美国哈佛公共卫生学院Barbra A. Dickerman研究组以他汀类药物和癌症的应用发现为例，总结了观察性分析中可避免的缺陷。这一成果2019年10月7日在线发表于《自然—医学》。
使用733804名英国成年人的电子健康记录，他们模拟了他汀类药物和癌症的目标试验，并将他们的估计值与使用先前应用的分析方法获得的估计值进行了比较。在10年的随访中，有28408个人患了癌症。在目标试验方法下，意向性治疗和按方案治疗10年无癌生存率的估计观察结果别为-0.5%（95%置信区间（CI）-1.0%，0.0%）和-0.3%（95%CI -1.5%，0.5%）。相比之下，以前的分析方法得出的估计似乎具有很强的保护性。他们的发现凸显了明确模拟目标试验的重要性，以减少观察性分析得出的效果评估中的偏倚。
据介绍，大型医疗数据库的可用性不断提高，引发了关于真实数据是否应在评估医疗收益风险中发挥作用的激烈辩论。例如，在许多观察性研究中，发现他汀类药物使用者的癌症风险远低于随机试验的荟萃分析。尽管这种差异通常归因于观察性研究的缺乏随机性，但它们可能是由可以通过明确模拟目标试验（可以回答感兴趣的问题的随机试验）而避免的缺陷来解释的。
附：英文原文
Title: Avoidable flaws in observational analyses: an application to statins and cancer
Author: Barbra A. Dickerman, Xabier Garca-Albniz, Roger W. Logan, Spiros Denaxas, Miguel A. Hernn
Issue&Volume: 2019-10-07
Abstract:
The increasing availability of large healthcare databases is fueling an intense debate on whether real-world data should play a role in the assessment of the benefit–risk of medical treatments. In many observational studies, for example, statin users were found to have a substantially lower risk of cancer than in meta-analyses of randomized trials. Although such discrepancies are often attributed to a lack of randomization in the observational studies, they might be explained by flaws that can be avoided by explicitly emulating a target trial (the randomized trial that would answer the question of interest). Using the electronic health records of 733,804 UK adults, we emulated a target trial of statins and cancer and compared our estimates with those obtained using previously applied analytic approaches. Over the 10-yr follow-up, 28,408 individuals developed cancer. Under the target trial approach, estimated observational analogs of intention-to-treat and per-protocol 10-yr cancer-free survival differences were −0.5% (95% confidence interval (CI) −1.0%, 0.0%) and −0.3% (95% CI −1.5%, 0.5%), respectively. By contrast, previous analytic approaches yielded estimates that appeared to be strongly protective. Our findings highlight the importance of explicitly emulating a target trial to reduce bias in the effect estimates derived from observational analyses.
DOI: 10.1038/s41591-019-0597-x
Source:https://www.nature.com/articles/s41591-019-0597-x
Nature Medicine：《自然—医学》，创刊于1995年。隶属于施普林格·自然出版集团，最新IF：87.241
官方网址：https://www.nature.com/nm/
投稿链接：https://mts-nmed.nature.com/cgi-bin/main.plex
本期文章：《自然—医学》：Online/在线发表
