Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments.
Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments.
复制标题
对糖尿病治疗的临床结果进行真实世界的证据研究。
DOI：
10.1210/endrev/bnab007
复制
发表时间：
2021-09-28
影响因子：
20.3
通讯作者：
Patorno E
中科院分区：
文献类型：
--
作者：
Schneeweiss S;Patorno E
真实世界证据（RWE）是对医疗保健系统常规操作中纵向患者水平数据产生的临床实践中治疗有效性的理解，被认为是对随机对照试验（RCT）中药物疗效证据的补充。RWE研究遵循结构化方法。(1)设计层决定研究设计，研究设计由研究问题驱动，并由医学知情的目标人群、患者知情的结局和生物学知情的效应窗口进行细化。在设计类似的RWE研究之前，想象一下我们理想中进行的随机试验会减少偏倚;新用户活性对照队列设计已被证明在许多糖尿病治疗的RWE研究中有用。(2)测量层将纵向患者水平数据流转换为识别研究人群、暴露前患者特征、治疗和治疗后结局的变量。与我们在大多数RCT中发现的主要数据收集相比，使用次要数据增加了测量的复杂性。(3)分析层侧重于因果治疗效果估计。倾向评分分析已越来越受欢迎，以尽量减少医疗保健数据库分析中的混淆。应避免众所周知的研究者错误，如永恒的时间偏差、调整因果中间体或反向因果关系。为了提高RWE研究结果的可重复性，研究需要完全的实施透明度。本文整合了关于如何进行和审查糖尿病治疗RWE研究的最新知识，以最大限度地提高研究有效性，并最终增加对基于RWE的决策的信心。
Real-world evidence (RWE), the understanding of treatment effectiveness in clinical practice generated from longitudinal patient-level data from the routine operation of the healthcare system, is thought to complement evidence on the efficacy of medications from randomized controlled trials (RCTs). RWE studies follow a structured approach. (1) A design layer decides on the study design, which is driven by the study question and refined by a medically informed target population, patient-informed outcomes, and biologically informed effect windows. Imagining the randomized trial we would ideally perform before designing an RWE study in its likeness reduces bias; the new-user active comparator cohort design has proven useful in many RWE studies of diabetes treatments. (2) A measurement layer transforms the longitudinal patient-level data stream into variables that identify the study population, the pre-exposure patient characteristics, the treatment, and the treatment-emergent outcomes. Working with secondary data increases the measurement complexity compared to primary data collection that we find in most RCTs. (3) An analysis layer focuses on the causal treatment effect estimation. Propensity score analyses have gained in popularity to minimize confounding in healthcare database analyses. Well-understood investigator errors, like immortal time bias, adjustment for causal intermediates, or reverse causation, should be avoided. To increase reproducibility of RWE findings, studies require full implementation transparency. This article integrates state-of-the-art knowledge on how to conduct and review RWE studies on diabetes treatments to maximize study validity and ultimately increased confidence in RWE-based decision making.
登录
查看更多内容
影响因子：
4
作者：
Bobo WV;Cooper WO;Epstein RA Jr;Arbogast PG;Mounsey J;Ray WA
通讯作者：
Ray WA
影响因子：
39
作者：
Faillie, Jean-Luc;Yu, Oriana H.;Azoulay, Laurent
通讯作者：
Azoulay, Laurent
影响因子：
3
作者：
Brookhart MA;Stürmer T;Glynn RJ;Rassen J;Schneeweiss S
通讯作者：
Schneeweiss S
影响因子：
5.4
作者：
Brookhart, MA;Wang, PS;Schneeweiss, S
通讯作者：
Schneeweiss, S
影响因子：
7.7
作者：
Farmer, Ruth E.;Ford, Deborah;Bhaskaran, Krishnan
通讯作者：
Bhaskaran, Krishnan