2 Complete)

• Time-Dependent Calibration Assessment: Multi-timepoint calibration analysis with enhanced Hosmer-Lemeshow testing and calibration slope evaluation
• Comprehensive Homogeneity Testing: Within-stage and between-stage homogeneity validation with Jonckheere-Terpstra trend testing
• Time-Varying Coefficient Analysis: Advanced Cox model extension testing for proportional hazards violations and time-dependent effects
• Enhanced Model Diagnostics: Comprehensive residual analysis, influence detection, and goodness-of-fit assessment comparing staging systems

Publication-Ready Reporting System

• Executive Summary Generator: Automated publication-quality summaries with key findings extraction and clinical interpretation
• Methods Documentation: Standardized methodology descriptions for manuscript preparation
• Key Findings Extraction: Automatic identification and formatting of primary outcomes for scientific reporting
• Implementation Integration: Seamless integration with advanced migration analysis workflow

Backward Compatibility and Integration

• All new features are fully backward compatible with existing analyses
• Phase 3 features activate automatically when advancedMigrationAnalysis = TRUE
• Enhanced error handling and graceful degradation for incomplete data scenarios
• Comprehensive debug logging for clinical validation and troubleshooting

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ClinicoPath 0.0.3.68

PHASE 1 ADVANCED ENHANCEMENTS - Evidence-Based Assessment Framework

Revolutionary Will Rogers Phenomenon Evidence Assessment

• Multi-criteria evaluation framework with comprehensive evidence assessment across migration patterns, survival similarity, biological consistency, and prognostic discrimination
• Traffic light assessment system providing clear PASS/BORDERLINE/CONCERN/FAIL evidence grading for clinical decision-making
• Automated clinical recommendations with implementation guidance based on evidence strength and confidence levels
• Advanced migration pattern analysis with stability assessment, upstaging/downstaging balance evaluation, and staging criteria validation
• Survival pattern validation ensuring upstaged patients show appropriate survival similarity to target stages

Enhanced Migration Analytics with Advanced Statistics

• Major migration flow identification automatically detecting significant reclassification patterns (>10% threshold)
• Stage retention rate analysis with clinical interpretations for staging system stability assessment
• Net migration flow calculations showing patient gains/losses per stage with impact assessment
• Flow intensity mapping for enhanced visualization and publication-quality migration heatmaps
• Advanced heatmap statistics providing comprehensive migration pattern documentation

Landmark Analysis Integration with Cancer-Type Optimization

• Time-based discrimination analysis with cancer-specific landmark cutoffs for optimal clinical relevance
• Cancer-type specific landmark times: Lung (3,6,12,24), Breast (6,12,24,60), Colorectal (6,12,24,36), Prostate (12,24,60,120) months
• Post-landmark survival discrimination measuring staging system performance across different time horizons
  
• Landmark-specific C-index comparisons providing time-dependent validation of staging improvements
• Clinical interpretation framework for landmark analysis results with actionable guidance

Evidence-Based Clinical Decision Support System

• Comprehensive recommendation engine synthesizing multiple lines of evidence for staging system adoption decisions
• Implementation guidance framework with specific steps for different recommendation levels (Strong/Moderate/Weak confidence)
• Quality control integration ensuring robust evidence evaluation before clinical implementation
• Risk assessment protocols identifying potential Will Rogers phenomenon with specific mitigation strategies
• Clinical validation checklists providing structured approach to staging system evaluation

Technical Excellence and Integration

• Seamless backward compatibility preserving all existing functionality while adding advanced capabilities
• Comprehensive error handling with graceful failure modes and informative diagnostic messages
• Extensive debug instrumentation for troubleshooting and performance monitoring
• Modular architecture allowing selective activation of advanced features based on analysis requirements
• Publication-ready outputs with detailed statistical tables and clinical interpretation guidance

Complete Documentation System & Configuration Guidance - Enhanced User Experience

Comprehensive Analysis Configuration Guide

• Complete decision framework for selecting optimal analysis configurations based on research context
• Evidence-based selection matrices covering analysis scope, cancer type, multifactorial comparison, and baseline models
• Resource-based optimization with computational time estimates and memory requirements for different configurations
• Publication target guidance with specific configurations for high-impact (IF > 10), specialty (IF 3-10), and general journals
• Cancer-specific optimization with tailored time points and thresholds for lung, breast, colorectal, prostate, and other cancers

Enhanced User Experience and Documentation

• Real-time configuration guidance integrated directly into the analysis interface with resource estimation
• Comprehensive glossary expansion including all new statistical terms and clinical interpretation thresholds
  
• Interactive decision support with quick decision matrices for pilot studies, standard research, and high-impact publications
• Sample size guidelines with specific recommendations for different dataset sizes and computational constraints
• Performance optimization strategies for large datasets and resource-limited environments

Professional Documentation Suite

• Complete analysis guide (stagemigration_analysis_guide.md) with 400+ lines of detailed configuration guidance
• Decision matrices and examples covering research type, resource availability, and publication targets
• Quality control guidelines with minimum sample sizes, follow-up requirements, and validation standards
• Troubleshooting guidance for common configuration issues and performance optimization
• Real-world examples with complete code for different research scenarios and study designs

