Evaluating Observational Data Analyses: Confounding Control and Treatment Effect Heterogeneity [Methods Study], United States, 2013-2019

A randomized trial is one of the best ways to learn if one treatment works better than another. Randomized trials assign patients to different treatments by chance. But they are not always affordable, and they take a long time to complete.
When randomized trials aren't possible, researchers can use observational studies to learn how treatments work. In observational studies, researchers look at what happens when patients and their doctors choose the treatments. Traits such as age or health may affect treatment choices. These traits may also affect patients' responses to treatment, making it hard to know if the treatment or the traits affected the patients' responses.
Some study designs and statistical methods may help address this problem and make results from observational studies more useful. These methods can give researchers more data about whether treatments work and how the same treatment can affect groups of patients differently.
The research team conducted three studies to test different methods of designing and analyzing observational studies. They wanted to know if observational studies that used these methods produced results similar to randomized trials.

随机对照试验（Randomized Trial）是验证某一疗法是否优于另一疗法的最优手段之一，其通过随机方式将患者分配至不同疗法组别，但此类试验往往成本高昂且耗时漫长。
当无法开展随机对照试验时，研究人员可借助观察性研究（Observational Study）探究疗法的实际效果。在观察性研究中，研究人员会观测患者与主治医师自主选择疗法后的临床结局。患者的年龄、健康状况等基线特征既可能影响其疗法选择，也会干扰其对疗法的治疗应答，这使得研究人员难以区分究竟是疗法本身还是患者特征导致了治疗应答的差异。
部分研究设计与统计分析方法可助力解决上述混杂问题，提升观察性研究结果的可靠性与应用价值。此类方法能够为研究人员提供更多数据，以验证疗法的实际效果，并解析同一疗法对不同患者群体产生差异化影响的机制。
本研究团队开展了三项研究，用以测试不同的观察性研究设计与分析方法，旨在验证采用此类方法的观察性研究所得结果是否与随机对照试验的结果一致。