Causal inference in statistics

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I will continue to develop my research program on statistical methods for causal inference in epidemiology, with focus on marginal structural models propensity score. My work to date has focused on the use of the propensity score in marginal structural models (MSMs) and targeted maximum likelihood (TML) methods. The core of my current and proposed work extends these results. I will investigate the properties of various statistical learning-based methods for fitting the propensity score, in the context of MSMs and TML. Via theoretical results and simulations, I will describe the properties of the HDPS in general, and in particular for use in marginal structural models. Close analogues of the propensity score may also be used in randomized trial design. It has been shown that the propensity for selection into a trial could be used to weight results from subjects in the trial to generalize to a larger population. With Elizabeth Stuart, I am investigating the properties of this propensity score for design of randomized trials and their use in meta-analysis. Consider a trial in which the subjects do not adequately reflect the population of interest. We have shown (manuscript in preparation) that if one uses the inverse of the probability of selection into the first trial as a weight for subjects in a second trial, bias is reduced relative to random selection for the second trial. This result will be extended to multiple trials. Finally, I will continue my work on longitudinal models for multivariate outcomes, with applications to pregnancy outcomes. Specifically, I will extend models for longitudinal growth processes to multiple outcomes, and develop a multivariate model extending Cole's SuperImposition by Translation And Rotation model.