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A comparison of estimators from self-controlled case series, case-crossover design, and sequence symmetry analysis for pharmacoepidemiological studies

机译:自控病例系列,病例交叉设计和序列对称性分析在药物流行病学研究中的估计量比较

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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.
机译:尽管在药物流行病学研究中经常使用自控方法,但在实际环境中尚未充分比较可能会使这些方法的估计值产生偏差的因素。在这里,我们比较地考察了时变混杂因素的影响及其与时不变混杂因素的交互作用,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控制方法的估计。本研究使用模拟和实际电子病历数据集分析了自控病例系列(SCCS),病例交叉(CCO)设计和序列对称性分析(SSA)。在以下情况下,我们评估了三种自控方法在模拟队列中的性能:1)时变混杂与混杂因素之间的相互作用; 2)时变混杂和时变混杂而没有相互作用; 3)时不变和时变与混杂因素之间的相互作用混杂在一起; 4)暴露和事件的时间趋势; 5)基于事件发生的限制随访时间; 6)基于事件历史的患者限制。还评估了估算者对错误指定的风险期持续时间的敏感性。作为案例研究,我们使用电子病历将这些方法应用于评估大环内酯类药物对肝损伤的风险。在仿真分析中,时变混杂因素在SCCS和CCO设计估计中产生了偏差,当时不变和时变混杂因素之间存在相互作用时,这种情况会加剧。 SCCS估计值受事件和事件的时间趋势所影响。错误的短风险期对CCO设计估计值造成了偏差,而错误的长风险期对这三种方法的估计值造成了偏差。限制随访时间导致SSA估算严重偏差。 SCCS估计对患者限制很敏感。案例研究表明,尽管在所有方法中使用大环内酯类药物均与增加的肝损伤发生率显着相关,但估算值却有所不同。三种自控方法的估计取决于各种基本假设,违反这些假设可能会导致最终估计中的偏差不可忽略。药物流行病学家应根据相关关键假设对现有数据的满意程度,选择适当的自我控制方法。

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