help stcox postestimation dialogs: predict estat stcurve
also see: stcox
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Title
[ST] stcox postestimation -- Postestimation tools for stcox
Description
The following postestimation commands are of special interest after
stcox:
command description
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estat concordance Harrell's C
stcurve plot the survivor, hazard, and cumulative hazard
functions
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estat concordance is not appropriate after estimation with svy.
The following standard postestimation commands are also available:
command description
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estat AIC, BIC, VCE, and estimation sample summary
estat (svy) postestimation statistics for survey data
estimates cataloging estimation results
lincom point estimates, standard errors, testing, and
inference for linear combinations of
coefficients
linktest link test for model specification
(1) lrtest likelihood-ratio test
margins marginal means, predictive margins, marginal
effects, and average marginal effects
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
test Wald tests of simple and composite linear
hypotheses
testnl Wald tests of nonlinear hypotheses
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(1) lrtest is not appropriate with svy estimation results.
Special-interest postestimation commands
estat concordance calculates Harrell's C, which is defined as the
proportion of all usable subject pairs in which the predictions and
outcomes are concordant. estat concordance also reports the Somers' D
rank correlation, which is obtained by calculating 2(C-0.5).
Syntax for predict
predict [type] newvar [if] [in] [, sv_statistic nooffset partial]
predict [type] {stub*|newvarlist} [if] [in] , mv_statistic [partial]
sv_statistic description
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Main
hr predicted hazard ratio, also known as the relative
hazard; the default
xb linear prediction xb
stdp standard error of the linear prediction; SE(xb)
* basesurv baseline survivor function
* basechazard baseline cumulative hazard function
* basehc baseline hazard contributions
* mgale martingale residuals
* csnell Cox-Snell residuals
* deviance deviance residuals
* ldisplace likelihood displacement values
* lmax LMAX measures of influence
* effects log-frailties
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mv_statistic description
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Main
* scores efficient score residuals
* esr synonym for scores
* dfbeta DFBETA measures of influence
* schoenfeld Schoenfeld residuals
* scaledsch scaled Schoenfeld residuals
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Unstarred statistics are available both in and out of sample; type
predict ... if e(sample) ... if wanted for only the estimation sample.
Starred statistics are calculated for only the estimation sample, even
when e(sample) is not specified. nooffset is allowed only with
unstarred statistics.
mgale, csnell, deviance, ldisplace, lmax, dfbeta, schoenfeld, and
scaledsch are not allowed with svy estimation results.
Menu
Statistics > Postestimation > Predictions, residuals, etc.
Options for predict
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----+ Main +-------------------------------------------------------------
hr, the default, calculates the relative hazard (hazard 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 b0, 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.
basesurv calculates the baseline survivor function. In the null model,
this is equivalent to the Kaplan-Meier product-limit estimate. If
stcox's strata() option was specified, baseline survivor functions
for each stratum are provided.
basechazard calculates the cumulative baseline hazard. If stcox's
strata() option was specified, cumulative baseline hazards for each
stratum are provided.
basehc calculates the baseline hazard contributions. These are used to
construct the product-limit type estimator for the baseline survivor
function generated by basesurv. If stcox's strata() option was
specified, baseline hazard contributions for each stratum are
provided.
mgale calculates the martingale 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 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-per-subject data, the
partial martingale residuals are the martingale residuals.
csnell calculates the Cox-Snell generalized 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 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 data, the partial
Cox-Snell residuals are the Cox-Snell 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.
ldisplace calculates the likelihood displacement values. A likelihood
displacement value is an influence measure of the effect of deleting
a subject on the overall coefficient vector. 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 likelihood
displacement values, one for each record within subject; see partial
below. Partial displacement values are interpreted as effects due to
deletion of individual records rather than deletion of individual
subjects. In single-record data, the partial likelihood displacement
values are the likelihood displacement values.
lmax calculates the LMAX measures of influence. LMAX values are related
to likelihood displacement values because they also measure the
effect of deleting a subject on the overall coefficient vector. For
multiple-record data, by default only one LMAX value per subject is
calculated, and it is placed on the last record for the subject.
Adding the partial option will produce partial LMAX values, one for
each record within subject; see partial below. Partial LMAX values
are interpreted as effects due to deletion of individual records
rather than deletion of individual subjects. In single-record data,
the partial LMAX values are the LMAX values.
effects is for use after stcox, shared() and provides estimates of the
log frailty for each group. The log frailties are random
group-specific offsets to the linear predictor that measure the group
effect on the log relative-hazard.
scores calculates the efficient score residuals for each regressor in the
model. 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 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 score residuals are the efficient score
residuals.
One efficient score 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.
esr is a synonym for scores.
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 residuals. This option may not be
used after stcox with the exactm or exactp option. Schoenfeld
residuals are calculated and reported only at failure times.
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.
scaledsch calculates the scaled Schoenfeld residuals. This option may
not be used after stcox with the exactm or exactp option. Scaled
Schoenfeld residuals are calculated and reported only at failure
times.
One scaled 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.
nooffset is allowed only with hr, xb, and stdp, and is relevant only if
you specified offset(varname) for stcox. 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.
partial is relevant only for multiple-record data and is valid with
mgale, csnell, deviance, ldisplace, lmax, 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.
Predictions after stcox with the tvc() option
The only predict options supported after stcox with the tvc() option are
the hr, xb, and stdp options. The other predictions require that you
stsplit your data to draw out the time-varying covariates inferred by
tvc(); see tvc note.
Predictions after stcox with the shared() option
All predict options described above are supported for shared-frailty
models fit using stcox with the shared() option. Predictions are
conditional on the estimated frailty variance, theta, and the definition
of baseline is extended to represent covariates equal to 0 and a frailty
value of 1 (log frailty of 0).
Syntax for estat concordance
estat concordance [if] [in] [, concordance_options]
concordance_options description
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Main
all compute statistic for all observations in the data
noshow do not show st setting information
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Menu
Statistics > Postestimation > Reports and statistics
Options for estat concordance
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all requests that the statistic be computed for all observations in the
data. By default, estat concordance computes over the estimation
subsample.
noshow prevents estat concordance from displaying the identities of the
key st variables above its output.
Examples
Setup
. webuse drugtr
Declare data to be survival-time data
. stset studytime, failure(died)
Fit Cox model
. stcox drug age
Obtain martingale residuals
. predict double mg, mgale
Obtain Cox-Snell residuals
. predict double cs, csnell
Obtain deviance residuals
. predict double dev, deviance
Calculate Harrell's C
. estat concordance, noshow
Saved results
estat concordance saves the following in r():
Scalars
r(N) number of observations
r(n_P) number of comparison pairs
r(n_E) number of orderings as expected
r(n_T) number of tied predictions
r(D) Somers' D coefficient
r(C) Harrell's C coefficient
r(n_P), r(n_E), and r(n_T) are returned only when strata are not
specified.
Also see
Manual: [ST] stcox postestimation
Help: [ST] stcox;
[ST] stcox PH-assumption tests, [ST] stcurve
