Stata 15 help for mestreg

[ME] mestreg -- Multilevel mixed-effects parametric survival models

Syntax

mestreg fe_equation [|| re_equation] [|| re_equation ...], distribution(distname) [options]

where the syntax of fe_equation is

[indepvars] [if] [in] [weight] [, fe_options]

and the syntax of re_equation is one of the following:

for random coefficients and intercepts

levelvar: [varlist] [, re_options]

for random effects among the values of a factor variable

levelvar: R.varname

levelvar is a variable identifying the group structure for the random effects at that level or is _all representing one group comprising all observations.

fe_options Description ------------------------------------------------------------------------- Model noconstant suppress constant term from the fixed-effects equation offset(varname) include varname in model with coefficient constrained to 1 -------------------------------------------------------------------------

re_options Description ------------------------------------------------------------------------- Model covariance(vartype) variance-covariance structure of the random effects noconstant suppress constant term from the random-effects equation fweight(varname) frequency weights at higher levels iweight(varname) importance weights at higher levels pweight(varname) sampling weights at higher levels -------------------------------------------------------------------------

options Description ------------------------------------------------------------------------- Model * distribution(distname) specify survival distribution time use accelerated failure-time metric constraints(constraints) apply specified linear constraints collinear keep collinear variables

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

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 nocnsreport do not display constraints notable suppress coefficient table noheader suppress output header nogroup suppress table summarizing groups display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Integration intmethod(intmethod) integration method intpoints(#) set the number of integration (quadrature) points for all levels; default is intpoints(7)

Maximization maximize_options control the maximization process; seldom used

startvalues(svmethod) method for obtaining starting values startgrid[(gridspec)] perform a grid search to improve starting values noestimate do not fit the model; show starting values instead dnumerical use numerical derivative techniques coeflegend display legend instead of statistics ------------------------------------------------------------------------- * distribution(distname) is required.

vartype Description ------------------------------------------------------------------------- independent one unique variance parameter per random effect, all covariances 0; the default unless the R. notation is used exchangeable equal variances for random effects, and one common pairwise covariance identity equal variances for random effects, all covariances 0; the default if the R. notation is used unstructured all variances and covariances to be distinctly estimated fixed(matname) user-selected variances and covariances constrained to specified values; the remaining variances and covariances unrestricted pattern(matname) user-selected variances and covariances constrained to be equal; the remaining variances and covariances unrestricted -------------------------------------------------------------------------

distname Description ------------------------------------------------------------------------- exponential exponential survival distribution loglogistic loglogistic survival distribution llogistic synonym for loglogistic weibull Weibull survival distribution lognormal lognormal survival distribution lnormal synonym for lognormal gamma gamma survival distribution -------------------------------------------------------------------------

intmethod Description ------------------------------------------------------------------------- mvaghermite mean-variance adaptive Gauss-Hermite quadrature; the default unless a crossed random-effects model is fit mcaghermite mode-curvature adaptive Gauss-Hermite quadrature ghermite nonadaptive Gauss-Hermite quadrature laplace Laplacian approximation; the default for crossed random-effects models -------------------------------------------------------------------------

You must stset your data before using mestreg; see [ST] stset. indepvars may contain factor variables; see fvvarlist. bayes, by, and svy are allowed; see prefix. For more details, see [BAYES] bayes: mestreg. vce() and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed; see weight. Only one type of weight may be specified. Weights are not supported under the Laplacian approximation or for crossed models. startvalues(), startgrid, noestimate, dnumerical, and coeflegend do not appear in the dialog box. See [ME] mestreg postestimation for features available after estimation.

Menu

Statistics > Multilevel mixed-effects models > Parametric survival regression

Description

mestreg fits a mixed-effects parametric survival-time model. The conditional distribution of the response given the random effects is assumed to be an exponential, Weibull, lognormal, loglogistic, or gamma distribution. mestreg can be used with single- or multiple-record st data.

