论文标题
边际结构模型的功率和样本量
Power and Sample Size for Marginal Structural Models
论文作者
论文摘要
边缘结构模型通过治疗加权的反可能性拟合，通常用于控制观察数据的因果关系时混淆。在计划将通过边缘结构建模进行分析的研究时，确定给定水平的统计能力所需的样本量很具有挑战性，因为加权对估计的因果关系方差的影响。本文考虑了设计效果的实用性，以量化加权对因果估计的精度的影响。设计效果定义为因果均值估计器的方差之比除以幼稚估计器的方差，如果与事实相反，不需要混淆，并且不需要权重。设计效果的简单，封闭形式的近似是结果不变的，可以在研究设计阶段进行估算。一旦为每个治疗组近似设计效果，就将进行样本量计算，以进行随机试验，但由于设计效果的差异以解释加权。模拟证明了设计效果近似的准确性，并讨论了实际的考虑。
Marginal structural models fit via inverse probability of treatment weighting are commonly used to control for confounding when estimating causal effects from observational data. When planning a study that will be analyzed with marginal structural modeling, determining the required sample size for a given level of statistical power is challenging because of the effect of weighting on the variance of the estimated causal means. This paper considers the utility of the design effect to quantify the effect of weighting on the precision of causal estimates. The design effect is defined as the ratio of the variance of the causal mean estimator divided by the variance of a naive estimator if, counter to fact, no confounding had been present and weights were not needed. A simple, closed-form approximation of the design effect is derived that is outcome invariant and can be estimated during the study design phase. Once the design effect is approximated for each treatment group, sample size calculations are conducted as for a randomized trial, but with variances inflated by the design effects to account for weighting. Simulations demonstrate the accuracy of the design effect approximation, and practical considerations are discussed.
