Stata 15 help for streg

[ST] streg -- Parametric survival models

Syntax

streg [indepvars] [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 constraints(constraints) apply specified linear constraints collinear keep collinear variables

SE/Robust vce(vcetype) vcetype may be oim, robust, cluster clustvar, opg, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) nohr do not report hazard ratios tratio report time ratios noshow do not show st setting information noheader suppress header from coefficient table nolrtest do not perform likelihood-ratio test nocnsreport do not display constraints display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics ------------------------------------------------------------------------- You must stset your data before using streg; see [ST] stset. varlist may contain factor variables; see fvvarlist. bayes, bootstrap, by, fmm, fp, jackknife, mfp, mi estimate, nestreg, statsby, stepwise, and svy are allowed; see prefix. For more details, see [BAYES] bayes: streg and [FMM] fmm: streg. vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate prefix. shared(), vce(), and noheader are not allowed with the svy prefix. fweights, iweights, and pweights may be specified using stset; see [ST] stset. However, weights may not be specified if you are using the bootstrap prefix with the streg command. coeflegend does not appear in the dialog box. See [ST] streg postestimation for features available after estimation.

Menu

Statistics > Survival analysis > Regression models > Parametric survival models

Description

streg performs maximum likelihood estimation for parametric regression survival-time models. streg can be used with single- or multiple-record or single- or multiple-failure st data. Survival models currently supported are exponential, Weibull, Gompertz, lognormal, loglogistic, and generalized gamma. Parametric frailty models and shared-frailty models are also fit using streg.

Also see [ST] stcox for proportional hazards models.

Options

+-------+ ----+ Model +------------------------------------------------------------

noconstant; see [R] estimation options.

distribution(distname) specifies the survival model to be fit. A specified distribution() is remembered from one estimation to the next when distribution() is not specified.

For instance, typing streg x1 x2, distribution(weibull) fits a Weibull model. Subsequently, you do not need to specify distribution(weibull) to fit other Weibull regression models.

All Stata estimation commands, including streg, redisplay results when you type the command name without arguments. To fit a model with no explanatory variables, type streg, distribution(distname) ....

frailty(gamma | invgaussian) specifies the assumed distribution of the frailty, or heterogeneity. The estimation results, in addition to the standard parameter estimates, will contain an estimate of the variance of the frailties and a likelihood-ratio test of the null hypothesis that this variance is zero. When this null hypothesis is true, the model reduces to the model with frailty(distname) not specified.

A specified frailty() is remembered from one estimation to the next when distribution() is not specified. When you specify distribution(), the previously remembered specification of frailty() is forgotten.

time specifies that the model be fit in the accelerated failure-time metric rather than in the log relative-hazard metric. This option is valid only for the exponential and Weibull models because these are the only models that have both a proportional hazards and an accelerated failure-time parameterization. Regardless of metric, the likelihood function is the same, and models are equally appropriate viewed in either metric; it is just a matter of changing interpretation.

time must be specified at estimation.

+---------+ ----+ Model 2 +----------------------------------------------------------

strata(varname) specifies the stratification ID variable. Observations with equal values of the variable are assumed to be in the same stratum. Stratified estimates (with equal coefficients across strata but intercepts and ancillary parameters unique to each stratum) are then obtained. This option is not available if frailty(distname) is specified.

offset(varname); see [R] estimation options.

shared(varname) is valid with frailty() and specifies a variable defining those groups over which the frailty is shared, analogous to a random-effects model for panel data where varname defines the panels. frailty() specified without shared() treats the frailties as occurring at the observation level.

A specified shared() is remembered from one estimation to the next when distribution() is not specified. When you specify distribution(), the previously remembered specification of shared() is forgotten.

shared() may not be used with distribution(ggamma), vce(robust), vce(cluster clustvar), vce(opg), the svy prefix, or in the presence of delayed entries or gaps.

If shared() is specified without frailty() and there is no remembered frailty() from the previous estimation, frailty(gamma) is assumed to provide behavior analogous to stcox; see [ST] stcox.

ancillary(varlist) specifies that the ancillary parameter for the Weibull, lognormal, Gompertz, and loglogistic distributions and that the first ancillary parameter (sigma) of the generalized log-gamma distribution be estimated as a linear combination of varlist. This option may not be used with frailty(distname).

When an ancillary parameter is constrained to be strictly positive, the logarithm of the ancillary parameter is modeled as a linear combination of varlist.

anc2(varlist) specifies that the second ancillary parameter (kappa) for the generalized log-gamma distribution be estimated as a linear combination of varlist. This option may not be used with frailty(distname).

constraints(constraints), collinear; see [R] estimation options.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim, opg), that are robust to some kinds of misspecification (robust), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

nohr, which may be specified at estimation or upon redisplaying results, specifies that coefficients rather than exponentiated coefficients be displayed, that is, that coefficients rather than hazard ratios be displayed. This option affects only how coefficients are displayed, not how they are estimated.

