Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Canada.
Stat Methods Med Res. 2019 Feb;28(2):357-371. doi: 10.1177/0962280217726800. Epub 2017 Aug 24.
Large databases used in observational studies of drug safety often lack information on important confounders. The resulting unmeasured confounding bias may be avoided by using additional confounder information, frequently available in smaller clinical "validation samples". Yet, no existing method that uses such validation samples is able to deal with unmeasured time-varying variables acting as both confounders and possible mediators of the treatment effect. We propose and compare alternative methods which control for confounders measured only in a validation sample within marginal structural Cox models. Each method corrects the time-varying inverse probability of treatment weights for all subject-by-time observations using either regression calibration of the propensity score, or multiple imputation of unmeasured confounders. Two proposed methods rely on martingale residuals from a Cox model that includes only confounders fully measured in the large database, to correct inverse probability of treatment weight for imputed values of unmeasured confounders. Simulation demonstrates that martingale residual-based methods systematically reduce confounding bias over naïve methods, with multiple imputation including the martingale residual yielding, on average, the best overall accuracy. We apply martingale residual-based imputation to re-assess the potential risk of drug-induced hypoglycemia in diabetic patients, where an important laboratory test is repeatedly measured only in a small sub-cohort.
大型数据库在药物安全性的观察性研究中常缺乏重要混杂因素的信息。通过使用额外的混杂因素信息（通常在较小的临床“验证样本”中可用），可以避免由此产生的未测量混杂偏倚。然而，目前尚无使用此类验证样本的方法能够处理作为混杂因素和治疗效果的可能中介的未测量时变变量。我们提出并比较了替代方法，这些方法在边缘结构 Cox 模型中控制仅在验证样本中测量的混杂因素。每种方法都使用倾向评分的回归校准或未测量混杂因素的多重插补，对所有个体-时间观察值的时变治疗反概率权重进行校正。两种拟议的方法都依赖于 Cox 模型中的马氏残差，该模型仅包含在大型数据库中完全测量的混杂因素，以校正未测量混杂因素的插补值的治疗反概率权重。模拟表明，马氏残差基于的方法系统地减少了混杂偏差，而包含马氏残差的多重插补平均来说具有最佳的整体准确性。我们应用马氏残差基于的插补方法重新评估了糖尿病患者中药物引起低血糖的潜在风险，其中一项重要的实验室测试仅在一个小亚队列中反复测量。
