Bonnie E Shook-Sa, Michael G Hudgens, Andrea K Knittel, Andrew Edmonds, Catalina Ramirez, Stephen R Cole, Mardge Cohen, Adebola Adedimeji, Tonya Taylor, Katherine G Michel, Andrea Kovacs, Jennifer Cohen, Jessica Donohue, Antonina Foster, Margaret A Fischl, Dustin Long, Adaora A Adimora
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引用次数: 0
Abstract
Causal inference methods can be applied to estimate the effect of a point exposure or treatment on an outcome of interest using data from observational studies. For example, in the Women's Interagency HIV Study, it is of interest to understand the effects of incarceration on the number of sexual partners and the number of cigarettes smoked after incarceration. In settings like this where the outcome is a count, the estimand is often the causal mean ratio, i.e., the ratio of the counterfactual mean count under exposure to the counterfactual mean count under no exposure. This paper considers estimators of the causal mean ratio based on inverse probability of treatment weights, the parametric g-formula, and doubly robust estimation, each of which can account for overdispersion, zero-inflation, and heaping in the measured outcome. Methods are compared in simulations and are applied to data from the Women's Interagency HIV Study.
因果推理方法可用于利用观察性研究的数据估算点暴露或治疗对相关结果的影响。例如,在 "妇女机构间艾滋病研究"(Women's Interagency HIV Study)中,我们有兴趣了解监禁对监禁后性伴侣数量和吸烟数量的影响。在这种结果为计数的情况下,估计值通常为因果平均比率,即暴露情况下的反事实平均计数与不暴露情况下的反事实平均计数之比。本文考虑了基于逆概率处理权重、参数 g 公式和双重稳健估计的因果平均比率估计方法,每种方法都可以考虑测量结果中的过度分散、零膨胀和堆叠。通过模拟对这些方法进行了比较,并将其应用于妇女机构间艾滋病毒研究的数据中。
期刊介绍:
Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.