Stata 15 help for stcrreg_postestimation

[ST] stcrreg postestimation -- Postestimation tools for stcrreg

Postestimation commands

The following postestimation command is of special interest after stcrreg:

Command Description ------------------------------------------------------------------------- stcurve plot the cumulative subhazard and cumulative incidence functions -------------------------------------------------------------------------

The following standard postestimation commands are also available:

Command Description ------------------------------------------------------------------------- contrast contrasts and ANOVA-style joint tests of estimates estat ic Akaike's and Schwarz's Bayesian information criteria (AIC and BIC) estat summarize summary statistics for the estimation sample estat vce variance-covariance matrix of the estimators (VCE) estimates cataloging estimation results hausman Hausman's specification test lincom point estimates, standard errors, testing, and inference for linear combinations of coefficients margins marginal means, predictive margins, marginal effects, and average marginal effects marginsplot graph the results from margins (profile plots, interaction plots, etc.) nlcom point estimates, standard errors, testing, and inference for nonlinear combinations of coefficients predict predictions, residuals, influence statistics, and other diagnostic measures predictnl point estimates, standard errors, testing, and inference for generalized predictions pwcompare pairwise comparisons of estimates test Wald tests of simple and composite linear hypotheses testnl Wald tests of nonlinear hypotheses -------------------------------------------------------------------------

Syntax for predict

predict [type] newvar [if] [in] [, sv_statistic nooffset]

predict [type] {stub*|newvarlist} [if] [in] , mv_statistic [partial]

sv_statistic Description ------------------------------------------------------------------------- Main shr predicted subhazard ratio, also known as the relative subhazard; the default xb linear prediction xb stdp standard error of the linear prediction; SE(xb) * basecif baseline cumulative incidence function (CIF) * basecshazard baseline cumulative subhazard function * kmcensor Kaplan-Meier survivor curve for censoring distribution -------------------------------------------------------------------------

mv_statistic Description ------------------------------------------------------------------------- Main * scores pseudolikelihood scores * esr efficient score residuals * dfbeta DFBETA measures of influence * schoenfeld Schoenfeld residuals ------------------------------------------------------------------------- Unstarred statistics are available both in and out of sample; type predict ... if e(sample) ... if wanted only for the estimation sample. Starred statistics are calculated only for the estimation sample, even when if e(sample) is not specified. nooffset is allowed only with unstarred statistics.

Menu for predict

Statistics > Postestimation

Description for predict

predict creates a new variable containing predictions such as subhazard ratios, linear predictions, standard errors, baseline cumulative incidence and subhazard functions, Kaplan-Meier survivor curves, pseudolikelihood scores, efficient score and Schoenfeld residuals, and DFBETA measures of influence.

Options for predict

+------+ ----+ Main +-------------------------------------------------------------

shr, the default, calculates the relative subhazard (subhazard ratio), that is, the exponentiated linear prediction.

xb calculates the linear prediction from the fitted model. That is, you fit the model by estimating a set of parameters, b1, b2, ..., bk, and the linear prediction is xb.

The x used in the calculation is obtained from the data currently in memory and need not correspond to the data on the independent variables used in estimating b.

stdp calculates the standard error of the prediction, that is, the standard error of xb.

basecif calculates the baseline CIF. This is the CIF of the subdistribution for the cause-specific failure process.

basecshazard calculates the baseline cumulative subhazard function. This is the cumulative hazard function of the subdistribution for the cause-specific failure process.

kmcensor calculates the Kaplan-Meier survivor function for the censoring distribution. These estimates are used to weight within risk pools observations that have experienced a competing event. As such, these values are not predictions or diagnostics in the strict sense, but are provided for those who wish to reproduce the pseudolikelihood calculations performed by stcrreg.

nooffset is allowed only with shr, xb, and stdp, and is relevant only if you specified offset(varname) for stcrreg. It modifies the calculations made by predict so that they ignore the offset variable; the linear prediction is treated as xb rather than xb + offset.

scores calculates the pseudolikelihood scores for each regressor in the model. These scores are components of the robust estimate of variance. For multiple-record data, by default only one score per subject is calculated and it is placed on the last record for the subject.

Adding the partial option will produce partial scores, one for each record within subject; see partial below. Partial pseudolikelihood scores are the additive contributions to a subject's overall pseudolikelihood score. In single-record data, the partial pseudolikelihood scores are the pseudolikelihood scores.

