When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?
When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?
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DOI：
10.1002/cpt.2255
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发表时间：
2022-01
影响因子：
6.7
通讯作者：
中科院分区：
文献类型：
--
作者：
随机对照试验（RCT）是评价药物因果关系的金标准。RCT的局限性导致人们越来越关注使用真实的世界证据（RWE）来增加RCT证据并为药物决策提供信息。虽然RWE可以是随机的或非随机的，但非随机RWE可以利用最近大型医疗保健数据库的激增，并且通常可以回答由于资源限制而无法在随机研究中回答的问题。然而，非随机研究的结果更容易受到混杂偏倚的影响，并且无法完全排除不可测量的混杂因素的存在。此外，非随机研究需要更复杂的设计考虑，有时可能导致设计相关偏倚。我们讨论的问题，可以帮助调查人员或证据消费者评估混淆或其他偏见对他们的研究结果的潜在影响：设计是否模仿一个假设的随机试验设计？比较或对照条件是否适当？主要分析是否调整了测量的混杂因素？敏感性分析是否量化了剩余混杂因素的潜在影响？方法是否开放供检查和（如果可能）复制？设计高质量的非随机药物研究仍然具有挑战性，需要跨一系列学科的广泛专业知识，包括相关临床领域，流行病学和生物统计学。本文提出的问题为评估非随机RWE的可信度提供了一个指导框架，可以应用于许多临床问题。
The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real‐world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design‐related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high‐quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions.
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