biostatistics

name biostatistics description Performs biostatistical analyses specialized for clinical and biomedical research including survival analysis, Kaplan-Meier estimation, Cox proportional hazards regression, longitudinal data modeling, and diagnostic test evaluation; trigger when users discuss clinical outcomes, survival curves, or biomedical study statistics. 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 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. 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). 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. 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. 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. 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. 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