Stata 15 help for streg_postestimation

[ST] streg postestimation -- Postestimation tools for streg

Postestimation commands

The following postestimation command is of special interest after streg:

Command Description ------------------------------------------------------------------------- stcurve plot the survivor, hazard, and cumulative hazard 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) estat (svy) postestimation statistics for survey data estimates cataloging estimation results * hausman Hausman's specification test lincom point estimates, standard errors, testing, and inference for linear combinations of coefficients linktest link test for model specification * lrtest likelihood-ratio test 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 suest seemingly unrelated estimation test Wald tests of simple and composite linear hypotheses testnl Wald tests of nonlinear hypotheses ------------------------------------------------------------------------- * hausman and lrtest are not appropriate with svy estimation results.

Syntax for predict

predict [type] newvar [if] [in] [, statistic options]

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

statistic Description ------------------------------------------------------------------------- Main median time median survival time; the default median lntime median ln(survival time) mean time mean survival time mean lntime mean ln(survival time) hazard hazard hr hazard ratio, also known as the relative hazard xb linear prediction xb stdp standard error of the linear prediction; SE(xb) surv S(t|t_0) * csurv S(t|earliest t_0 for subject) * csnell Cox-Snell residuals * mgale martingale-like residuals * deviance deviance residuals -------------------------------------------------------------------------

options Description ------------------------------------------------------------------------- oos make statistic available in and out of sample nooffset ignore the offset() variable specified in streg alpha1 predict statistic conditional on frailty value equal to one unconditional predict statistic unconditionally on the frailty marginal synonym for unconditional partial produce observation-level results -------------------------------------------------------------------------

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 for the estimation sample by default, but the oos option makes them available both in and out of sample. When no option is specified, the predicted median survival time is calculated for all models. The predicted hazard ratio, option hr, is available only for the exponential, Weibull, and Gompertz models. The mean time and mean lntime options are not available for the Gompertz model. Unconditional estimates of mean time and mean lntime are not available if frailty() was specified with streg. csnell, mgale, and deviance are not allowed with svy estimation results.

Menu for predict

Statistics > Postestimation

Description for predict

predict creates a new variable containing predictions such as median and mean survival times; hazards; hazard ratios; linear predictions; standard errors; probabilities; Cox-Snell, martingale-like, and deviance residuals.

Options for predict

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

median time calculates the predicted median survival time in analysis-time units. This is the prediction from time 0 conditional on constant covariates. When no options are specified with predict, the predicted median survival time is calculated for all models.

median lntime calculates the natural logarithm of what median time produces.

mean time calculates the predicted mean survival time in analysis-time units. This is the prediction from time 0 conditional on constant covariates. This option is not available for Gompertz regressions and is available for frailty models only if alpha1 is specified, in which case what you obtain is an estimate of the mean survival time conditional on a frailty effect of one.

mean lntime predicts the mean of the natural logarithm of time. This option is not available for Gompertz regression and is available for frailty models only if alpha1 is specified, in which case what you obtain is an estimate of the mean log survival-time conditional on a frailty effect of one.

hazard calculates the predicted hazard.

hr calculates the hazard ratio. This option is valid only for models having a proportional-hazards parameterization.

xb calculates the linear prediction from the fitted model. That is, you fit the model by estimating a set of parameters b0, b1, b2, ..., bk, and the linear prediction is y = 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 y.

surv calculates each observation's predicted survivor probability, S(t|t0), where t_0 is _t0, the analysis time at which each record became at risk. For multiple-record data, see the csurv option below.

csurv calculates the predicted S(t|earliest t0) for each subject in multiple-record data by calculating the conditional survivor values, S(t|t0) (see the surv option above), and then multiplying them.

What you obtain from surv will differ from what you obtain from csurv only if you have multiple records for that subject.

