BACKGROUND Attrition due to death and non-attendance are common sources of bias in studies of age-related diseases. A simulation study is presented to compare two methods for estimating the survivor average causal effect (SACE) of a binary exposure (sex-specific dietary iron intake) on a binary outcome (age-related macular degeneration, AMD) in this setting. METHODS A dataset of 10,000 participants was simulated 1200 times under each scenario with outcome data missing dependent on measured and unmeasured covariates and survival. Scenarios differed by the magnitude and direction of effect of an unmeasured confounder on both survival and the outcome, and whether participants who died following a protective exposure would also die if they had not received the exposure (validity of the monotonicity assumption). The performance of a marginal structural model (MSM, weighting for exposure, survival and missing data) was compared to a sensitivity approach for estimating the SACE. As an illustrative example, the SACE of iron intake on AMD was estimated using data from 39,918 participants of the Melbourne Collaborative Cohort Study. RESULTS The MSM approach tended to underestimate the true magnitude of effect when the unmeasured confounder had opposing directions of effect on survival and the outcome. Overestimation was observed when the unmeasured confounder had the same direction of effect on survival and the outcome. Violation of the monotonicity assumption did not increase bias. The estimates were similar between the MSM approach and the sensitivity approach assessed at the sensitivity parameter of 1 (assuming no survival bias). In the illustrative example, high iron intake was found to be protective of AMD (adjusted OR 0.57, 95% CI 0.40-0.82) using complete case analysis via traditional logistic regression. The adjusted SACE odds ratio did not differ substantially from the complete case estimate, ranging from 0.54 to 0.58 for each of the SACE methods. CONCLUSIONS On average, MSMs with weighting for exposure, missing data and survival produced biased estimates of the SACE in the presence of an unmeasured survival-outcome confounder. The direction and magnitude of effect of unmeasured survival-outcome confounders should be considered when assessing exposure-outcome associations in the presence of attrition due to death.

中文翻译：

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在缺少数据的情况下估算幸存者平均因果效应的方法比较：一项模拟研究。

背景技术在与年龄有关的疾病的研究中，由于死亡和缺勤而引起的人员流失是偏见的常见来源。提出了一项模拟研究，以比较两种方法估算在这种情况下二元暴露（性别特定的饮食铁摄入量）对二元结局（年龄相关性黄斑变性，AMD）的幸存者平均因果效应（SACE）。方法在每种情况下，对10,000名参与者的数据集进行了1200次模拟，结果数据的缺失取决于测量和未测量的协变量以及生存率。情景因无法衡量的混杂因素对生存和结果的影响的大小和方向的不同而不同，并且如果在未受到保护性暴露后死亡的参与者也未接受暴露也会死亡（单调性假设的有效性）。将边际结构模型（MSM，暴露，生存和缺失数据的权重）的性能与用于估计SACE的敏感性方法进行了比较。举一个说明性的例子，使用来自墨尔本合作队列研究的39,918名参与者的数据估算了AMD上铁摄入的SACE。结果当无法衡量的混杂因素对生存和结果有相反的影响方向时，MSM方法往往会低估影响的真实程度。当无法衡量的混杂因素对生存和结果具有相同的影响方向时，会观察到高估。违反单调性假设并不会增加偏差。MSM方法和以敏感性参数1（假定无生存偏差）评估的敏感性方法之间的估计值相似。在说明性示例中，使用传统的逻辑回归进行完整病例分析，发现高铁摄入量可预防AMD（调整后的OR 0.57，95％CI 0.40-0.82）。调整后的SACE优势比与完整案例估计值没有实质性差异，每种SACE方法的范围从0.54到0.58。结论平均而言，在存在无法衡量的生存结果混杂因素的情况下，MSM随暴露，数据丢失和生存权重而产生的SACE估计值存在偏差。在评估由于死亡导致的流失的暴露-结果关联时，应考虑无法衡量的生存-结果混杂因素的作用方向和幅度。82）使用传统的逻辑回归进行完整的案例分析。调整后的SACE优势比与完整案例估计值没有实质性差异，每种SACE方法的范围从0.54到0.58。结论平均而言，在存在无法衡量的生存结果混杂因素的情况下，MSM随暴露，数据丢失和生存权重而产生的SACE估计值存在偏差。在评估由于死亡导致的流失的暴露-结果关联时，应考虑无法衡量的生存-结果混杂因素的作用方向和幅度。82）使用传统的逻辑回归进行完整的案例分析。调整后的SACE优势比与完整案例估计值没有实质性差异，每种SACE方法的范围从0.54到0.58。结论平均而言，在存在无法衡量的生存结果混杂因素的情况下，MSM随暴露，数据丢失和生存权重而产生的SACE估计值存在偏差。在评估由于死亡导致的流失的暴露-结果关联时，应考虑无法衡量的生存-结果混杂因素的作用方向和幅度。在存在无法衡量的生存结局混杂因素的情况下，数据的丢失和生存率对SACE的估计产生了偏差。在评估由于死亡导致的流失的暴露-结果关联时，应考虑无法衡量的生存-结果混杂因素的作用方向和幅度。在存在无法衡量的生存结局混杂因素的情况下，数据的丢失和生存率对SACE的估计产生了偏差。在评估由于死亡导致的流失的暴露-结果关联时，应考虑无法衡量的生存-结果混杂因素的作用方向和幅度。