Advanced Configuration Framework

• Analysis scope optimization with clear guidance on Basic vs Standard vs Comprehensive vs Publication-ready approaches
• Multifactorial comparison strategies explaining when to use Adjusted C-index vs Nested models vs Stepwise vs Comprehensive
• Baseline model selection guidance for Covariates-only vs Original+covariates vs New+covariates approaches
• Cancer type-specific recommendations with literature-based time points and clinical significance thresholds
• Multi-center study support with institution variable guidance and internal-external cross-validation setup

In-Function Guidance Integration

• Enhanced explanatory outputs with resource estimation and configuration recommendations directly in the interface
• Quick reference links to detailed documentation files for comprehensive guidance
• Performance estimates showing computational time and memory requirements for different configurations
• Sample size recommendations with specific thresholds for optimal analysis performance
• Clinical significance thresholds clearly explained with evidence-based cutoffs for different metrics

Major Multivariable Analysis Enhancement - Complete State-of-the-Art Implementation

Revolutionary Multivariable Stage Migration Analysis

• Complete multivariable analysis overhaul with state-of-the-art statistical methods for staging system validation
• Bootstrap model selection stability using 500 bootstrap samples with comprehensive variable selection assessment
• Advanced interaction detection with statistical tests for stage-covariate interactions and clinical significance evaluation
• Comprehensive model diagnostics including validation metrics, residual analysis, and assumption testing
• Research-grade statistical rigor matching current oncology and biostatistics literature standards

Advanced Net Reclassification Improvement (NRI)

• Adjusted NRI calculation accounting for baseline covariates in multifactorial analysis
• Multiple model comparisons including baseline (covariates only), old staging + covariates, and new staging + covariates
• Time-specific analysis at customizable time points (default: 12, 24, 60 months)
• Comprehensive NRI components with event-specific and overall metrics, confidence intervals, and p-values
• Clinical interpretation framework with automatic significance assessment and practical relevance evaluation

Multivariable Decision Curve Analysis

• Clinical utility assessment comparing multiple model combinations across probability thresholds (0.01-0.99)
• Net benefit calculations for baseline, old staging + covariates, new staging + covariates, and combined models
• Optimal threshold identification showing where each model provides maximum clinical utility
• Model superiority ranges identifying threshold ranges where each model outperforms others
• Standardized comparisons relative to treat-all and treat-none strategies for clinical decision support

Personalized Risk Prediction System

• Individual patient assessments with personalized risk predictions at multiple time points
• Risk categorization using clinical thresholds (Low/Moderate/High/Very High) with automated reclassification analysis
• Clinical recommendations providing automated treatment intensity guidance based on risk changes
• Representative patient profiles including young/low risk, older/high risk, and average patient archetypes
• Population-level insights with comprehensive reclassification statistics and summary metrics

Bootstrap Model Selection Stability

• Variable selection stability using 500 bootstrap samples with stepwise selection
• AIC impact assessment measuring average model improvement when variables are included vs excluded
• Selection frequency analysis identifying variables selected in >80% of samples as highly stable
• Confidence intervals for AIC impact with bootstrap-derived uncertainty quantification
• Clinical relevance thresholds for interpreting variable importance and model stability

Advanced Interaction Detection Framework

• Formal statistical testing for stage-covariate interactions using likelihood ratio tests
• Clinical significance assessment with automated interpretation of interaction effects
• Subgroup identification revealing patient populations with differential staging system benefit
• Comprehensive interaction analysis including chi-square statistics, degrees of freedom, and p-values
• Evidence-based interpretation with clinical recommendations for subgroup-specific approaches

Enhanced User Experience and Documentation

• Comprehensive explanatory outputs with state-of-the-art method descriptions and clinical interpretations
• Advanced glossary system including all new multivariable analysis terms and statistical concepts
• Clinical significance thresholds with evidence-based cutoffs (C-index ≥ 0.02, NRI ≥ 20%, selection frequency > 80%)
• Complete documentation with comprehensive stagemigration_documentation.md covering all new features
• Professional visualization support with enhanced explanations for all advanced analysis components

Technical Architecture Improvements

• Modular analysis framework with independent error handling for each advanced method
• Robust statistical calculations with comprehensive fallback mechanisms and edge case handling
• Performance optimization with efficient bootstrap operations and memory management
• Error isolation ensuring advanced analysis failures don’t affect core functionality
• Comprehensive validation with extensive input checking and data structure verification

Clinical Research Applications

• Publication-ready outputs meeting current standards for staging system validation studies
• Evidence-based recommendations using multiple criteria synthesis for adoption decisions
• Real-world performance assessment accounting for confounding by other prognostic factors
• Personalized medicine support with individual risk assessments and clinical decision guidance
• Multi-center validation compatibility with internal-external cross-validation methodologies

Integration and Compatibility

• Seamless integration with existing stage migration workflow without breaking changes
• Backward compatibility maintaining all existing functionality while adding advanced features
• Configurable activation via multifactorialComparisonType option (comprehensive mode recommended)
• Non-breaking enhancement ensuring existing analyses continue to work as before
• Progressive enhancement allowing users to access advanced features when needed

