help stcrreg dialog: stcrreg
also see: stcrreg postestimation
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Title
[ST] stcrreg -- Competing-risks regression
Syntax
stcrreg [varlist] [if] [in], compete(crvar[==numlist]) [options]
options description
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Model
* compete(crvar[==numlist]) specify competing-risks event(s)
tvc(tvarlist) time-varying covariates
texp(exp) multiplier for time-varying covariates;
default is texp(_t)
offset(varname) include varname in model with coefficient
constrained to 1
constraints(constraints) apply specified linear constraints
collinear keep collinear variables
SE/Robust
vce(vcetype) vcetype may be robust, cluster clustvar,
bootstrap, or jackknife
noadjust do not use standard degree-of-freedom
adjustment
Reporting
level(#) set confidence level; default is level(95)
noshr report coefficients, not subhazard ratios
noshow do not show st setting information
noheader suppress header from coefficient table
notable suppress coefficient table
nodisplay suppress output; iteration log is still
displayed
nocnsreport do not display constraints
display_options control spacing and display of omitted
variables and base and empty cells
Maximization
maximize_options control the maximization process; seldom
used
+ coeflegend display coefficients' legend instead of
coefficient table
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* compete(crvar[==numlist]) is required.
+ coeflegend does not appear in the dialog box.
You must stset your data before using stcrreg; see [ST] stset.
varlist and tvarlist may contain factor variables; see fvvarlist.
bootstrap, by, jackknife, mi estimate, nestreg, statsby, and stepwise are
allowed; see prefix.
vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate
prefix.
fweights, iweights, and pweights may be specified using stset; see [ST]
stset. In multiple-record data, weights are applied to subjects as a
whole, not to individual observations. iweights are treated as
fweights that can be noninteger, but not negative. Weights are not
allowed with the bootstrap prefix.
See [ST] stcrreg postestimation for features available after estimation.
Menu
Statistics > Survival analysis > Regression models > Competing-risks
regression
Description
stcrreg fits, via maximum likelihood, competing-risks regression models
on st data, according to the method of Fine and Gray (1999).
Competing-risks regression posits a model for the subhazard function of a
failure event of primary interest. In the presence of competing failure
events that impede the event of interest, a standard analysis using Cox
regression (see stcox) is able to produce incidence-rate curves that
either 1) are appropriate only for a hypothetical universe where
competing events do not occur or 2) are appropriate for the data at hand,
yet the effects of covariates on these curves are not easily quantified.
Competing-risks regression, as performed using stcrreg, provides an
alternative model that can produce incidence curves that represent the
observed data and for which describing covariate effects is
straightforward.
stcrreg can be used with single- or multiple-record st data. stcrreg
cannot be used when you have multiple failures per subject.
Options
+-------+
----+ Model +------------------------------------------------------------
compete(crvar[==numlist]) is required and specifies the events that are
associated with failure due to competing risks.
If compete(crvar) is specified, crvar is interpreted as an indicator
variable; any nonzero, nonmissing values are interpreted as
representing competing events.
If compete(crvar==numlist) is specified, records with crvar taking on
any of the values in numlist are assumed to be competing events.
The syntax for compete() is the same as that for stset's failure()
option. Use stset, failure() to specify the failure event of
interest, that is, the failure event you wish to model using stcox,
streg, stcrreg, or whatever. Use stcrreg, compete() to specify the
event or events that compete with the failure event of interest.
Competing events, because they are not the failure event of primary
interest, must be stset as censored.
If you have multiple records per subject, only the value of crvar for
the last chronological record for each subject is used to determine
the event type for that subject.
tvc(tvarlist) specifies those variables that vary continuously with
respect to time, i.e., time-varying covariates. These variables are
multiplied by the function of time specified in texp().
texp(exp) is used in conjunction with tvc(tvarlist) to specify the
function of analysis time that should be multiplied by the
time-varying covariates. For example, specifying texp(ln(_t)) would
cause the time-varying covariates to be multiplied by the logarithm
of analysis time. If tvc(tvarlist) is used without texp(exp), Stata
understands that you mean texp(_t), and thus multiplies the
time-varying covariates by the analysis time.
See [ST] stcrreg for more information on the tvc() and texp()
options.
offset(varname), constraints(constraints), collinear; see [R] estimation
options.
+------------+
----+ SE/Robust +-------------------------------------------------------
vce(vcetype) specifies the type of standard error reported, which
includes types that are robust to some kinds of misspecification,
that allow for intragroup correlation, and that use bootstrap or
jackknife methods; see [R] vce_option. vce(robust) is the default in
single-record-per-subject st data. For multiple-record st data,
vce(cluster idvar) is the default, where idvar is the ID variable
previously stset.
