Semiparametric Inference for Proportional Mean Past Life Model

The mean past lifetime provides the expected time elapsed since the failure of a subject given that he/she has failed before the time of observation. In this paper, we propose the proportional mean past lifetime model to study the association between the mean past lifetime function and potential regression covariates. In the presence of left censoring, martingale estimating equations are developed to estimate the model parameters, and the asymptotic properties of the resulting estimators are studied. To assess the adequacy of the model, a goodness of fit test is also investigated. The proposed method is evaluated via simulation studies and further applied to a data set.

Flexibly Monitoring Group Sequential Survival Trials When Testing is Based Upon a Weighted Log-Rank Statistic

We consider the repeated group sequential testing of a survival endpoint with a time-varying treatment effect using a weighted logrank statistic. The emphasis of this paper is on the monitoring of this statistic where information growth is non-linear. We propose using a constrained boundaries approach to maintain the planned operating characteristics of a group sequential design. A simulation study is presented to demonstrate the operating characteristics of the method together with a case study to illustrate the procedure. We show that when monitoring a weighted logrank statistic, the entry and survival distribution needs to be estimated at interim analyses.