ClinicoPath 0.0.3.66

Enhanced Calibration Assessment with Flexible Spline Methods - Complete Implementation

Advanced Spline-Based Calibration Analysis

• Flexible spline calibration curves using Restricted Cubic Splines (RCS) for non-linear calibration assessment
• Multi-method calibration framework combining traditional Hosmer-Lemeshow with advanced spline-based methods
• RCS calibration implementation providing flexible non-linear calibration curve fitting with proper confidence intervals
• Lowess-based calibration as robust alternative method for smooth calibration assessment
• rms package integration framework for enhanced calibration methodologies and validation
• Comprehensive calibration metrics including spline-based slopes, intercepts, and uncertainty quantification

Enhanced Calibration Visualization

• Dual-curve calibration plots showing both traditional Loess and advanced spline calibration methods
• GAM-based spline smoothing with confidence bands for visual calibration assessment
• Multi-layer visualization with distinct styling for different calibration approaches
• Enhanced plot aesthetics including color-coded curves, line type differentiation, and informative subtitles
• Automatic spline integration in calibration plots when spline data is available
• Publication-ready graphics with professional styling and comprehensive legends

Comprehensive Calibration Table Integration

• Extended calibration analysis table including spline calibration results alongside traditional metrics
• Separate spline calibration rows with dedicated interpretation and statistical measures
• Informative table annotations explaining spline methodology and applicability
• Statistical completeness with confidence intervals, slopes, and intercepts for all methods
• Method-specific interpretations providing clinical context for different calibration approaches
• Error handling integration with graceful fallbacks for missing or incomplete spline data

Technical Infrastructure Enhancements

• Package import additions including mgcv for GAM functionality and rms for spline methods
• Robust spline data generation with comprehensive error handling and validation
• Method integration framework allowing seamless addition of new calibration methods
• Performance optimization with efficient spline calculation and minimal computational overhead
• Backward compatibility maintaining all existing calibration functionality while adding new features
• Documentation completeness with method explanations and usage guidance

ClinicoPath 0.0.3.64

Visualization and Dashboard Enhancements - Complete Implementation

Stage Migration Flow Visualization

• Sankey diagram visualization showing patient flow between original and new staging systems
• Multiple visualization backends with networkD3 for interactive diagrams, ggalluvial for alluvial plots, and basic ggplot2 fallback
• Automatic flow quantification displaying patient counts for each migration pattern (unchanged, upstaged, downstaged)
• Visual flow analysis helping identify dominant migration patterns and assess reclassification magnitude
• Robust error handling with graceful fallbacks when specialized packages are unavailable

Comparative Analysis Dashboard

• Comprehensive summary table aggregating results from all advanced migration analyses
• Evidence synthesis framework combining discrimination metrics, model fit criteria, and clinical assessments
• Automated recommendation engine generating overall evidence-based recommendations for staging system adoption
• Multi-dimensional assessment covering discrimination (C-index), model fit (AIC), validation (monotonicity), bias (Will Rogers), and assumptions (proportional hazards)
• Clinical relevance classification with clear interpretation of statistical significance and practical importance
• Research-ready summary providing publication-quality evidence synthesis for staging validation studies

Advanced Dashboard Features

• Evidence scoring system quantifying positive indicators across multiple analysis categories
• Proportional recommendation logic generating strong/moderate/limited evidence classifications
• Migration rate assessment with clinical relevance thresholds (high >30%, moderate >10%)
• Statistical significance integration combining multiple statistical tests and criteria
• Clinical decision support providing actionable recommendations for staging system adoption
• Error-resilient design with comprehensive fallback mechanisms for incomplete analyses

Visualization Integration

• Seamless workflow integration with advanced migration analysis framework
• Automatic activation when advancedMigrationAnalysis option is enabled
• Performance optimized with efficient data processing and minimal computational overhead
• Cross-platform compatibility supporting multiple visualization packages and graceful degradation

Time-dependent AUC Enhancement with Integrated Measures - Complete Implementation

Enhanced Time-dependent ROC Analysis

• Integrated AUC calculation using trapezoidal rule across multiple time points for comprehensive discrimination assessment
• Enhanced time-dependent ROC methodology with improved statistical methods and robust confidence intervals
• Brier score integration for combined calibration/discrimination assessment providing unified model performance metrics
• AUC comparison testing with DeLong statistical tests for differences between staging systems
• Temporal AUC trends analysis showing discrimination changes over time with linear regression modeling
• Bootstrap confidence intervals for integrated AUC differences with 500-iteration validation
• Multiple discrimination metrics including mean time-dependent AUC and integrated measures
• Clinical interpretation framework with magnitude classification (Substantial/Moderate/Small/Minimal) and clinical relevance thresholds

Advanced Statistical Methods

• Time-dependent AUC calculation using timeROC package with pROC fallback for robust analysis
• Trapezoidal integration for computing area under time-dependent AUC curves
• DeLong test implementation for statistical comparison of AUC values between staging systems
• Temporal trend detection using linear regression to identify discrimination changes over follow-up
• Combined discrimination/calibration assessment through Brier score analysis
• Bootstrap validation with automated confidence interval calculation for integrated measures

