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!

Let’s see it work

Let’s look at several examples.

Parametric survival models

Multilevel survival models

Parametric survival models

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.

```. stset time1, id(id) failure(fracture) time0(time0)
```

Then, we fit a Weibull survival model using streg.

```. streg protect age, distribution(weibull)

failure _d:  fracture
analysis time _t:  time1
id:  id

Weibull PH regression

No. of subjects =          148                  Number of obs    =         206
No. of failures =           37
Time at risk    =         1703
LR chi2(2)       =       49.97
Log likelihood  =   -77.446477                  Prob > chi2      =      0.0000

```
 _t Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] protect .0922046 .0321722 -6.83 0.000 .0465318 .1827072 age 1.101041 .038173 2.78 0.005 1.028709 1.178459 _cons .000024 .0000624 -4.09 0.000 1.48e-07 .0039042 /ln_p .4513032 .1265975 3.56 0.000 .2031767 .6994297 p 1.570357 .1988033 1.225289 2.012605 1/p .6367977 .080617 .4968686 .816134
```Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.

```

Finally, to fit a Bayesian survival model, we simply prefix the above streg command with bayes:.

```. bayes: streg protect age, distribution(weibull)

```
 Model summary Likelihood: _t ~ streg_weibull(xb__t,{ln_p}) Priors: {_t:protect age _cons} ~ normal(0,10000) (1) {ln_p} ~ normal(0,10000) (1) Parameters are elements of the linear form xb__t.
```Bayesian Weibull PH regression                   MCMC iterations  =     12,500
Random-walk Metropolis-Hastings sampling         Burn-in          =      2,500
MCMC sample size =     10,000
No. of subjects =        148                     Number of obs    =        206
No. of failures =         37
No. at risk     =       1703
Acceptance rate  =       .368
Efficiency:  min =     .05571
avg =     .09994
Log marginal likelihood = -107.88854                          max =      .1767

```
 Equal-tailed Haz. Ratio Std. Dev. MCSE Median [95% Cred. Interval] _t protect .0956023 .0338626 .001435 .0899154 .0463754 .1787249 age 1.103866 .0379671 .001313 1.102685 1.033111 1.180283 _cons .0075815 .0411427 .000979 .000567 4.02e-06 .0560771 ln_p .4473869 .1285796 .004443 .4493192 .1866153 .6912467
```Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline hazard.
Note: Default priors are used for model parameters.```

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.

```. bayes, nohr

```
 Equal-tailed Mean Std. Dev. MCSE Median [95% Cred. Interval] _t protect -2.407909 .3482806 .015077 -2.408886 -3.070986 -1.721908 age .0982285 .0343418 .001189 .0977484 .0325748 .165754 _cons -7.561389 2.474563 .084712 -7.475201 -12.42343 -2.881028 ln_p .4473869 .1285796 .004443 .4493192 .1866153 .6912467

Unlike streg, 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.

```. bayesstats summary (p: exp({ln_p})) (sigma: 1/exp({ln_p}))

Posterior summary statistics                      MCMC sample size =    10,000

p : exp({ln_p})
sigma : 1/exp({ln_p})

```
 Equal-tailed Mean Std. Dev. MCSE Median [95% Cred. Interval] p 1.577122 .201685 .006993 1.567245 1.205164 1.996203 sigma .6446338 .0839366 .002879 .6380624 .5009511 .8297629

Tell me more

Learn more about the general features of the bayes prefix.