Xu and Cheung (2015, Stata Journal 15: 135–154) introduced the strmcure command, which fits frailty models and frailty-mixture models in the analysis of recurrent event times. In this article, we provide an update to strmcure. The update implements a two-step estimation procedure for a frailty-mixture model that allows the estimation of the effect of an intervention on the probability of cure and on the total effect on event rate in the noncured. To illustrate, we will use the same example dataset on respiratory exacerbations from the original article.
Box-SteffensmeierJ. M., and De BoefS.2006. Repeated events survival models: The conditional frailty model. Statistics in Medicine25: 3518–3533.
2.
CheungY. B., XuY., TanS. H., CuttsF., and MilliganP.2010. Estimation of intervention effects using first or multiple episodes in clinical trials: The Andersen-Gill model re-examined. Statistics in Medicine29: 328–336.
3.
CookR. J., and LawlessJ. F.2007. The Statistical Analysis of Recurrent Events.New York: Springer.
4.
HandD. J.1994. Deconstructing statistical questions. Journal of the Royal Statistical Society, Series A157: 317–356.
5.
LouisT. A.1982. Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society, Series B44: 226–233.
6.
XuY., and CheungY. B.2015. Frailty models and frailty-mixture models for recurrent event times. Stata Journal15: 135–154.
7.
XuY., CheungY. B., LamK. F., and MilliganP.2012. Estimation of summary protective efficacy using a frailty mixture model for recurrent event time data. Statistics in Medicine31: 4023–4039.
8.
XuY., LamK. F., and CheungY. B.2014. Estimation of intervention effects using recurrent event time data in the presence of event dependence and a cured fraction. Statistics in Medicine33: 2263–2274.