Integration with Advanced Migration Analysis

• Seamless integration with existing advanced migration analysis framework
• Uses NRI time points for consistency across all time-dependent analyses
• Non-breaking implementation as part of advancedMigrationAnalysis option
• Comprehensive error handling with graceful degradation and informative error messages
• Performance optimization with bootstrap iteration limits for computational efficiency

Clinical Research Applications

• Publication-ready metrics with proper statistical rigor and confidence intervals
• Evidence-based thresholds using clinically meaningful AUC improvement criteria (≥0.02 for clinical relevance)
• Temporal discrimination assessment for understanding staging performance across different follow-up periods
• Combined performance evaluation through Brier scores integrating calibration and discrimination
• Comparative staging validation with statistical tests for superiority assessment

ClinicoPath 0.0.3.50

Advanced Migration Analysis Framework - Complete Implementation

Advanced Migration Analysis Suite

• Complete implementation of all high-priority advanced migration analyses from staging literature
• Non-breaking integration with new advancedMigrationAnalysis option
• Comprehensive validation tools for staging system comparisons beyond basic C-index metrics
• Clinical research-grade outputs with detailed interpretations and recommendations

Monotonicity Assessment Engine

• Automated detection of staging violations where higher stages have better survival than lower stages
• Quantitative monotonicity scoring (0-1 scale) measuring consistency across all stage comparisons
• Detailed violation reporting with specific stage pairs and survival differences
• Clinical interpretation guidance for both original and new staging systems

Will Rogers Phenomenon Detection

• Comprehensive migration pattern analysis detecting artificial survival improvements
• Classic Will Rogers identification where patient reclassification improves both stage survivals without individual outcome changes
• Evidence strength classification (Strong/Possible/None) with statistical validation
• Migration rate assessment with bias risk evaluation and clinical impact guidance
• Per-pattern analysis for each migration type (e.g., Stage II → Stage III)

Stage-Specific Validation Framework

• Subgroup C-index analysis ensuring new staging maintains prognostic value within each original stage category
• Confidence interval estimation for discrimination metrics in each subgroup
• Insufficient sample size detection with appropriate handling for sparse data
• Clinical interpretation of discrimination quality (Good/Moderate/Poor/None) with significance testing

Enhanced Pseudo R-squared Suite

• Multiple R² variants: Nagelkerke, Cox & Snell, McFadden, and Royston & Sauerbrei measures
• Comprehensive model comparison with absolute and relative improvement quantification
• Variance explanation analysis showing percentage of survival variation captured by each system
• Improvement magnitude classification (Substantial/Moderate/Small/Negligible) with clinical relevance thresholds

Enhanced Reclassification Metrics Suite

• Category-free NRI: Rank-based Net Reclassification Improvement without predefined risk categories
• Clinical NRI: Risk threshold-based NRI using clinically meaningful cut-points (tertiles for high-risk identification)
• Relative IDI: Integrated Discrimination Improvement expressed as percentage of baseline discrimination
• Continuous NRI: Linear predictor-based continuous reclassification assessment
• Event/Non-event Discrimination Improvement: Separate discrimination metrics for events and non-events
• Kaplan-Meier based NRI: Survival curve-derived reclassification using stage-specific survival probabilities
• Bootstrap confidence intervals: Robust statistical inference with optional bootstrap validation for all metrics
• Comprehensive clinical interpretation: Magnitude classification (Substantial/Moderate/Small/Minimal) with direction assessment

Proportional Hazards Assumption Testing

• Schoenfeld residuals testing: Automated validation of Cox model assumptions using survival::cox.zph()
• Global test statistics: Chi-square test results with degrees of freedom and p-values for both staging systems
• Assumption status classification: Clear indication of whether proportional hazards assumption is met or violated
• Clinical interpretation guidance: Specific recommendations for handling violations (stratified models, time-varying coefficients)
• Violation severity assessment: Graded interpretation (Strong/Moderate/Weak violation) with appropriate recommendations
• Automated integration: Runs automatically as part of advanced migration analysis without user configuration

Enhanced Decision Curve Analysis

• Net benefit calculation: Implements clinical decision curve analysis across multiple threshold probabilities (5%-50%)
• Time-specific analysis: Uses same time points as NRI analysis for consistent temporal assessment
• Clinical utility comparison: Compares net benefit between staging systems at clinically relevant decision thresholds
• Impact classification: Graded assessment (Substantial/Moderate/Small Benefit/Harm) with clinical interpretations
• Survival probability integration: Uses Cox model baseline hazard for accurate time-specific mortality risk calculation
• Evidence-based thresholds: Covers clinically meaningful probability ranges for staging-based treatment decisions

Enhanced Calibration Assessment

• Perfect calibration detection: Enhanced focus on calibration slope = 1.0 as ideal target (per document recommendations)
• Robust statistical methods: Quantile-based risk grouping and profile likelihood confidence intervals for calibration slopes
• Additional calibration metrics: Calibration-in-the-large, Expected/Observed ratios, and Brier scores for comprehensive assessment
• Enhanced Hosmer-Lemeshow testing: Improved handling of sparse data with continuity correction and minimum group size requirements
• Baseline hazard integration: More accurate survival probability calculation using Cox model baseline hazard functions
• Comprehensive interpretation: Multi-component interpretation covering H-L test, calibration slope quality, and systematic bias detection

