Causal inference in statistics

我将继续研究流行病学因果推理的统计方法，重点是边际结构模型倾向评分。迄今为止，我的工作主要集中在边际结构模型（msm）和目标最大似然（TML）方法中倾向得分的使用。我当前和提议的工作的核心是扩展这些结果。我将研究在msm和TML的背景下，用于拟合倾向得分的各种基于统计学习的方法的特性。通过理论结果和模拟，我将描述HDPS的一般特性，特别是用于边缘结构模型。

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.