streg_postestimation.hlp

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{smcl}
{* 18mar2005}{...}
{cmd:help streg postestimation} {right:dialog:  {bf:{dialog streghet1_p:predict}}}
{right:also see:  {helpb streg}{space 2}}
{hline}

{title:Title}

{p2colset 5 34 36 2}{...}
{p2col :{hi:[ST] streg postestimation} {hline 2}}Postestimation tools for streg{p_end}
{p2colreset}{...}


{title:Description}

{pstd}
The following postestimation command is of special interest after {cmd:streg}:

{synoptset 11}{...}
{p2coldent :command}description{p_end}
{synoptline}
{synopt :{helpb stcurve}}plots the survival, hazard, and cumulative hazard functions{p_end}
{synoptline}
{p2colreset}{...}

{pstd}
In addition, The following standard postestimation commands are available:

{synoptset 11}{...}
{p2coldent :command}description{p_end}
{synoptline}
INCLUDE help post_adjust2
INCLUDE help post_estat
INCLUDE help post_estimates
INCLUDE help post_lincom
INCLUDE help post_linktest
INCLUDE help post_lrtest
INCLUDE help post_mfx
INCLUDE help post_nlcom
{p2col :{helpb stcox postestimation##predict:predict}}predictions, residuals, influence statistics, and other diagnostic measures{p_end}
INCLUDE help post_predictnl
INCLUDE help post_suest
INCLUDE help post_test
INCLUDE help post_testnl
{synoptline}
{p2colreset}{...}


{title:Special-interest postestimation command}

{cmd:stcurve} is used after {cmd:streg} (or {cmd:stcox}) to plot the
cumulative hazard, survival, and hazard functions at the mean value of the
covariates or at the values specified by the {opt at()} options.


{marker predict}{...}
{title:Syntax for predict}

{p 8 16 2}
{cmd:predict} {dtype} {newvar} {ifin} [{cmd:,} {it:statistic} 
              {opt alpha:1} {opt uncond:itional}]

{p 8 16 2}
{cmd:predict} {dtype} {it:stub}{cmd:*} {ifin} [{cmd:,} {opt sc:ores}]

{synoptset 17 tabbed}{...}
{synopthdr :statistic}
{synoptline}
{syntab:Main}
{synopt :{opt med:ian} {opt time}}predicted median survival time; the default{p_end}
{synopt :{opt med:ian} {opt lnt:ime}}predicted median ln(survival time){p_end}
{synopt :{opt mean time}}predicted mean survival time{p_end}
{synopt :{opt mean} {opt lnt:ime}}predicted mean ln(survival time){p_end}
{synopt :{opt hazard}}predicted hazard{p_end}
{synopt :{opt hr}}predicted hazard ratio, also known as the relative hazard{p_end}
{synopt :{opt xb}}linear prediction xb{p_end}
{synopt :{opt stdp}}standard error of the linear prediction; SE(xb){p_end}
{synopt :{opt s:urv}}predicted S(t|t_0){p_end}
{synopt :{opt csn:ell}}(partial) Cox-Snell residuals{p_end}
{synopt :{opt mg:ale}}(partial) martingale-like residuals{p_end}
{synopt :{opt dev:iance}}deviance residuals{p_end}
{synopt :{opt cs:urv}}predicted S(t|earliest t_0 for subject){p_end}
{synopt :{opt ccs:nell}}cumulative Cox-Snell residuals{p_end}
{synopt :{opt cmg:ale}}cumulative martingale residuals{p_end}
{synoptline}
{p2colreset}{...}
{pstd}
All statistics are available both in and out of sample; type
"{cmd:predict} {it:...} {cmd:if e(sample)} {it:...}" if wanted only for the
estimation sample.{p_end}
{pstd}
When no option is specified, the predicted median survival time is calculated
for all models.  The predicted hazard ratio option {opt hr} is only available
for the exponential, Weibull, and Gompertz models.  The {opt mean time} and
{opt mean lntime} options are not available for the Gompertz model.
Unconditional estimates of {opt mean time} and {opt mean lntime} are not
available when {opt frailty()} is specified.{p_end}


{title:Options for predict}

{dlgtab:Main}

{phang}
{opt median time} calculates the predicted median survival time in
analysis-time units.  Note that this is the prediction from time 0 conditional
on constant covariates.  When no options are specified with {cmd:predict}, the
predicted median survival time is calculated for all models.

{phang}
{opt median lntime} calculates the natural logarithm of what {opt median time}
produces.

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

{phang}
{opt mean lntime} predicts the mean of the natural logarithm of {opt time}.
This option is not available for Gompertz regression and is only available for
frailty models if {opt 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.

{phang}
{opt hazard} calculates the predicted hazard.

{phang}
{opt hr} calculates the hazard ratio.  This option is valid only for models
having a proportional hazards parameterization.

{phang}
{opt xb} calculates the linear prediction from the fitted model.

{phang}
{opt stdp} calculates the standard error of the prediction.

{phang}
{opt 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 {opt csurv} option below.

{phang}
{opt csnell} calculates the (partial) Cox-Snell residual.  If you have a
single observation per subject, {opt csnell} calculates the usual Cox-Snell
residual.  Otherwise, {opt csnell} calculates the additive contribution of
this observation to the subject's overall Cox-Snell residual.

{phang}
{opt mgale} calculates the (partial) martingale-like residual.  The
issues are the same as with {opt csnell} above.

{phang}
{opt deviance} calculates the deviance residual.  In the case of
multiple-record data, only one value per subject is calculated, and it is
placed on the last record for the subject.

{phang}
{opt 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 option {opt surv} above) and then multiplying them together.

{phang}
{opt ccsnell} calculates the (cumulative) Cox-Snell residual in
multiple-record data by calculating the partial Cox-Snell residuals (see
option {opt csnell} above) and then summing them.  Only one value per subject
is recorded{hline 2}the overall sum{hline 2}and it is placed on the last
record for the subject.

{phang}
{opt cmgale} calculates the (cumulative) martingale-like residual in
multiple-record data.  This is based on calculating the partial
martingale-like residuals (see option {opt mgale} above) and then summing
them.  Only one value per subject is recorded{hline 2}the overall 
sum{hline 2}and it is placed on the last record for the subject.

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

{phang}
{opt unconditional}, when used after fitting a frailty model, specifies that
{it: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.

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


{title:Also see}

{psee}
Manual:  {bf:[ST] streg postestimation}

{psee}
Online:  {helpb streg}; {helpb stcurve};{break}
{helpb adjust}, {helpb constraint}, 
{helpb lincom}, {helpb linktest}, {helpb lrtest}, {helpb mfx} {helpb nlcom},
{helpb predictnl}, {helpb stepwise}, {helpb test}, {helpb testnl}
{p_end}