I’m looking to create a Bayesian proportional hazard model where the baseline hazard is modeled by a Weibull distribution (or some similar continuous distribution).
I’ve reviewed (and implemented) the cox proportional hazard example here where the baseline hazard is piece wise constant and modeled with a Poisson’s distribution Bayesian Survival Analysis — PyMC3 3.11.5 documentation
I’ve reviewed (and implemented) the accelerated failure Weibull models at
https://docs.pymc.io/en/v3/pymc-examples/examples/survival_analysis/bayes_param_survival_pymc3.html
It’s not obvious to me how to put them together. I started down the path of thinking of logs of hazard ratios, but couldn’t quite land how to model this and bring in my measured and censored survival times.
Any advice?