Triparna Poddar, Ph.D. Candidate, Department of Biostatistics and Bioinformatics, University of Louisville

"Estimation of Average treatment effect for Survival Outcomes with Continuous Treatment In Observational Studies"

Recent literature on causal effect for survival analyses mainly focus on multiple treatment settings, studies with continuous treatment setting are seldom explored. In this project, we investigate estimating the average treatment effect (ATE) of continuous treatment on time to event outcome by adjusting multiple confounding factors and considering censoring observations. To adjust confounding factors, various propensity score methods such as multinomial regression and covariate balance propensity score models are used to estimate the ATE via the inverse probability of treatment weighting (IPTW) method. For continuous treatments, the IPTW is generated from covariate balancing generalized propensity score. To remedy the possible bias in estimating ATE for time-to-event data due to censoring observations, we incorporate the censoring weights to estimate ATE. We propose using both the IPTW and the censoring weights (say, double weighting approach) to estimate ATE using the marginal structural accelerated failure time (AFT) model, where the IPTW adjusts for confounding factors and the censoring weights remedy the impact due to censored observations. Extensive simulation studies demonstrated our proposed method performed well. We applied our proposed method to study the impact of the social economic condition on the development of alcoholic liver disease (ALD) among patients who had been diagnosed with alcohol use disorder.

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