This option is valid only for models with a natural proportional-hazards parameterization: exponential, Weibull, and Gompertz. These three models, by default, report hazards ratios (exponentiated coefficients).

tratio specifies that exponentiated coefficients, which are interpreted as time ratios, be displayed. tratio is appropriate only for the loglogistic, lognormal, and generalized gamma models, or for the exponential and Weibull models when fit in the accelerated failure-time metric.

tratio may be specified at estimation or upon replay.

noshow prevents streg from showing the key st variables. This option is rarely used because most people type stset, show or stset, noshow to set once and for all whether they want to see these variables mentioned at the top of the output of every st command; see [ST] stset.

noheader suppresses the output header, either at estimation or upon replay.

nolrtest is valid only with frailty models, in which case it suppresses the likelihood-ratio test for significant frailty.

nocnsreport; see [R] estimation options.

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

+--------------+ ----+ Maximization +----------------------------------------------------- maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, and from(init_specs); see [R] maximize. These options are seldom used.

Setting the optimization type to technique(bhhh) resets the default vcetype to vce(opg).

The following option is available with streg but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Examples

--------------------------------------------------------------------------- Setup . webuse kva

Fit a Weibull survival model . streg load bearings, distribution(weibull)

Replay results, but display coefficients rather than hazard ratios . streg, nohr

Fit a Weibull survival model in the accelerated failure-time metric . streg load bearings, distribution(weibull) time

--------------------------------------------------------------------------- Setup . webuse mfail

Fit a Weibull survival model using data that has multiple failures per subject, and specify robust standard errors . streg x1 x2, distribution(weibull) vce(robust)

Same as above, but fit exponential model rather than Weibull . streg x1 x2, distribution(exp) vce(robust)

--------------------------------------------------------------------------- Setup . webuse cancer

Map values for drug into 0 for placebo and 1 for nonplacebo . replace drug = drug == 2 | drug == 3

Declare data to be survival-time data . stset studytime, failure(died)

Fit a generalized gamma survival model . streg drug age, distribution(ggamma)

Test for appropriateness of Weibull model . test [/kappa] = 1

--------------------------------------------------------------------------- Setup . webuse hip3, clear

Fit a Weibull survival model, using male to model the ancillary parameter . streg protect age, dist(weibull) ancillary(male)

--------------------------------------------------------------------------- Setup . webuse cancer

Declare data to be survival-time data . stset studytime died

Fit a stratified Weibull survival model . streg age, dist(weibull) strata(drug)

Produce a "less-stratified" model than above . streg age, dist(weibull) ancillary(i.drug)

--------------------------------------------------------------------------- Setup . webuse bc

List some of the data . list in 1/12

Declare data to be survival-time data . stset t, fail(dead)

Fit Weibull survival model with gamma-distributed frailty . streg age smoking, dist(weibull) frailty(gamma)

Fit Weibull survival model with inverse-Gaussian-distributed frailty . streg age smoking, dist(weibull) frailty(invgauss)

--------------------------------------------------------------------------- Setup . webuse catheter

List some of the data . list in 1/10

Declare data to be survival-time data . stset time, fail(infect)

Fit Weibull survival model with inverse-Gaussian-distributed shared frailty . streg age female, dist(weibull) frailty(invgauss) shared(patient)

Same as above, but fit lognormal model rather than Weibull . streg age female, dist(lnormal) frailty(invgauss) shared(patient)

--------------------------------------------------------------------------- Setup . webuse nhefs

Declare survey design for data . svyset psu2 [pw=swgt2], strata(strata2)

Declare data to be survival-time data . stset age_lung_cancer if age_lung_cancer < . [pw=swgt2], fail(lung_cancer)

Fit exponential survival model taking into account data are survey data . svy: streg former_smoker smoker male urban1 rural, dist(exp) ---------------------------------------------------------------------------

Stored results

streg stores the following in e():

Scalars e(N) number of observations e(N_sub) number of subjects e(N_fail) number of failures e(N_g) number of groups e(k) number of parameters e(k_eq) number of equations in e(b) e(k_eq_model) number of equations in overall model test e(k_aux) number of auxiliary parameters e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(ll) log likelihood e(ll_0) log likelihood, constant-only model e(ll_c) log likelihood, comparison model e(N_clust) number of clusters e(chi2) chi-squared e(chi2_c) chi-squared, comparison model e(risk) total time at risk e(g_min) smallest group size e(g_avg) average group size e(g_max) largest group size e(theta) frailty parameter e(aux_p) ancillary parameter (weibull) e(gamma) ancillary parameter (gompertz, loglogistic) e(sigma) ancillary parameter (ggamma, lnormal) e(kappa) ancillary parameter (ggamma) e(p) p-value for model test e(p_c) p-value for comparison test e(rank) rank of e(V) e(rank0) rank of e(V), constant-only model e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) model or regression name e(cmd2) streg e(cmdline) command as typed e(dead) _d e(depvar) _t e(strata) stratum variable e(title) title in estimation output e(clustvar) name of cluster variable e(shared) frailty grouping variable e(fr_title) title in output identifying frailty e(wtype) weight type e(wexp) weight expression e(t0) _t0 e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(frm2) hazard or time e(chi2type) Wald or LR; type of model chi-squared test e(offset1) offset for main equation e(stcurve) stcurve e(opt) type of optimization e(which) max or min; whether optimizer is to perform maximization or minimization e(ml_method) type of ml method e(user) name of likelihood-evaluator program e(technique) maximization technique e(properties) b V e(predict) program used to implement predict e(predict_sub) predict subprogram e(footnote) program used to implement the footnote display e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(Cns) constraints matrix e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample


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