Stata 15 help for bayes_streg

[BAYES] bayes: streg -- Bayesian parametric survival models

Syntax

bayes [, bayesopts] : streg [varlist] [if] [in] [, options]

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term distribution(exponential) exponential survival distribution distribution(gompertz) Gompertz survival distribution distribution(loglogistic) loglogistic survival distribution distribution(llogistic) synonym for distribution(loglogistic) distribution(weibull) Weibull survival distribution distribution(lognormal) lognormal survival distribution distribution(lnormal) synonym for distribution(lognormal) distribution(ggamma) generalized gamma survival distribution frailty(gamma) gamma frailty distribution frailty(invgaussian) inverse-Gaussian distribution time use accelerated failure-time metric

Model 2 strata(varname) strata ID variable offset(varname) include varname in model with coefficient constrained to 1 shared(varname) shared frailty ID variable ancillary(varlist) use varlist to model the first ancillary parameter anc2(varlist) use varlist to model the second ancillary parameter collinear keep collinear variables

Reporting nohr do not report hazard ratios tratio report time ratios noshow do not show st setting information display_options control spacing, line width, and base and empty cells

level(#) set credible level; default is level(95) ------------------------------------------------------------------------- You must stset your data before using bayes: streg; see [ST] stset. varlist may contain factor variables; see fvvarlist. bayes: streg, level() is equivalent to bayes, clevel(): streg. For a detailed description of options, see Options in [ST] streg.

bayesopts Description ------------------------------------------------------------------------- Priors * normalprior(#) specify standard deviation of default normal priors for regression coefficients and log-ancillary parameters; default is normalprior(100)

prior(priorspec) prior for model parameters; this option may be repeated dryrun show model summary without estimation

Simulation mcmcsize(#) MCMC sample size; default is mcmcsize(10000) burnin(#) burn-in period; default is burnin(2500) thinning(#) thinning interval; default is thinning(1) rseed(#) random-number seed exclude(paramref) specify model parameters to be excluded from the simulation results

Blocking * blocksize(#) maximum block size; default is blocksize(50) block(paramref[, blockopts]) specify a block of model parameters; this option may be repeated blocksummary display block summary * noblocking do not block parameters by default

Initialization initial(initspec) initial values for model parameters nomleinitial suppress the use of maximum likelihood estimates as starting values initrandom specify random initial values initsummary display initial values used for simulation * noisily display output from the estimation command during initialization

Adaptation adaptation(adaptopts) control the adaptive MCMC procedure scale(#) initial multiplier for scale factor; default is scale(2.38) covariance(cov) initial proposal covariance; default is the identity matrix

Reporting clevel(#) set credible interval level; default is clevel(95) hpd display HPD credible intervals instead of the default equal-tailed credible intervals * nohr do not report hazard ratios * tratio report time ratios; requires option time with streg eform[(string)] report exponentiated coefficients and, optionally, label as string batch(#) specify length of block for batch-means calculations; default is batch(0) saving(filename[, replace]) save simulation results to filename.dta nomodelsummary suppress model summary dots display dots every 100 iterations and iteration numbers every 1,000 iterations dots(#[, every(#)]) display dots as simulation is performed [no]show(paramref) specify model parameters to be excluded from or included in the output notable suppress estimation table noheader suppress output header title(string) display string as title above the table of parameter estimates display_options control spacing, line width, and base and empty cells

Advanced search(search_options) control the search for feasible initial values corrlag(#) specify maximum autocorrelation lag; default varies corrtol(#) specify autocorrelation tolerance; default is corrtol(0.01) ------------------------------------------------------------------------- * Starred options are specific to the bayes prefix; other options are common between bayes and bayesmh. Options prior() and block() can be repeated. priorspec and paramref are defined in [BAYES] bayesmh. paramref may contain factor variables; see fvvarlist. See [BAYES] bayesian postestimation for features available after estimation. Model parameters are regression coefficients {depvar:indepvars} and ancillary parameters as described in Ancillary model parameters in [BAYES] bayes: streg. Use the dryrun option to see the definitions of model parameters prior to estimation. For a detailed description of bayesopts, see Options in [BAYES] bayes.

Menu

Statistics > Survival analysis > Regression models > Bayesian parametric survival models

Description

bayes: streg fits a Bayesian parametric survival model to a survival-time outcome; see [BAYES] bayes and [ST] streg for details.

Examples

Setup . webuse hip3 . stset

Fit Bayesian Weibull survival model . bayes: streg protect age, distribution(weibull)

Display coefficients instead of hazard ratios . bayes, nohr

Display estimates of the shape parameter and its reciprocal . bayesstats summary (p: exp({ln_p})) (sigma: 1/exp({ln_p}))

Fit Bayesian Weibull regression using uniform priors for all regression coefficients . bayes, prior({_t:protect age _cons}, uniform(-20,20)): streg protect age, distribution(weibull)

Same as above, but use a shortcut notation to refer to all regression coefficients . bayes, prior({_t:}, uniform(-20,20)): streg protect age, distribution(weibull)

Use a different uniform prior for each regression coefficient . bayes, prior({_t:protect}, uniform(-5,5)) prior({_t:age}, uniform(0,1)) prior({_t:_cons}, uniform(-20,20)): streg protect age, distribution(weibull)

Fit Bayesian Weibull survival model with ancillary variable male; use standard deviation of 10 of the default normal prior for regression coefficients and display dots as iterations are performed . bayes, normalprior(10) dots: streg protect age, distribution(weibull) ancillary(male)

Same as above, but also increase the burn-in period to 3,000 from the default of 2,500 and specify random-number seed for reproducibility . bayes, normalprior(10) dots burnin(3000) rseed(12345): streg protect age, distribution(weibull) ancillary(male)

Examine the diagnostic plot for {ln_p:male} . bayesgraph diagnostic {ln_p:male}

Save MCMC results on replay . bayes, saving(mymcmc)

Stored results

See Stored results in [BAYES] bayesmh.


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