Standard Hessian-based standard errors -- vcetype oim -- are not
statistically appropriate for this model and thus are not allowed.
noadjust is for use with vce(robust) or vce(cluster clustvar). noadjust
prevents the estimated variance matrix from being multiplied by
N/(N-1) or g/(g-1), where g is the number of clusters. The default
adjustment is somewhat arbitrary because it is not always clear how
to count observations or clusters. In such cases, however, the
adjustment is likely to be biased toward 1, so we would still
recommend making it.
+------------+
----+ Reporting +-------------------------------------------------------
level(#); see [R] estimation options.
noshr specifies that coefficients be displayed rather than exponentiated
coefficients or subhazard ratios. This option affects only how
results are displayed and not how they are estimated. noshr may be
specified at estimation time or when redisplaying previously
estimated results (which you do by typing stcrreg without a variable
list).
noshow prevents stcrreg from showing the key st variables. This option
is seldom used because most people type stset, show or stset, noshow
to set whether they want to see these variables mentioned at the top
of the output of every st command; see [ST] stset.
noheader suppresses the header information from the output. The
coefficient table is still displayed. noheader may be specified at
estimation time or when redisplaying previously estimated results.
notable suppresses the table of coefficients from the output. The header
information is still displayed. notable may be specified at
estimation time or when redisplaying previously estimated results.
nodisplay suppresses the output. The iteration log is still displayed.
nocnsreport; see [R] estimation options.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
+--------------+
----+ Maximization +-----------------------------------------------------
maximize_options: difficult, technique(algorithm_spec), iterate(#),
[no]log, trace, gradient, showstep, hessian, showtolerance,
tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance,
from(init_specs); see [R] maximize. These options are seldom used.
The following option is available with stcrreg but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Example of competing-risks regression
Setup
. webuse hypoxia
Declare data to be survival-time data and declare the failure event of
interest, that is, the event to be modeled
. stset dftime, failure(failtype==1)
Fit competing-risks model with failtype==2 as the competing event
. stcrreg ifp tumsize pelnode, compete(failtype==2)
Replay results, but show coefficients rather than subhazard ratios
. stcrreg, noshr
Saved results
stcrreg saves the following in e():
Scalars
e(N) number of observations
e(N_sub) number of subjects
e(N_fail) number of failures
e(N_compete) number of competing events
e(N_censor) number of censored subjects
e(k) number of parameters
e(k_eq) number of equations in e(b)
e(k_eq_model) number of equations in model Wald test
e(k_autoCns) number of base, empty, and omitted constraints
e(k_dv) number of dependent variables
e(df_m) model degrees of freedom
e(ll) log pseudolikelihood
e(N_clust) number of clusters
e(chi2) chi-squared statistic
e(p) significance
e(k_eform) number of leading equations appropriate for eform
output
e(rank) rank of e(V)
e(fmult) 1 if > 1 failure events specified, 0 otherwise
e(crmult) 1 if > 1 competing events specified, 0 otherwise
e(fnz) 1 if nonzero indicates failure, 0 otherwise
e(crnz) 1 if nonzero indicates competing, 0 otherwise
e(ic) number of iterations
e(rc) return code
e(converged) 1 if converged, 0 otherwise
Macros
e(cmd) stcrreg
e(cmdline) command as typed
e(depvar) name of dependent variable
e(mainvars) variables in main equation
e(tvc) time-varying covariates
e(texp) function used for time-varying covariates
e(fevent) failure event(s) in estimation output
e(crevent) competing event(s) in estimation output
e(compete) competing event(s) as typed
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(clustvar) name of cluster variable
e(offset1) offset
e(chi2type) Wald; type of model chi-squared test
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
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(singularHmethod) m-marquardt or hybrid; method used when Hessian
is singular
e(crittype) optimization criterion
e(properties) b V
e(predict) program used to implement predict
e(marginsnotok) predictions disallowed by 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
e(gradient) gradient vector
e(V) variance-covariance matrix of the estimators
e(V_modelbased) model-based variance
Functions
e(sample) marks estimation sample
Reference
Fine, J. P., and R. J. Gray. 1999. A proportional hazards model for the
subdistribution of a competing risk. Journal of the American
Statistical Association 94: 496-509.
Also see
Manual: [ST] stcrreg
Help: [ST] stcrreg postestimation; [ST] stcurve;
[ST] stcox, [ST] stset, [ST] streg
