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Activity Number: 238 - SPEED: Environment and Health, Governmental Policies and Population Surveys, Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Lifetime Data Science Section
Abstract #305131 Presentation
Title: Conditional Survival Methods for Evaluating the Effect of a Time-Dependent Treatment on the Survival Function
Author(s): Danting Zhu* and Douglas Schaubel
Companies: and University of Michigan
Keywords: Prognostic score; Matching; Survival

We propose flexible methods applicable to estimate 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. We will focus on introducing the method on survival function that would have been observed in the absence of treatment. The proposed methods utilize prognostic scores, but are otherwise nonparametric. Essentially, each patient is grouped based on the pre-treatment prognostic score. The treatment-absent survival of a particular treated patient is obtained by conditional survival function of the corresponding group. Then we weight each treatment-absent survival function by IPTW. 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. Large-sample properties are derived, with finite-sample properties evaluated through simulation.

Authors who are presenting talks have a * after their name.

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