Department of Biostatistics, School of Public Health, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan.
Department of Healthcare Information Management, The University of Tokyo Hospital, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
BMC Med Res Methodol. 2018 Jan 8;18(1):4. doi: 10.1186/s12874-017-0457-7.
Despite the frequent use of self-controlled methods in pharmacoepidemiological studies, the factors that may bias the estimates from these methods have not been adequately compared in real-world settings. Here, we comparatively examined the impact of a time-varying confounder and its interactions with time-invariant confounders, time trends in exposures and events, restrictions, and misspecification of risk period durations on the estimators from three self-controlled methods. This study analyzed self-controlled case series (SCCS), case-crossover (CCO) design, and sequence symmetry analysis (SSA) using simulated and actual electronic medical records datasets.
We evaluated the performance of the three self-controlled methods in simulated cohorts for the following scenarios: 1) time-invariant confounding with interactions between the confounders, 2) time-invariant and time-varying confounding without interactions, 3) time-invariant and time-varying confounding with interactions among the confounders, 4) time trends in exposures and events, 5) restricted follow-up time based on event occurrence, and 6) patient restriction based on event history. The sensitivity of the estimators to misspecified risk period durations was also evaluated. As a case study, we applied these methods to evaluate the risk of macrolides on liver injury using electronic medical records.
In the simulation analysis, time-varying confounding produced bias in the SCCS and CCO design estimates, which aggravated in the presence of interactions between the time-invariant and time-varying confounders. The SCCS estimates were biased by time trends in both exposures and events. Erroneously short risk periods introduced bias to the CCO design estimate, whereas erroneously long risk periods introduced bias to the estimates of all three methods. Restricting the follow-up time led to severe bias in the SSA estimates. The SCCS estimates were sensitive to patient restriction. The case study showed that although macrolide use was significantly associated with increased liver injury occurrence in all methods, the value of the estimates varied.
The estimations of the three self-controlled methods depended on various underlying assumptions, and the violation of these assumptions may cause non-negligible bias in the resulting estimates. Pharmacoepidemiologists should select the appropriate self-controlled method based on how well the relevant key assumptions are satisfied with respect to the available data.
尽管在药物流行病学研究中经常使用自我对照方法，但在真实环境中，这些方法的估计值可能存在偏倚的因素尚未得到充分比较。在这里，我们比较地研究了时变混杂因素及其与时不变混杂因素、暴露和事件的时间趋势、限制以及风险期持续时间的错误指定对三种自我对照方法估计值的影响。本研究使用模拟和实际电子病历数据集分析了自我对照病例系列（SCCS）、病例交叉（CCO）设计和序列对称性分析（SSA）。
我们评估了三种自我对照方法在模拟队列中的性能，包括以下情况：1）时不变混杂因素且混杂因素之间存在相互作用，2）时不变和时变混杂因素且混杂因素之间没有相互作用，3）时不变和时变混杂因素且混杂因素之间存在相互作用，4）暴露和事件的时间趋势，5）基于事件发生的受限随访时间，6）基于事件史的患者限制。还评估了估计值对错误指定风险期持续时间的敏感性。作为案例研究，我们应用这些方法来评估电子病历中大环内酯类药物对肝损伤的风险。
在模拟分析中，时变混杂因素会导致 SCCS 和 CCO 设计估计值出现偏差，而当时间不变和时变混杂因素之间存在相互作用时，偏差会加剧。暴露和事件的时间趋势会使 SCCS 估计值产生偏差。错误地指定较短的风险期会导致 CCO 设计估计值产生偏差，而错误地指定较长的风险期会导致所有三种方法的估计值产生偏差。限制随访时间会严重影响 SSA 估计值。SCCS 估计值对患者限制敏感。案例研究表明，尽管大环内酯类药物的使用与所有方法中的肝损伤发生率增加显著相关，但估计值的值有所不同。
三种自我对照方法的估计值取决于各种基本假设，违反这些假设可能会导致结果估计值产生不可忽略的偏差。药物流行病学学家应根据可用数据中相关关键假设的满足程度选择适当的自我对照方法。
