Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Cancer Invest. 2021 Nov;39(10):783-788. doi: 10.1080/07357907.2021.1974030. Epub 2021 Sep 13.
The random allocation of therapies in randomized clinical trials is a powerful tool that removes all confounding biases that can affect treatment assignment. However, confounders influencing mediators of the treatment effect are unaffected by randomization and should be considered during trial design and statistical modeling.Examples of such mediators include biomarkers predictive of response to targeted therapies in oncology. Patient selection for such biomarkers is prudent in clinical trials. Conversely, prognostic information on outcome heterogeneity can be derived from observational datasets that include more representative populations. The fusion of experimental and observational data can then allow patient-specific inferences.
在随机临床试验中，治疗方法的随机分配是一种强大的工具，它可以消除所有可能影响治疗分配的混杂偏差。然而，影响治疗效果中介因素的混杂因素不受随机化的影响，在试验设计和统计建模时应加以考虑。这类中介因素的例子包括肿瘤学中预测靶向治疗反应的生物标志物。在临床试验中，对这类生物标志物进行患者选择是谨慎的。相反，来自包括更具代表性人群的观察性数据集的预后信息可以用来推断结果的异质性。然后，实验数据和观察数据的融合可以允许进行基于患者个体的推断。
