Ertefaie Ashkan, Flory James H, Hennessy Sean, Small Dylan S
Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, New York.
Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania.
Am J Epidemiol. 2017 Jun 15;185(12):1233-1239. doi: 10.1093/aje/kww137.
Instrumental variable (IV) methods provide unbiased treatment effect estimation in the presence of unmeasured confounders under certain assumptions. To provide valid estimates of treatment effect, treatment effect confounders that are associated with the IV (IV-confounders) must be included in the analysis, and not including observations with missing values may lead to bias. Missing covariate data are particularly problematic when the probability that a value is missing is related to the value itself, which is known as nonignorable missingness. In such cases, imputation-based methods are biased. Using health-care provider preference as an IV method, we propose a 2-step procedure with which to estimate a valid treatment effect in the presence of baseline variables with nonignorable missing values. First, the provider preference IV value is estimated by performing a complete-case analysis using a random-effects model that includes IV-confounders. Second, the treatment effect is estimated using a 2-stage least squares IV approach that excludes IV-confounders with missing values. Simulation results are presented, and the method is applied to an analysis comparing the effects of sulfonylureas versus metformin on body mass index, where the variables baseline body mass index and glycosylated hemoglobin have missing values. Our result supports the association of sulfonylureas with weight gain.
在某些假设下，工具变量（IV）方法可在存在未测量混杂因素的情况下提供无偏的治疗效果估计。为了提供有效的治疗效果估计，与工具变量相关的治疗效果混杂因素（IV混杂因素）必须纳入分析，而不包括有缺失值的观察结果可能会导致偏差。当一个值缺失的概率与该值本身相关时，即所谓的不可忽略的缺失值，协变量数据缺失问题尤为严重。在这种情况下，基于插补的方法存在偏差。以医疗服务提供者的偏好作为一种工具变量方法，我们提出了一个两步程序，用于在存在具有不可忽略缺失值的基线变量的情况下估计有效的治疗效果。首先，通过使用包含IV混杂因素的随机效应模型进行完全病例分析来估计提供者偏好IV值。其次，使用两阶段最小二乘IV方法估计治疗效果，该方法排除具有缺失值的IV混杂因素。给出了模拟结果，并将该方法应用于一项比较磺脲类药物与二甲双胍对体重指数影响的分析中，其中基线体重指数和糖化血红蛋白变量存在缺失值。我们的结果支持磺脲类药物与体重增加之间的关联。
