biostatistics

## When to Trigger Activate this skill when the user mentions: - Survival analysis, time-to-event, censoring - Kaplan-Meier curves, log-rank test, median survival - Cox regression, proportional hazards, hazard ratio - Longitudinal data, mixed-effects models, GEE - Diagnostic accuracy, sensitivity, specificity, ROC/AUC - Competing risks, Fine-Gray model, cumulative incidence - Sample size for clinical endpoints, multiplicity adjustment - Missing data in clinical studies, multiple imputation, MCAR/MAR/MNAR ## Step-by-Step Methodology 1. **Study design assessment** - Confirm study type (cohort, case-control, cross-sectional, RCT). Identify primary endpoint type (continuous, binary, time-to-event, count, ordinal). Determine if data is clustered or longitudinal. 2. **Survival analysis** - Define time origin, event definition, and censoring mechanism. Verify censoring is non-informative. Estimate survival curves with Kaplan-Meier method. Compare groups with log-rank test (or weighted variants: Wilcoxon, Tarone-Ware for non-proportional hazards). 3. **Cox regression** - Check proportional hazards assumption (Schoenfeld residuals, log-log plots). If violated, use time-varying coefficients, stratified Cox, or restricted mean survival time (RMST). Report hazard ratios with 95% CIs. Handle multiple covariates with purposeful selection or penalized regression. 4. **Competing risks** - When multiple event types exist, use cumulative incidence functions (not 1-KM). Apply Fine-Gray subdistribution hazard model or cause-specific hazard models. Report cumulative incidence at clinically relevant timepoints. 5. **Longitudinal analysis** - For repeated measures: linear or generalized mixed-effects models (random intercepts/slopes). Choose appropriate correlation structure. Handle dropout with pattern mixture models or joint models for longitudinal and survival data. 6. **Diagnostic test evaluation** - Compute sensitivity, specificity, PPV, NPV at defined cutoffs. Generate ROC curve and compute AUC with DeLong confidence intervals. For biomarker discovery, apply cross-validation to avoid overoptimism. 7. **Missing data handling** - Classify missingness mechanism (MCAR, MAR, MNAR). For MAR: multiple imputation (m >= 20 imputations, Rubin's rules for pooling). Conduct sensitivity analysis under MNAR assumptions. ## Key Databases and Tools - **R survival / survminer** - Survival analysis packages - **SAS PROC PHREG / LIFETEST** - Clinical biostatistics standard - **STATA stcox / stcurve** - Survival modeling - **R mice / Amelia** - Multiple imputation - **pROC / cutpointr** - ROC analysis ## Output Format - Kaplan-Meier curves with number-at-risk table, median survival with 95% CI. - Cox model results as a table: variable, HR, 95% CI, p-value, with PH assumption test. - Cumulative incidence curves for competing risks with event-specific estimates. - ROC curves with AUC, optimal cutpoint, and sensitivity/specificity at that point. - Missing data report: pattern, mechanism assessment, imputation method, sensitivity results. ## Quality Checklist - [ ] Time origin and event definition clearly specified - [ ] Censoring mechanism described and non-informative assumption justified - [ ] Proportional hazards assumption tested and result reported - [ ] Competing risks handled appropriately (not ignored) - [ ] Multiple comparisons adjustment applied when needed - [ ] Missing data mechanism assessed and appropriate method used - [ ] Sample size adequate for number of covariates (EPV >= 10 for Cox) - [ ] Effect estimates reported with confidence intervals, not just p-values - [ ] Sensitivity analyses performed for key assumptions