Doug Schaubel

Matching methods for evaluating the effect of a time-dependent treatment on the survival function

We consider observational studies of survival time featuring a binary time-dependent treatment. We propose flexible methods applicable to big data sets for the purpose of estimating the causal effect of treatment among the treated with respect to survival probability. The objective is to compare post-treatment survival with the survival function that would have been observed in the absence of treatment.   The proposed methods utilize prognostic scores, but are otherwise nonparametric.  Essentially, each treated patient is matched to a group of similar not-yet-treated patients. The treatment effect is then estimated through a difference in weighted Nelson-Aalen survival curves, which can be subsequently integrated to obtain the corresponding difference in restricted mean survival time (area between the survival curves). Large-sample properties are derived, with finite-sample properties evaluated through simulation. The proposed methods are then applied to estimate the effect on survival of kidney transplantation.  This is joint work with Kevin He, Yun Li and Danting Zhu