cox-survival-analysis

Cox proportional hazards analysis on clinical time-to-event data requires a TSV input with time, event indicators, and specified covariates to compute hazard ratios. The workflow automates univariate screening, multivariate modeling, and generates publication-ready forest plots.

Checking proportional hazards assumptions with Schoenfeld residuals involves testing covariate relationships over time. This analysis computes Schoenfeld residual plots and diagnostics to validate if hazard ratios remain constant across the follow-up period.

This Cox model workflow applies to clinical datasets ranging from 50 to 10,000 samples. Datasets with at least 50 samples provide sufficient statistical power for univariate screening and multivariate modeling.

Preparing categorical covariates for multivariate Cox modeling requires one-hot encoding before analysis. You specify covariates in the input TSV, and the workflow applies encoding to categorical variables to estimate adjusted hazard ratios.

Cox survival analysis needs Kaplan-Meier curves and risk scores to visualize survival differences and stratify patient groups. These outputs, alongside hazard ratios and forest plots, provide publication-ready evidence for clinical manuscripts.

This survival analysis workflow uses pandas for TSV data handling and matplotlib for generating forest plots. It also depends on numpy and scipy to compute hazard ratios and perform biostatistical modeling.