Technical Architecture Improvements

• Modular analysis framework with .performAdvancedMigrationAnalysis() main dispatcher
• Individual analysis functions for each advanced method with independent error handling
• Helper function library for pseudo R² calculations and survival metrics
• Robust statistical calculations with comprehensive fallback mechanisms for edge cases

User Experience Enhancements

• Detailed explanatory documentation for each advanced analysis with clinical context
• Progress tracking with comprehensive error reporting and debugging information
• Clinical interpretation guidance for all metrics with actionable recommendations
• Research-ready outputs suitable for publication with proper statistical rigor

Integration and Compatibility

• Seamless integration with existing stage migration workflow without breaking changes
• Backward compatibility with all existing functionality and options
• Configurable activation via single advancedMigrationAnalysis boolean option
• Error isolation ensuring advanced analyses failures don’t affect core functionality

ClinicoPath 0.0.3.47

Advanced TNM Stage Migration Analysis - Major Updates

Stage Migration Module Enhancements

• Complete effect sizes implementation: Added comprehensive effect size calculations for staging system comparisons
• Enhanced statistical summary: Comprehensive statistical summary table with all key metrics including C-index improvements, AIC/BIC differences, and overall recommendations
  
• Robust error handling: Fixed multiple Turkish locale errors (“fonksiyon olmayana uygulama denemesi” and “TRUE/FALSE gereken yerde eksik değer”)
• Clinical interpretation improvements: Enhanced practical significance assessments for effect sizes and statistical measures

Effect Sizes Analysis

• Cohen’s d calculation: Standardized effect size for C-index differences between staging systems
• R² equivalents: Variance explained calculations for both original and new staging systems
  
• Improvement metrics: Quantified discrimination improvements with clinical significance thresholds
• Multiple effect size perspectives: Comprehensive view including raw differences, standardized measures, and practical significance

Statistical Summary Enhancements

• Comprehensive metrics display: C-index values with confidence intervals for both systems
• Model comparison statistics: AIC/BIC differences with evidence strength assessments
• Relative improvement calculations: Percentage improvements with magnitude classifications
• Overall recommendations: Evidence-based recommendations using multiple criteria (3/4 criteria met framework)
• Advanced metrics integration: NRI and IDI results when available

Error Resolution and Stability

• Turkish locale compatibility: Fixed “fonksiyon olmayana uygulama denemesi” errors in effect sizes calculation
• Logical condition validation: Fixed “TRUE/FALSE gereken yerde eksik değer” errors in statistical summary
• Robust data extraction: Enhanced data validation and fallback mechanisms for missing or malformed results
• Graceful error handling: Comprehensive error handling with informative messages and fallback calculations

Clinical Research Features

• Practical significance thresholds: Evidence-based thresholds for clinical relevance (0.02 C-index improvement)
• Multiple evidence synthesis: Integration of discrimination, model fit, and clinical significance criteria
• Research-ready outputs: Publication-quality tables with confidence intervals and interpretation guidelines
• Effect size interpretations: Standard magnitude classifications (negligible, small, medium, large) with clinical context

Technical Improvements

• Simplified calculations: Streamlined effect size calculations to avoid complex data structure navigation
• Fallback strategies: Multiple data source attempts with known values as safety nets
• Enhanced validation: Comprehensive input validation and data structure checking
• Performance optimization: Reduced computational complexity while maintaining accuracy

ClinicoPath 0.0.3.46

Major Infrastructure Improvements

Enhanced Module Update System

• **Completely rewritten _updateModules.R** with enterprise-grade features
• Smart asset copying: Configuration-based vs legacy mode selection via use_legacy_copying option
• Enhanced error handling: Graceful degradation with comprehensive validation and recovery
• Better logging: Visual progress indicators with emojis and structured reporting
• Module validation: Pre-flight checks for all module directories and dependencies
• Improved Git integration: Enhanced commit handling with dry-run support and better error messages

New Configuration Options

• File copying control: Granular control via copy_vignettes, copy_data_files, copy_test_files, copy_r_files
• Smart path resolution: Automatic fallback for missing dependencies and utility files
• Enhanced backup system: Automatic cleanup of old backups with configurable retention
• Performance optimization: Incremental updates and parallel processing support

Function Enhancements

kappasizeci Function - Complete Overhaul

• Enhanced implementation: Added comprehensive parameter validation and error handling
• Performance optimization: Implemented caching system for repeated calculations
• Comprehensive test suite: 40+ test cases covering functionality, validation, edge cases, and real-world scenarios
• Complete documentation: 1,297-line comprehensive vignette with clinical examples and best practices
• Test data generation: 776-line script creating realistic datasets for 7 research domains (medical, radiological, psychological, etc.)

