biostatistics
This skill handles survival analysis and time-to-event data common in clinical research, including Kaplan-Meier estimation, Cox proportional hazards regression, and competing risks modeling. It also covers longitudinal data analysis, diagnostic accuracy assessment, and missing data strategies tailored to biomedical studies.
Biostatistics performs Kaplan-Meier survival analysis and Cox regression for clinical research outcomes.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-06-08
Biostatistics performs Kaplan-Meier survival analysis and Cox regression for clinical research outcomes. This skill handles survival analysis and time-to-event data common in clinical research, including Kaplan-Meier estimation, Cox proportional hazards regression, and competing risks modeling. It also covers longitudinal data analysis, diagnostic accuracy assessment, and missing data strategies tailored to biomedical studies.
Use it when
- biostatistics provides tools for Kaplan-Meier estimation to visualize survival curves accounting for censoring.
- Yes, biostatistics assesses diagnostic test accuracy through sensitivity, specificity, and ROC/AUC analysis for biomarker evaluation.
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What is biostatistics used for in clinical trials?
biostatistics handles survival analysis for clinical trials, including time-to-event data analysis with methods like Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression. It enables researchers to estimate survival probabilities, compare treatment groups, and quantify hazard ratios with confidence intervals for regulatory submissions and evidence synthesis.
How does biostatistics handle Kaplan-Meier curves and Cox regression?
biostatistics provides tools for Kaplan-Meier estimation to visualize survival curves accounting for censoring, log-rank tests to compare groups, and Cox proportional hazards regression to model covariate effects on time-to-event outcomes. It also tests the proportional hazards assumption and computes adjusted hazard ratios for multivariable inference.
Can biostatistics evaluate diagnostic test accuracy and ROC performance?
Yes, biostatistics assesses diagnostic test accuracy through sensitivity, specificity, and ROC/AUC analysis for biomarker evaluation. It quantifies discriminative ability, compares competing diagnostic strategies, and supports threshold optimization for clinical decision-making in biomedical research.
How does biostatistics model competing risks and cumulative incidence?
biostatistics handles competing risks analysis using Fine-Gray models and cumulative incidence functions to estimate the probability of specific events when multiple outcomes can occur. This is essential when subjects may experience alternative endpoints (e.g., death from different causes) that prevent observation of the primary event of interest.
What missing data methods does biostatistics support?
biostatistics performs multiple imputation for missing data under MCAR, MAR, and MNAR assumptions in clinical studies. It enables valid inference when data are incomplete, preserves uncertainty through imputation variability, and supports sensitivity analyses to assess robustness to missing data mechanisms.
Does biostatistics analyze longitudinal and repeated measures data?
Yes, biostatistics analyzes longitudinal and repeated measures data using mixed effects models and generalized estimating equations (GEE). These methods account for within-subject correlation, handle unbalanced designs, and provide population-averaged or subject-specific inference for biomedical studies tracking outcomes over time.
SKILL.md
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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
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skills/biostatistics/SKILL.md
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