Options

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

noconstant suppresses the constant (intercept) term and may be specified for the fixed-effects equation and for any of or all the random-effects equations.

offset(varname) specifies that varname be included in the fixed-effects portion of the model with the coefficient constrained to be 1.

covariance(vartype) specifies the structure of the covariance matrix for the random effects and may be specified for each random-effects equation. vartype is one of the following: independent, exchangeable, identity, unstructured, fixed(matname), or pattern(matname).

covariance(independent) covariance structure allows for a distinct variance for each random effect within a random-effects equation and assumes that all covariances are 0. The default is covariance(independent) unless a crossed random-effects model is fit, in which case the default is covariance(identity).

covariance(exchangeable) structure specifies one common variance for all random effects and one common pairwise covariance.

covariance(identity) is short for "multiple of the identity"; that is, all variances are equal and all covariances are 0.

covariance(unstructured) allows for all variances and covariances to be distinct. If an equation consists of p random-effects terms, the unstructured covariance matrix will have p(p+1)/2 unique parameters.

covariance(fixed(matname)) and covariance(pattern(matname)) covariance structures provide a convenient way to impose constraints on variances and covariances of random effects. Each specification requires a matname that defines the restrictions placed on variances and covariances. Only elements in the lower triangle of matname are used, and row and column names of matname are ignored. A missing value in matname means that a given element is unrestricted. In a fixed(matname) covariance structure, (co)variance (i,j) is constrained to equal the value specified in the i,jth entry of matname. In a pattern(matname) covariance structure, (co)variances (i,j) and (k,l) are constrained to be equal if matname[i,j] = matname[k,l].

fweight(varname) specifies frequency weights at higher levels in a multilevel model, whereas frequency weights at the first level (the observation level) are specified in the usual manner, for example, [fw=fwtvar1]. varname can be any valid Stata variable name, and you can specify fweight() at levels two and higher of a multilevel model. For example, in the two-level model

. me... fixed_portion [fw = wt1] || school: ... , fweight(wt2) ...

the variable wt1 would hold the first-level (the observation-level) frequency weights, and wt2 would hold the second-level (the school-level) frequency weights.

iweight(varname) specifies importance weights at higher levels in a multilevel model, whereas importance weights at the first level (the observation level) are specified in the usual manner, for example, [iw=iwtvar1]. varname can be any valid Stata variable name, and you can specify iweight() at levels two and higher of a multilevel model. For example, in the two-level model

. me... fixed_portion [iw = wt1] || school: ... , iweight(wt2) ...

the variable wt1 would hold the first-level (the observation-level) importance weights, and wt2 would hold the second-level (the school-level) importance weights.

pweight(varname) specifies sampling weights at higher levels in a multilevel model, whereas sampling weights at the first level (the observation level) are specified in the usual manner, for example, [pw=pwtvar1]. varname can be any valid Stata variable name, and you can specify pweight() at levels two and higher of a multilevel model. For example, in the two-level model

. me... fixed_portion [pw = wt1] || school: ... , pweight(wt2) ...

variable wt1 would hold the first-level (the observation-level) sampling weights, and wt2 would hold the second-level (the school-level) sampling weights.

distribution(distname) specifies the survival model to be fit. distname is one of the following: exponential, loglogistic, llogistic, weibull, lognormal, lnormal, or gamma. This option is required.

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 in either metric; it is just a matter of changing interpretation.

time must be specified at estimation.

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), that are robust to some kinds of misspecification (robust), and that allow for intragroup correlation (cluster clustvar); see [R] vce_option. If vce(robust) is specified, robust variances are clustered at the highest level in the multilevel model.

+-----------+ ----+ 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 the exponential and Weibull models because they have a natural proportional-hazards parameterization. These two 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 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 mestreg from showing the key st variables. This option is rarely used because most users 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.

nocnsreport; see [R] estimation options.

notable suppresses the estimation table, either at estimation or upon replay.

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

nogroup suppresses the display of group summary information (number of groups, average group size, minimum, and maximum) from the output header.

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.

