Stata 15 help for stintreg

[ST] stintreg -- Parametric models for interval-censored survival-time data


stintreg [indepvars] [if] [in] [weight], interval(t_l t_u) distribution(distname) [options]

options Description ------------------------------------------------------------------------- Model * interval(t_l t_u) lower and upper endpoints for the censoring interval noconstant suppress constant term * distribution(distname) specify survival 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 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 epsilon(#) tolerance to treat observations as uncensored; default is epsilon(1e-6)

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 noheader suppress header from coefficient table 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 ------------------------------------------------------------------------- * interval(t_l t_u) and distribution(distname) are required.

distname Description ------------------------------------------------------------------------- exponential exponential survival distribution gompertz Gompertz survival distribution loglogistic loglogistic survival distribution llogistic synonym for loglogistic weibull Weibull survival distribution lognormal lognormal survival distribution lnormal synonym for lognormal ggamma generalized gamma survival distribution -------------------------------------------------------------------------

varlist may contain factor variables; see fvvarlist. bootstrap, by, fp, jackknife, nestreg, statsby, stepwise, and svy are allowed; see prefix. Weights are not allowed with the bootstrap prefix. vce() and noheader are not allowed with the svy prefix. fweights, iweights, and pweights may be specified. coeflegend does not appear in the dialog box. See [ST] stintreg postestimation for features available after estimation.


Statistics > Survival analysis > Regression models > Interval-censored parametric survival models


stintreg fits parametric models to survival-time data that can be uncensored, right-censored, left-censored, or interval-censored. These models are generalizations of the models fit by streg to support interval-censored data. The supported survival models are exponential, Weibull, Gompertz, lognormal, loglogistic, and generalized gamma. Proportional-hazards (PH) and accelerated failure-time (AFT) parameterizations are provided.

With interval-censored data, the survival-time variables are specified with the stintreg command instead of using stset. Any st settings are ignored by stintreg.


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

interval(t_l t_u) specifies two time variables that contain the endpoints of the censoring interval. t_l represents the lower endpoint, and t_u represents the upper endpoint. interval() is required.

The interval time variables t_l and t_u should have the following form:

Type of data t_l t_u -------------------------------------------------- uncensored data a = [a,a] a a interval-censored data (a,b] a b left-censored data (0,b] . b left-censored data (0,b] 0 b right-censored data [a,+inf) a . missing . . missing 0 . -------------------------------------------------- noconstant; see [R] estimation options.

distribution(distname) specifies the survival model to be fit. distribution() is required.

time specifies that the model be fit in the accelerated failure-time metric rather than in the log relative-hazard metric or proportional hazards 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 the interpretation.

+---------+ ----+ 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. varname may be a factor variable; see fvvarlist.

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

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.

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.

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

epsilon(#) specifies that observations with t_u - t_l < # be treated as uncensored. The default is epsilon(1e-6).

+-----------+ ----+ 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.

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

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 stintreg but is not shown in the dialog box:

coeflegend; see [R] estimation options.


Setup . webuse aids

Fit a Weibull survival model . stintreg i.stage, interval(ltime rtime) distribution(weibull)

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

Fit a Weibull survival model in the accelerated failure-time metric . stintreg i.stage, interval(ltime rtime) distribution(weibull) time

Fit a Weibull survival model, using dose to model the ancillary parameter . stintreg i.stage, interval(ltime rtime) distribution(weibull) ancillary(i.dose)

Fit a stratified Weibull survival model . stintreg i.stage, interval(ltime rtime) distribution(weibull) strata(dose)

Stored results

stintreg stores the following in e():

Scalars e(N) number of observations e(N_unc) number of uncensored observations e(N_lc) number of left-censored observations e(N_rc) number of right-censored observations e(N_int) number of interval-censored observations 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(N_clust) number of clusters e(chi2) chi-squared e(aux_p) ancillary parameter (weibull) e(gamma) ancillary parameter (gompertz, loglogistic) e(sigma) ancillary parameter (ggamma, lnormal) e(kappa) ancillary parameter (ggamma) e(epsilon) tolerance for uncensored observations e(p) p-value for model 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) stintreg e(cmdline) command as typed e(depvar) names of time interval variables specified in interval() e(distribution) distribution e(strata) stratum variable e(title) title in estimation output e(clustvar) name of cluster variable e(wtype) weight type e(wexp) weight expression 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(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(estat_cmd) program used to implement estat e(predict) program used to implement predict e(predict_sub) predict subprogram e(marginsok) predictions allowed by margins e(marginsnotok) predictions disallowed by margins 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

© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index