Quality of Life Analysis & Patient-Centered Outcomes

Specialized Quality of Life (QoL) analysis for clinical research and patient care. Comprehensive assessment of health-related quality of life using standardized instruments (SF-36, EORTC QLQ-C30, FACT, EQ-5D) with domain-specific scoring, clinical interpretation, and longitudinal change analysis. Includes utilities calculation, preference-based measures, and health economic outcomes. Essential for clinical trials, patient-centered care, and health technology assessment.

Usage

qualityoflife(
  data,
  physical_function_items,
  role_physical_items,
  bodily_pain_items,
  general_health_items,
  vitality_items,
  social_function_items,
  role_emotional_items,
  mental_health_items,
  symptom_items,
  functional_items,
  global_qol_items,
  patient_id,
  time_var,
  group_var,
  clinical_vars,
  instrument_type = "sf36",
  scoring_algorithm = "standard",
  reference_population = "general_us",
  missing_domain_threshold = 0.5,
  imputation_method = "person_mean",
  quality_control = TRUE,
  response_pattern_analysis = TRUE,
  clinical_interpretation = TRUE,
  minimally_important_difference = TRUE,
  mid_values = "",
  ceiling_floor_analysis = TRUE,
  health_utilities = FALSE,
  utility_algorithm = "sf6d",
  longitudinal_analysis = FALSE,
  change_analysis = "simple_change",
  baseline_adjustment = TRUE,
  time_to_deterioration = FALSE,
  group_comparisons = FALSE,
  comparison_domains = "all_domains",
  statistical_test = "t_test",
  multiple_testing_correction = "fdr",
  domain_correlations = TRUE,
  clinical_correlations = FALSE,
  predictive_modeling = FALSE,
  factor_analysis = FALSE,
  clustering_analysis = FALSE,
  health_economics = FALSE,
  qaly_calculation = FALSE,
  cost_effectiveness = FALSE,
  cost_variables,
  comprehensive_report = TRUE,
  domain_profiles = FALSE,
  population_norms_comparison = TRUE,
  clinical_cutoffs = TRUE,
  save_domain_scores = FALSE,
  save_utility_scores = FALSE,
  regulatory_documentation = TRUE
)

Arguments

data: the data as a data frame
physical_function_items: Items measuring physical functioning domain
role_physical_items: Items measuring role limitations due to physical problems
bodily_pain_items: Items measuring bodily pain domain
general_health_items: Items measuring general health perceptions
vitality_items: Items measuring vitality and energy levels
social_function_items: Items measuring social functioning domain
role_emotional_items: Items measuring role limitations due to emotional problems
mental_health_items: Items measuring mental health domain
symptom_items: Items measuring disease-specific symptoms
functional_items: Additional functional assessment items
global_qol_items: Global quality of life assessment items
patient_id: Patient identifier for longitudinal analysis
time_var: Time point variable (visit, week, month)
group_var: Grouping variable for comparisons (treatment, disease stage)
clinical_vars: Clinical variables for correlation and validation
instrument_type: Type of QoL instrument being analyzed
scoring_algorithm: Scoring algorithm for QoL domains
reference_population: Reference population for norm-based scoring
missing_domain_threshold: Maximum missing proportion for domain scoring (50 percent = 0.5)
imputation_method: Method for handling missing QoL data
quality_control: Perform QoL data quality control checks
response_pattern_analysis: Analyze response patterns and validity
clinical_interpretation: Provide clinical interpretation of QoL scores
minimally_important_difference: Apply minimally important difference thresholds
mid_values: Custom MID values for domains (comma-separated, e.g., "5,10,3")
ceiling_floor_analysis: Analyze ceiling and floor effects by domain
health_utilities: Calculate health utility values for health economics
utility_algorithm: Algorithm for health utility calculation
longitudinal_analysis: Perform longitudinal QoL analysis
change_analysis: Method for analyzing QoL changes over time
baseline_adjustment: Adjust for baseline QoL differences in change analysis
time_to_deterioration: Analyze time to meaningful QoL deterioration
group_comparisons: Compare QoL between groups
comparison_domains: QoL domains to include in group comparisons
statistical_test: Statistical test for group comparisons
multiple_testing_correction: Multiple testing correction method
domain_correlations: Analyze correlations between QoL domains
clinical_correlations: Correlate QoL domains with clinical variables
predictive_modeling: Build predictive models for QoL outcomes
factor_analysis: Perform factor analysis of QoL domains
clustering_analysis: Cluster patients based on QoL profiles
health_economics: Calculate health economic outcomes
qaly_calculation: Calculate Quality-Adjusted Life Years
cost_effectiveness: Perform cost-effectiveness analysis (requires cost data)
cost_variables: Cost variables for health economic analysis
comprehensive_report: Generate comprehensive QoL analysis report
domain_profiles: Generate individual patient QoL domain profiles
population_norms_comparison: Compare scores to population normative data
clinical_cutoffs: Apply clinical cutoffs and severity classifications
save_domain_scores: Save calculated domain scores to dataset
save_utility_scores: Save calculated utility scores to dataset
regulatory_documentation: Include regulatory compliance documentation

Value

A results object containing:

`results$qol_overview`					a table
`results$domain_configuration`					a table
`results$domain_scores`					a table
`results$summary_scores`					a table
`results$ceiling_floor_effects`					a table
`results$population_norms`					a table
`results$group_comparison_domains`					a table
`results$group_descriptives`					a table
`results$longitudinal_summary`					a table
`results$change_analysis_results`					a table
`results$health_utilities_summary`					a table
`results$qaly_results`					a table
`results$domain_correlations`					a table
`results$clinical_correlations`					a table
`results$quality_control_results`					a table
`results$response_patterns`					a table
`results$mid_analysis`					a table
`results$clinical_interpretation_summary`					a table
`results$severity_classification`					a table
`results$health_economics_summary`					a table
`results$cost_effectiveness_results`					a table
`results$regulatory_summary`					a table
`results$domain_scores_plot`					an image
`results$summary_scores_plot`					an image
`results$population_comparison_plot`					an image
`results$group_comparison_plot`					an image
`results$longitudinal_trajectory_plot`					an image
`results$change_waterfall_plot`					an image
`results$domain_correlation_heatmap`					an image
`results$utilities_distribution_plot`					an image
`results$ceiling_floor_plot`					an image
`results$radar_plot`					an image
`results$missing_data_pattern_plot`					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$qol_overview$asDF

as.data.frame(results$qol_overview)

Examples

# \donttest{
data('qol_data')
#> Warning: data set ‘qol_data’ not found

qualityoflife(
    data = qol_data,
    qol_domains = list(
        physical = c("pf1", "pf2", "pf3"),
        mental = c("mh1", "mh2", "mh3")
    ),
    patient_id = "patient_id",
    instrument_type = "sf36"
)
#> Error in qualityoflife(data = qol_data, qol_domains = list(physical = c("pf1",     "pf2", "pf3"), mental = c("mh1", "mh2", "mh3")), patient_id = "patient_id",     instrument_type = "sf36"): unused argument (qol_domains = list(physical = c("pf1", "pf2", "pf3"), mental = c("mh1", "mh2", "mh3")))
# }