You can fit parametric survival models in Stata using streg
. You can fit multilevel parametric survival models using mestreg
. You can now fit Bayesian parametric survival models by simply typing bayes:
in front of streg
and mestreg
!
Consider a dataset in which we model the time until hip fracture as a function of age and whether the patient wears a hip-protective device (variable protect
). Let's fit a Bayesian Weibull model to these data and compare the results with the classical analysis.
First, we declare our survival data.
Then, we fit a Weibull survival model using streg:
Finally, to fit a Bayesian survival model, we simply prefix the abovestreg
command with bayes
:
Because the default priors used are noninformative for these data, the above results are similar to those obtained from streg
. Instead of the default priors, you can specify your own; see Custom priors.
The hazard ratios are reported by default, but you can use the nohr
option with bayes
, during estimation or on replay, to report coefficients. Alternatively, you can specify this option with streg
during estimation.
Unlikestreg
, bayes: streg
reports only the log of the shape parameter. We can use the bayesstats summary
command [BAYES]bayesstats summary
) to obtain the estimates of the shape parameter and its reciprocal.
Add your own power and sample size methods