Initiator Types and the Causal Question of the Prevalent New-User Design: A Simulation Study.
Initiator Types and the Causal Question of the Prevalent New-User Design: A Simulation Study.
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引发剂类型和流行的新用户设计的因果问题：模拟研究。
DOI：
10.1093/aje/kwaa283
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发表时间：
2021
影响因子：
5
通讯作者：
Lund,JenniferL
中科院分区：
文献类型：
--
作者：
Webster-Clark,Michael;Ross,RachaelK;Lund,JenniferL
限制治疗起始剂的新用户设计已成为利用非实验数据研究药物比较安全性和有效性的首选设计。这种设计以较小的研究规模和降低外部有效性为代价，减少了适应症和健康坚持者的偏见造成的混淆，特别是在评估新批准的治疗与标准治疗相比时。流行的新用户设计包括从标准治疗转向或以前使用标准治疗(即对照)的新治疗的采用者，扩大了研究样本量，潜在地扩大了用于推断的研究人群。以前的工作已经建议使用时间条件倾向-分数匹配来缓解普遍存在的用户偏见。在这项研究中，我们描述了三种类型的治疗发起者：新用户、直接转换者和延迟转换者。使用这些启动器类型，我们清楚地表达了流行的新用户设计所回答的因果问题，并将它们与新用户设计所回答的问题进行比较。然后，我们使用模拟显示，自启动比较器(而不是完整的治疗历史)以来，如何对时间进行条件调整仍然可以导致对治疗效果的偏见估计。如果实施得当，流行的新用户设计会估计出与新用户设计不同的新的重要因果关系。
New-user designs restricting to treatment initiators have become the preferred design for studying drug comparative safety and effectiveness using nonexperimental data. This design reduces confounding by indication and healthy-adherer bias at the cost of smaller study sizes and reduced external validity, particularly when assessing a newly approved treatment compared with standard treatment. The prevalent new-user design includes adopters of a new treatment who switched from or previously used standard treatment (i.e., the comparator), expanding study sample size and potentially broadening the study population for inference. Previous work has suggested the use of time-conditional propensity-score matching to mitigate prevalent user bias. In this study, we describe 3 “types” of initiators of a treatment: new users, direct switchers, and delayed switchers. Using these initiator types, we articulate the causal questions answered by the prevalent new-user design and compare them with those answered by the new-user design. We then show, using simulation, how conditioning on time since initiating the comparator (rather than full treatment history) can still result in a biased estimate of the treatment effect. When implemented properly, the prevalent new-user design estimates new and important causal effects distinct from the new-user design.