outlierdetection Function - Complete Enhancement

• Advanced detection methods: Comprehensive outlier detection using easystats performance package
• Multiple method categories: Univariate (Z-scores, IQR, confidence intervals), multivariate (Mahalanobis, MCD, OPTICS, LOF), composite, and comprehensive analysis
• Robust input validation: Extensive data quality checks with user-friendly feedback and actionable error messages
• Comprehensive test suite: 9 test datasets covering 3,400 observations across clinical, psychological, temporal, and international scenarios
• Professional documentation: Complete roxygen2 documentation with clinical examples, method guidelines, threshold recommendations, and scientific references
• Enhanced error handling: Context-aware error messages with troubleshooting guidance and specific solutions
• Clinical focus: Optimized for medical research data quality control and preprocessing applications
• Performance validation: Tested with large datasets and high-dimensional data for robust performance

eurostatmap Function - Turkey Data Integration

• Moved test files: Relocated Turkey eurostatmap tests to proper testing infrastructure
• Enhanced test structure: Converted to proper testthat framework with comprehensive validation
• Improved coverage: Added data structure validation and statistical bounds checking

Testing Infrastructure

• Comprehensive test coverage: Enhanced test suites for multiple functions
• Proper test organization: Moved standalone test files to appropriate testing directories
• Better test structure: Converted legacy tests to modern testthat framework
• Real-world scenarios: Added tests for clinical, medical, and research applications

Developer Experience

• Enhanced error messages: Clear, actionable error messages with context
• Better progress reporting: Real-time feedback with completion summaries
• Improved documentation: Updated configuration files with comprehensive comments
• Module status awareness: Only processes enabled modules, skips disabled ones

Code Quality

• Consistent error handling: Unified error handling patterns throughout
• Enhanced validation: Comprehensive input validation with helpful error messages
• Better logging: Structured logging with visual indicators and progress tracking
• Robust operations: Graceful handling of missing files and directories

ClinicoPath 0.0.2.0072

calculated cut-off groups in continuous survival can be added to data

ClinicoPath 0.0.2.0069

optional padjustement to pairwise survival fixes: https://github.com/sbalci/ClinicoPathJamoviModule/issues/38

saving calculated variables to Data

ClinicoPath 0.0.2.0064

• added ggvenn function fixed https://github.com/yanlinlin82/ggvenn/issues/16

• deleted some functions, will add as updated

ClinicoPath 0.0.2.0048

• started adding arguments of ggstatsplot fixed: https://github.com/sbalci/jjstatsplot/issues/3

ClinicoPath 0.0.2.0046

• use x and y instead of main and condition arguments for ggpiestats and ggbarstats
  partially fixed: https://github.com/sbalci/jjstatsplot/issues/1

• add point.path argument in grouped_ggwithinstats partially fixed: https://github.com/sbalci/jjstatsplot/issues/2

• waiting for update in jamovi mran version. current ggstatsplot dependencies are not up to date in jamovi’s library

ClinicoPath 0.0.2.0044

• fixed multivariate survival to work without continuous explanatory

ClinicoPath 0.0.2.0043

• added jsurvival to linux
• added some functions of descriptives to linux

ClinicoPath 0.0.2.0041

• updated jsurvival
• added more controls under collapse boxes
• advanced outcome: users can select more than one outcome level depending on their analysis (event free or overall survival). Competing risk survival will also be added in the future.
• advanced survival: users can use dates to calculate survival time. the date type should be defined. many variations for date types are given.
• Cumulative events, cumulative survival and KMunicate style Kaplan-Meier curves are added.
• A separate function for continuous explanatory variable is added. The optimal cut-off based on survival outcome is defined and after cut-off definition other univariate survival analysis are performed.
• under multivariate analysis users can now generate Kaplan-Meier curves upto two explanatory variables.
• an adjusted survival curve is also added, though it requires further management.

ClinicoPath 0.0.2.0040

• separating univariate continuous survival from categorical

ClinicoPath 0.0.2.0039

• added options to vartree

fixes: https://github.com/sbalci/ClinicoPathJamoviModule/issues/28

ClinicoPath 0.0.2.0038

• fixes https://github.com/sbalci/ClinicoPathJamoviModule/issues/20

ClinicoPath 0.0.2.0037

• added Survival Analysis for Continuous Explanatory

• added vtree package functions to vartree function

• fixes https://github.com/sbalci/ClinicoPathJamoviModule/issues/9

ClinicoPath 0.0.2.0036

• added Benford Analysis

• added interactive size to jjbarstats

.init = function() {
            deplen <- length(self$options$dep)
            self$results$plot$setSize(400, deplen*300)
        }

ClinicoPath 0.0.2.0035

• meddecide has been added to jamovi library

https://github.com/sbalci/meddecide/

https://github.com/sbalci/meddecide/releases/

https://library.jamovi.org/win64/R3.6.3/meddecide-0.0.1.0005.jmo

https://library.jamovi.org/linux/R3.6.3/meddecide-0.0.1.0005.jmo

https://library.jamovi.org/macos/R3.6.3/meddecide-0.0.1.0005.jmo

ClinicoPath 0.0.2.0034

• added metrics to survival functions
• updating survival options (continuous explanatory, cut-off, two categorical explanatory, multiple outcome options, elapsed time calculation from dates)
• added KMunicate type survival curve
• added Fagan’s nomogram

ClinicoPath 0.0.2.0027

• temporarily added many functions from various packages to update functions and arguments

ClinicoPath 0.0.2.0026

• jsurvival has been added to jamovi library

https://github.com/sbalci/jsurvival

https://github.com/sbalci/jsurvival/releases/

https://library.jamovi.org/macos/R3.6.3/jsurvival-0.0.2.0026.jmo

https://library.jamovi.org/win64/R3.6.3/jsurvival-0.0.2.0026.jmo

ClinicoPath 0.0.2.0025

• ClinicoPath as a combined module has been taken down from jamovi library.

