Advanced clinical trial methodologies including group sequential designs, adaptive trials, and interim monitoring. Provides sophisticated statistical methods for non-inferiority trials, futility analysis, and early stopping rules. Implements cutting-edge designs such as seamless Phase II/III, platform trials, and master protocol studies. Designed for clinical trialists requiring state-of-the-art methodology and regulatory compliance.
Usage
advancedtrials(
data,
time_var,
event_var,
treatment_var,
biomarker_var,
interim_time_var,
stratification_vars,
design_type = "group_sequential",
primary_endpoint = "overall_survival",
statistical_test = "log_rank",
alpha_spending = "obrien_fleming",
beta_spending = "obrien_fleming",
number_of_looks = 3,
information_fractions = "0.5,0.75,1.0",
efficacy_boundaries = "auto",
adaptation_type = "sample_size_reestimation",
adaptation_timing = 50,
conditional_power_threshold = 0.3,
ni_margin = 1.25,
ni_margin_type = "hazard_ratio",
overall_alpha = 0.025,
overall_power = 0.8,
expected_hr = 0.75,
accrual_rate = 25,
dropout_rate = 0.05,
number_of_arms = 2,
control_arm_sharing = TRUE,
interim_arm_addition = FALSE,
interim_arm_dropping = FALSE,
biomarker_strategy = "all_comers",
biomarker_prevalence = 0.3,
futility_analysis = TRUE,
stochastic_curtailment = FALSE,
predictive_power_threshold = 0.1,
interim_monitoring_plan = TRUE,
operating_characteristics = TRUE,
boundary_plots = TRUE,
conditional_power_analysis = FALSE,
predictive_power_analysis = FALSE,
bias_adjustment = FALSE,
multiplicity_adjustment = FALSE,
simulation_runs = 10000,
seed_value = 12345
)Arguments
- data
the data as a data frame
- time_var
Time to primary endpoint (survival, progression, response)
- event_var
Event indicator (1=event, 0=censored)
- treatment_var
Treatment arm assignment (experimental vs control)
- biomarker_var
Biomarker for enrichment or stratification strategies
- interim_time_var
Calendar time of interim analysis
- stratification_vars
Variables for stratified randomization
- design_type
Advanced clinical trial design type
- primary_endpoint
Primary efficacy endpoint
- statistical_test
Statistical test for primary analysis
- alpha_spending
Alpha spending function for efficacy boundaries
- beta_spending
Beta spending function for futility boundaries
- number_of_looks
Total number of planned interim looks
- information_fractions
Information fractions for each analysis (comma-separated)
- efficacy_boundaries
Custom efficacy boundaries or 'auto' for spending function
- adaptation_type
Type of mid-trial adaptation
- adaptation_timing
Percentage of planned events for adaptation decision
- conditional_power_threshold
Threshold for sample size re-estimation
- ni_margin
Non-inferiority margin (hazard ratio scale)
- ni_margin_type
Scale for non-inferiority margin
- overall_alpha
One-sided Type I error rate
- overall_power
Overall study power
- expected_hr
Expected treatment effect (hazard ratio)
- accrual_rate
Expected patient accrual rate
- dropout_rate
Annual patient dropout rate
- number_of_arms
Total number of treatment arms
- control_arm_sharing
Share control arm across comparisons
- interim_arm_addition
Allow adding arms during trial
- interim_arm_dropping
Allow dropping arms for futility
- biomarker_strategy
Biomarker-driven design strategy
- biomarker_prevalence
Prevalence of positive biomarker
- futility_analysis
Include formal futility testing
- stochastic_curtailment
Use stochastic curtailment approach
- predictive_power_threshold
Threshold for futility based on predictive power
- interim_monitoring_plan
Generate detailed interim monitoring guidelines
- operating_characteristics
Calculate design operating characteristics via simulation
- boundary_plots
Generate efficacy and futility boundary plots
- conditional_power_analysis
Calculate conditional power at interim analyses
- predictive_power_analysis
Calculate predictive power for study completion
- bias_adjustment
Apply bias adjustment methods for adaptive designs
- multiplicity_adjustment
Apply multiplicity adjustment for multiple arms/endpoints
- simulation_runs
Number of simulations for operating characteristics
- seed_value
Random seed for reproducible simulations
Value
A results object containing:
results$instructions | a html | ||||
results$trial_design_summary | a table | ||||
results$group_sequential_boundaries | a table | ||||
results$adaptive_design_parameters | a table | ||||
results$platform_trial_design | a table | ||||
results$biomarker_strategy_results | a table | ||||
results$operating_characteristics | a table | ||||
results$interim_monitoring_guidelines | a table | ||||
results$sample_size_calculations | a table | ||||
results$futility_analysis_results | a table | ||||
results$multiplicity_adjustment | a table | ||||
results$regulatory_compliance | a table | ||||
results$boundary_plot | an image | ||||
results$operating_characteristics_plot | an image | ||||
results$conditional_power_plot | an image | ||||
results$sample_size_sensitivity_plot | an image | ||||
results$platform_trial_flowchart | an image | ||||
results$design_recommendations | a html |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$trial_design_summary$asDF
as.data.frame(results$trial_design_summary)
Examples
data('clinical_trial')
advancedtrials(
data = clinical_trial,
time_var = "survival_time",
event_var = "death",
treatment_var = "arm",
design_type = "group_sequential",
alpha_spending = "obrien_fleming",
number_of_looks = 3
)