+-------------+ ----+ Integration +------------------------------------------------------

intmethod(intmethod) specifies the integration method to be used for the random-effects model. mvaghermite performs mean-variance adaptive Gauss-Hermite quadrature; mcaghermite performs mode-curvature adaptive Gauss-Hermite quadrature; ghermite performs nonadaptive Gauss-Hermite quadrature; and laplace performs the Laplacian approximation, equivalent to mode-curvature adaptive Gaussian quadrature with one integration point.

The default integration method is mvaghermite unless a crossed random-effects model is fit, in which case the default integration method is laplace. The Laplacian approximation has been known to produce biased parameter estimates; however, the bias tends to be more prominent in the estimates of the variance components rather than in the estimates of the fixed effects.

For crossed random-effects models, estimation with more than one quadrature point may be prohibitively intensive even for a small number of levels. For this reason, the integration method defaults to the Laplacian approximation. You may override this behavior by specifying a different integration method.

intpoints(#) sets the number of integration points for quadrature. The default is intpoints(7), which means that seven quadrature points are used for each level of random effects. This option is not allowed with intmethod(laplace).

The more integration points, the more accurate the approximation to the log likelihood. However, computation time increases as a function of the number of quadrature points raised to a power equaling the dimension of the random-effects specification. In crossed random-effects models and in models with many levels or many random coefficients, this increase can be substantial.

+--------------+ ----+ 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. Those that require special mention for mestreg are listed below.

from() accepts a properly labeled vector of initial values or a list of coefficient names with values. A list of values is not allowed.

The following options are available with mestreg but are not shown in the dialog box:

startvalues(svmethod), startgrid[(gridspec)], noestimate, and dnumerical; see [ME] meglm.

coeflegend; see [R] estimation options.

Examples

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

Two-level random-intercept Weibull survival model, analogous to xtstreg . mestreg age female || patient:, distribution(weibull)

Two-level random-intercept Weibull survival model in the accelerated failure-time metric . mestreg age female || patient:, distribution(weibull) time

Two-level random-intercept gamma survival model . mestreg age female || patient:, distribution(gamma)

--------------------------------------------------------------------------- Setup . webuse jobhistory . stset tend, origin(tstart) fail(failure)

Three-level random-intercept lognormal survival model . mestreg education njobs prestige i.female || birthyear: || id:, distribution(lognormal)

---------------------------------------------------------------------------

Stored results

mestreg stores the following in e():

Scalars e(N) number of 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_dv) number of dependent variables e(k_f) number of fixed-effects parameters e(k_r) number of random-effects parameters e(k_rs) number of variances e(k_rc) number of covariances e(df_m) model degrees of freedom e(ll) log likelihood e(chi2) chi-squared e(p) p-value for model test e(ll_c) log likelihood, comparison model e(chi2_c) chi-squared, comparison test e(df_c) degrees of freedom, comparison test e(p_c) p-value for comparison test e(N_clust) number of clusters e(rank) rank of e(V) e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) gsem e(cmd2) mestreg e(cmdline) command as typed e(depvar) name of dependent variable e(wtype) weight type e(wexp) weight expression (first-level weights) e(fweightk) fweight variable for kth highest level, if specified e(iweightk) iweight variable for kth highest level, if specified e(pweightk) pweight variable for kth highest level, if specified e(covariates) list of covariates e(ivars) grouping variables e(model) model name e(title) title in estimation output e(distribution) distribution e(clustvar) name of cluster variable e(offset) offset e(intmethod) integration method e(n_quad) number of integration points e(chi2type) Wald; type of model chi-squared e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(frm2) hazard or time 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(datasignature) the checksum e(datasignaturevars) variables used in calculation of checksum e(properties) b V e(estat_cmd) program used to implement estat e(predict) program used to implement predict e(marginsnotok) predictions disallowed by margins e(marginswtype) weight type for margins e(marginswexp) weight expression for 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(N_g) group counts e(g_min) group-size minimums e(g_avg) group-size averages e(g_max) group-size maximums e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample


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