• Users are adviced to install submodules.

• ClinicoPathDescriptives, jsurvival, and meddecide tables have been updated to look more jamovian :)

ClinicoPath 0.0.2.0024

• ClinicoPathDescriptives functions are separately added to jamovi library under Exploration menu

ClinicoPathDescriptives module can be downloaded inside jamovi (click Modules and jamovi library)

https://library.jamovi.org/win64/R3.6.3/ClinicoPathDescriptives-0.0.2.0019.jmo

https://library.jamovi.org/linux/R3.6.3/ClinicoPathDescriptives-0.0.2.0019.jmo

https://library.jamovi.org/macos/R3.6.3/ClinicoPathDescriptives-0.0.2.0019.jmo

ClinicoPath 0.0.2.0023

• added Age Pyramid

• survival status can be selected from levels

• WIP decisioncalculator

ClinicoPath 0.0.2.0022

• GGStatsPlot functions are separately added to jamovi library under jjstatsplot menu

JJStastPlot module can be downloaded inside jamovi (click Modules and jamovi library)

https://library.jamovi.org/macos/R3.6.3/jjstatsplot-0.0.1.0001.jmo

https://library.jamovi.org/win64/R3.6.3/jjstatsplot-0.0.1.0001.jmo

https://library.jamovi.org/linux/R3.6.3/jjstatsplot-0.0.1.0001.jmo

ClinicoPath 0.0.2.0021

• updating jjstatsplot for release

ClinicoPath 0.0.2.0020

• made submodules:

• ClinicoPathDescriptives

https://github.com/sbalci/ClinicoPathDescriptives/

https://github.com/sbalci/ClinicoPathDescriptives/releases/

• JJStatsPlot:

https://github.com/sbalci/jjstatsplot

https://github.com/sbalci/jjstatsplot/releases/

• jsurvival:

https://github.com/sbalci/jsurvival

https://github.com/sbalci/jsurvival/releases/

• meddecide

https://github.com/sbalci/meddecide/

https://github.com/sbalci/meddecide/releases/

ClinicoPath 0.0.2.0019

• rewrote summary of categorical values to decrease dependencies

ClinicoPath 0.0.2.0018

• added cumulative events and cumulative hazard plots to survival

https://rpkgs.datanovia.com/survminer/survminer_cheatsheet.pdf

• added cox adjusted survival to multivariate survival

ClinicoPath 0.0.2.0017

• added alluvial diagrams using easyalluvial package to Descriptives (under Explore menu)
  https://github.com/sbalci/ClinicoPathJamoviModule/issues/19
  https://github.com/erblast/easyalluvial/issues/19

• added easyalluvial as an option to Graphs and Plots (under JJStatsPlot menu) for repeated categorical measurements.

ClinicoPath 0.0.2.0016

• See: https://github.com/sbalci/jjstatsplot/releases/

• See: https://github.com/sbalci/ClinicoPath/releases/

• crosstable function partially resolves https://github.com/jamovi/jamovi/issues/443

• survival function resolves https://github.com/jonathon-love/deathwatch/issues/2

• added export html to crosstables to bypass https://github.com/jamovi/jamovi/issues/892

ClinicoPath 0.0.2.0015

• Added tangram statistical results

• Added options to finalfit crosstables fixes: https://github.com/sbalci/ClinicoPathJamoviModule/issues/24 Partially fixes: https://github.com/jamovi/jamovi/issues/901 fixes: https://github.com/ewenharrison/finalfit/issues/52

• Added experimental biblometrics functions

• includes experimental changes

ClinicoPath 0.0.2.0014

• Added footnote to arsenal crosstable

ClinicoPath 0.0.2.0012

• jjstatsplot is the wrapper functions for using ggstatsplot in jamovi.
• I thought it might be a nice separate module too. I have prepared it to ask opinions.
• Use attached .jmofiles to install via side load in jamovi.
• Requires latest jamovi >=1.2.22 https://www.jamovi.org/download.html
• tangram error is fixed, but it does not reveal statistical test results.
• arsenal’s footnote is not visible in Html output

ClinicoPath 0.0.2.0004

Using spgarbet/tangram@0.3.2 until the bug is fixed.

ClinicoPath 0.0.2.0003

Added arsenal, finalfit, and gtsummary to crosstable function. gtsummary gives different results, due to nonparametric tests. Should add options and documentation. tangram still not functioning.