One score variable is created for each regressor in the model; the first new variable corresponds to the first regressor, the second to the second, and so on.

esr calculates the efficient score residuals for each regressor in the model. Efficient score residuals are diagnostic measures equivalent to pseudolikelihood scores, with the exception that efficient score residuals treat the censoring distribution (that used for weighting) as known rather than estimated. For multiple-record data, by default only one efficient score per subject is calculated and it is placed on the last record for the subject.

Adding the partial option will produce partial efficient score residuals, one for each record within subject; see partial below. Partial efficient score residuals are the additive contributions to a subject's overall efficient score residual. In single-record data, the partial efficient scores are the efficient scores.

One efficient score variable is created for each regressor in the model; the first new variable corresponds to the first regressor, the second to the second, and so on.

dfbeta calculates the DFBETA measures of influence for each regressor in the model. The DFBETA value for a subject estimates the change in the regressor's coefficient due to deletion of that subject. For multiple-record data, by default only one value per subject is calculated and it is placed on the last record for the subject.

Adding the partial option will produce partial DFBETAs, one for each record within subject; see partial below. Partial DFBETAs are interpreted as effects due to deletion of individual records rather than deletion of individual subjects. In single-record data, the partial DFBETAs are the DFBETAs.

One DFBETA variable is created for each regressor in the model; the first new variable corresponds to the first regressor, the second to the second, and so on.

schoenfeld calculates the Schoenfeld-like residuals. Schoenfeld-like residuals are diagnostic measures analogous to Schoenfeld residuals in Cox regression. They compare a failed observation's covariate values to the (weighted) average covariate values for all those at risk at the time of failure. Schoenfeld-like residuals are calculated only for those observations that end in failure; missing values are produced otherwise.

One Schoenfeld residual variable is created for each regressor in the model; the first new variable corresponds to the first regressor, the second to the second, and so on.

Note: The easiest way to use the preceding four options is, for example,

. predict double stub*, scores

where stub is a short name of your choosing. Stata then creates variables stub1, stub2, etc. You may also specify each variable name explicitly, in which case there must be as many (and no more) variables specified as there are regressors in the model.

partial is relevant only for multiple-record data and is valid with scores, esr, and dfbeta. Specifying partial will produce "partial" versions of these statistics, where one value is calculated for each record instead of one for each subject. The subjects are determined by the id() option to stset.

Specify partial if you wish to perform diagnostics on individual records rather than on individual subjects. For example, a partial DFBETA would be interpreted as the effect on a coefficient due to deletion of one record, rather than the effect due to deletion of all records for a given subject.

Syntax for margins

margins [marginlist] [, options]

margins [marginlist] , predict(statistic ...) [predict(statistic ...) ...] [options]

statistic Description ------------------------------------------------------------------------- shr predicted subhazard ratio, also known as the relative subhazard; the default xb linear prediction xb stdp not allowed with margins basecif not allowed with margins basecshazard not allowed with margins kmcensor not allowed with margins scores not allowed with margins esr not allowed with margins dfbeta not allowed with margins schoenfeld not allowed with margins -------------------------------------------------------------------------

Statistics not allowed with margins are functions of stochastic quantities other than e(b).

For the full syntax, see [R] margins.

Menu for margins

Statistics > Postestimation

Description for margins

margins estimates margins of response for subhazard ratios and linear predictions.

Examples of predictions after stcrreg

Setup . webuse hypoxia

Declare data to be survival-time data . stset dftime, failure(failtype==1)

Fit competing-risks model . stcrreg ifp tumsize pelnode, compete(failtype==2)

Obtain the relative subhazards . predict double shr

Obtain the baseline cumulative subhazard function . predict double bcsh, basecsh

Obtain DFBETA measures of influence . predict double df*, dfbeta

Plot the first DFBETA versus analysis time . twoway scatter df1 _t

Example of using stcurve after stcrreg

Setup . webuse hypoxia, clear

Declare data to be survival-time data . stset dftime, failure(failtype==1)

Fit competing-risks model . stcrreg ifp tumsize pelnode, compete(failtype==2)

Use stcurve to compare CIFs . stcurve, cif at1(pelnode=0) at2(pelnode=1)


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