survival-models

Bayesian survival analysis with censored time-to-event data is handled using Stan and JAGS templates that accept structured inputs (N, time, event, X) to estimate hazard models and survival probabilities despite incomplete follow-up.

Bayesian survival modeling supports Exponential, Weibull, Log-Normal, and Piecewise Exponential hazard models, allowing covariate effect estimation and frailty modeling for clustered or hierarchical time-to-event data.

Stan and JAGS both support frailty modeling in survival analysis, enabling hierarchical random effects to account for unobserved heterogeneity in clustered time-to-event data alongside standard covariate hazard modeling.

Bayesian survival models are needed when incorporating prior distributions into hazard modeling, handling complex censoring patterns, or requiring generated quantities like survival probabilities and hazard ratios with full uncertainty quantification for clinical biostatistics.

Hazard ratios and survival probabilities are interpreted through generated quantities produced by the Stan and JAGS models, providing posterior distributions that quantify uncertainty around covariate effects and predicted survival curves.

Bayesian survival analysis works for both reliability engineering and clinical biostatistics, supporting covariate effects, censoring, and hazard modeling applicable to medical research and mechanical failure time-to-event data.