Competing risks are present when the patients within a dataset could experience one or more of several exclusive events and the occurrence of any one of these could impede the event of interest. One of the measures of interest for analyses of this type is the cumulative incidence function. stpm2cif is a postestimation command used to generate predictions of the cumulative incidence function after fitting a flexible parametric survival model using stpm2. There is also the option to generate confidence intervals, cause-specific hazards, and two other measures that will be discussed in further detail. The new command is illustrated through a simple example.
CovielloE.2009. stcompadj: Stata module to estimate the covariate-adjusted cumulative incidence function in the presence of competing risks. Statistical Software Components S457063, Department of Economics, Boston College.http://ideas.repec.org/c/boc/bocode/s457063.html.
2.
CovielloV., and BoggessM.2004. Cumulative incidence estimation in the presence of competing risks. Stata Journal4: 103–112.
3.
FineJ. P., and GrayR. J.1999. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association94: 496–509.
4.
LambertP. C., and RoystonP.2009. Further development of flexible parametric models for survival analysis. Stata Journal9: 265–290.
5.
PrenticeR. L., KalbfleischJ. D., PetersonA. V.Jr., FlournoyN., FarewellV. T., and BreslowN. E.1978. The analysis of failure times in the presence of competing risks. Biometrics34: 541–554.
6.
PutterH., FioccoM., and GeskusR. B.2007. Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine26: 2389–2430.
7.
RoystonP., and ParmarM. K. B.2002. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine21: 2175–2197.
