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Abstract:

Doubly robust estimation under the marginal structural Cox model has been a challenge until recently due to the non-collapsibility of the Cox regression model. This is because the estimand of causal hazard ratio assumes that the marginal structural Cox model holds, while the doubly robust estimating function requires the specification of an additional model for the conditional distribution of the time-to-event given treatment and covariates, both models unlikely to hold simultaneously. It became possible recently to resolve this issue with the understanding of rate double robustness and machine learning or nonparametric approaches, although technical details are still to be spelt out to ensure root-n inference for the estimand. We describe our work considering both observational studies setting and in the presence of covariate-induced informative censoring. An added benefit of our approach is the interpretation of the estimand when the assumed marginal structural Cox model does not hold, as a time-averaged treatment effect. This allows meaningful estimation of treatment effects for general two-group comparison without the Cox model, or under alternative models such as the semiparametric proportional odds or transformation models for the potential time-to-event outcomes.

Speaker

Dr. Xu received her Ph.D. from the University of California, San Diego. She served as an assistant prof of biostatistics at the Harvard school of public health and the Dana-Farber cancer institute. She returned to UC San Diego in 2004 and is now Lead of the Teaching Division of Biostatistics and Bioinformatics, and Director of Research Program of Quantitative Methods in Public Health, at the Herbert Wertheim School of Public Health. She is a recipient of the David Byar young investigator award, and a fellow of the ASA.

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