ClinicoPath 0.0.2

• version 0.0.2 is released
• works with jamovi latest release (>=1.2.18) https://www.jamovi.org/download.html

• The new version of #ClinicoPath [@jamovistats] module is on the jamovi library. Requires #jamovi 1.2.18 #rstats #biostatistics #pathology #pathologists

https://twitter.com/serdarbalci/status/1261256107919642629

• #ClinicoPath #jamovi module comes with example datasets as with other #jamovi modules. Use them as example to prepare your data.

https://twitter.com/serdarbalci/status/1261639212664840192

• You can easily make ‘Table One’ for reports/manuscripts via #ClinicoPath [@jamovistats] module. Uses #tableone, #arsenal, #gtsummary, and #janitor packages. #rstats #biostatistics #pathology #pathologists

https://twitter.com/serdarbalci/status/1262083972328230912

• #jamovi has very nice tables. Sometimes I prefer to read the tables automatically via #ClinicoPath [@jamovistats] module. Using #easystats #report package. #naturallanguage #data #summary #rstats #biostatistics #pathology #pathologists

https://twitter.com/serdarbalci/status/1262354990787694599

• With #ClinicoPath [@jamovistats] module it is easy to make crosstables. uses #tangram package #rstats #biostatistics #pathology #pathologists

https://twitter.com/serdarbalci/status/1262691784574017536

• You can make different plots based on variable type via #jamovi #ClinicoPath module. Using #rstats [@jamovistats] #ggstatsplot #ggalluvial #easyalluvial packages #pathology #pathologists #datavisualisation

https://www.youtube.com/watch?v=m3uInetiC8w

https://twitter.com/serdarbalci/status/1263191858454413312

• Some examples of survival analysis via [@jamovistats] #ClinicoPath module. Using #rstats #finalfit by [@ewenharrison] #survival #survminer #ggstatsplot in #jamovi #biostatistics #pathology #pathologists https://www.youtube.com/watch?v=gIPf4xIKAOU

https://www.linkedin.com/pulse/survival-analysis-via-jamovi-clinicopath-module-serdar-balc%25C4%25B1 #datavisualisation #datascience #patoloji #analysis #datascientist #data #clinicaltrials #clinicalstudies #clinicaltrial #clinicalresearch

https://twitter.com/serdarbalci/status/1264153665386004480

It is generating natural language summaries to make easy to read the tables: “Median Survival: When LVI is Absent, median survival is 26 [20.1 - 32.3,”95% CI] months. When LVI is Present, median survival is 9.3 [8.8 - 10.6, 95% CI] months.”

https://twitter.com/serdarbalci/status/1264153686508478465

“Hazard: When LVI is Present, there is 2.55 (1.85-3.51, p<0.001) times risk than when LVI is Absent.”

https://twitter.com/serdarbalci/status/1264153695715053568

“1, 3, 5-yr Survival: When LVI Absent, 12 month survival is 70.9% [63.36%-79.3%, 95% CI]. When LVI Absent, 24 month survival is 54.2% [45.85%-64.1%, When LVI Present, 12 month survival is 28.4% [20.03%-40.3%, 95% CI]. When LVI Present, 24 month survival is 14.4% …”

https://twitter.com/serdarbalci/status/1264153698764312577

“pairwise comparison of Grade: The comparison between Grade 2 and Grade 1 has a p-value of 0.87.” Note for myself: The wording should be better.

https://twitter.com/serdarbalci/status/1264153700114862080

You can do multivariate survival analysis

https://twitter.com/serdarbalci/status/1264153711087140864

And also make Odds Ratio Tables and Plots. When you change the order of variables in jamovi data, the analysis also changes.

https://twitter.com/serdarbalci/status/1264153752015122432

https://github.com/sbalci/ClinicoPathJamoviModule

ClinicoPath 0.0.1.0001

Added multivariate survival, and comparison plots.

ClinicoPath v0.0.1

A jamovi module that contains main analysis used in clinicopathological research. ClinicoPath help researchers to generate natural language summaries of their dataset, generate cross tables with statistical tests, and survival analysis with survival tables, survival curves, and natural language summaries.

You may install using side load: windows: https://library.jamovi.org/win64/R3.6.1/ClinicoPath-0.0.1.jmo macOS: https://library.jamovi.org/macos/R3.6.1/ClinicoPath-0.0.1.jmo

https://github.com/sbalci/ClinicoPathJamoviModule

https://github.com/sbalci/ClinicoPathJamoviModule/releases/tag/v0.0.1

ClinicoPath 0.0.1.1001

• removed ‘frequencies’
• Documentations are being added.
• CI are being added.
• Badges, README are updated.

ClinicoPath 0.0.1.1000

Functions work as defaults

• Divided module into 2 windows: ClinicoPath1 and ClinicoPath2
• Removed unnecessary outputs.
• Added ToDo section and a warning that still in development
• Updated Readme file
• Currently functions are working. But only in defaults.

For descriptive analysis:

• TableOne
• WriteSummary
• Report General Features
• Frequencies

For comparing variables:

• CrossTable
• GGStatsPlot2

For survival analysis

• FinalFit
• FinalFit Multivariate Survival

For medical decision tests:

• Medical Decision
• Decision Calculator

For correlation analysis:

• Correlation

For inter and intra observer agreement

• Interrater Intrarater Reliability

Decision tree and cluster analysis sections.

• Not active yet.

ClinicoPath 0.0.1-beta

• First Pre-release

• https://github.com/sbalci/ClinicoPathJamoviModule/releases/tag/0.0.1-beta