In the presence of gaps or delayed entry, the estimates obtained from csurv can be different for subjects with gaps from those without gaps, having the same covariate values, because the probability of survival over gaps is assumed to be 1. Thus the predicted cumulative conditional survivor function is not a smooth function of time _t for constant values of the covariates. Use stcurve, survival to obtain a smooth estimate of the cumulative survivor function S(t|x).

csnell calculates the Cox-Snell generalized residuals. For multiple-record-per-subject 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 Cox-Snell residuals, one for each record within subject; see partial below. Partial Cox-Snell residuals are the additive contributions to a subject's overall Cox-Snell residual. In single-record-per-subject data, the partial Cox-Snell residuals are the Cox-Snell residuals.

mgale calculates the martingale-like residuals. 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 martingale residuals, one for each record within subject; see partial below. Partial martingale residuals are the additive contributions to a subject's overall martingale residual. In single-record data, the partial martingale residuals are the martingale residuals.

deviance calculates the deviance residuals. Deviance residuals are martingale residuals that have been transformed to be more symmetric about zero. 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 deviance residuals, one for each record within subject; see partial below. Partial deviance residuals are transformed partial martingale residuals. In single-record data, the partial deviance residuals are the deviance residuals.

oos makes csurv, csnell, mgale, and deviance available both in and out of sample. oos also dictates that summations and other accumulations take place over the sample as defined by if and in. By default, the summations are taken over the estimation sample, with if and in merely determining which values of newvar are to be filled in once the calculation is finished.

nooffset is relevant only if you specified offset(varname) with streg. 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.

alpha1, when used after fitting a frailty model, specifies that statistic be predicted conditional on a frailty value equal to one. This is the default for shared-frailty models.

unconditional and marginal, when used after fitting a frailty model, specify that statistic be predicted unconditional on the frailty. That is, the prediction is averaged over the frailty distribution. This is the default for unshared-frailty models.

partial is relevant only for multiple-record data and is valid with csnell, mgale, and deviance. 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 of stset.

Specify partial if you wish to perform diagnostics on individual records rather than on individual subjects. For example, a partial deviance can be used to diagnose the fitted characteristics of an individual record rather than those of the set of records for a given subject.

scores calculates equation-level score variables. The number of score variables created depends upon the chosen distribution.

The first new variable will always contain the partial derivative of the log likelihood with respect to the linear prediction (regression equation) from the fitted model.

The subsequent new variables will contain the partial derivative of the log likelihood with respect to the ancillary parameters. For example, in the generalized gamma model, the second new variable will contain the partial derivative of the log likelihood with respect to the linear prediction (sigma equation), and the third new variable will contain the partial derivative of the log likelihood with respect to the linear prediction (kappa equation).

Syntax for margins

margins [marginlist] [, options]

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

statistic Description ------------------------------------------------------------------------- median time median survival time; the default median lntime median ln(survival time) mean time mean survival time mean lntime mean ln(survival time) hr hazard ratio, also known as the relative hazard xb linear prediction xb hazard not allowed with margins stdp not allowed with margins surv not allowed with margins csurv not allowed with margins csnell not allowed with margins mgale not allowed with margins deviance 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 median and mean survival times, hazard ratios, and linear predictions.

Examples

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

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

Predict median survival time . predict time, time

Predict log-median survival time . predict lntime, lntime

--------------------------------------------------------------------------- Setup . webuse cancer, clear

Map value 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 exponential survival model . streg age drug, d(exp)

Predict Cox-Snell residuals . predict double cs, csnell

stset the data again with cs as failure-time variable . stset cs, failure(died)

Create km containing Kaplan-Meier survival estimates . sts generate km = s

Create H, the cumulative hazard . generate double H = -ln(km)

Plot H against cs, specifying cs twice to obtain reference line . line H cs cs, sort

Fit lognormal survival model . streg age drug, d(lnormal)

Predict deviance residuals . predict dev, deviance

Plot deviance residuals . scatter dev studytime, yline(0) m(o) ---------------------------------------------------------------------------


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