We consider estimation of the average effect of time-varying dichotomous exposure on outcome using inverse probability weighting (IPW) under the assumption that there is no unmeasured confounding of the exposure–outcome association at each time point. Despite the popularity of IPW, its performance is often poor due to instability of the estimated weights. We develop an estimating equation-based strategy for the nuisance parameters indexing the weights at each time point, aimed at preventing highly volatile weights and ensuring the stability of IPW estimation. Our proposed approach targets the estimation of the counterfactual mean under a chosen treatment regime and requires fitting a separate propensity score model at each time point. We discuss and examine extensions to enable the fitting of marginal structural models using one propensity score model across all time points. Extensive simulation studies demonstrate adequate performance of our approach compared with the maximum likelihood propensity score estimator and the covariate balancing propensity score estimator.

中文翻译：

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纵向研究的稳定逆概率加权估计

我们考虑使用逆概率加权 (IPW) 来估计时变二分法暴露对结果的平均影响，前提是每个时间点的暴露-结果关联没有不可测量的混杂因素。尽管 IPW 很受欢迎，但由于估计权重的不稳定性，其性能通常很差。我们为在每个时间点对权重进行索引的有害参数开发了一种基于估计方程的策略，旨在防止权重高度波动并确保 IPW 估计的稳定性。我们提出的方法的目标是在选定的治疗方案下估计反事实均值，并且需要在每个时间点拟合单独的倾向评分模型。我们讨论和检查扩展，以在所有时间点使用一个倾向评分模型来拟合边际结构模型。广泛的模拟研究表明，与最大似然倾向评分估计量和协变量平衡倾向评分估计量相比，我们的方法具有足够的性能。