Department of Biostatistics, School of Public Health, University of California-Los Angeles, CA 90095-1772, U.S.A.
Stat Med. 2011 Mar 30;30(7):709-17. doi: 10.1002/sim.4131. Epub 2010 Nov 30.
Subgroup analysis arises in clinical trials research when we wish to estimate a treatment effect on a specific subgroup of the population distinguished by baseline characteristics. Many trial designs induce latent subgroups such that subgroup membership is observable in one arm of the trial and unidentified in the other. This occurs, for example, in oncology trials when a biopsy or dissection is performed only on subjects randomized to active treatment. We discuss a general framework to estimate a biological treatment effect on the latent subgroup of interest when the survival outcome is right-censored and can be appropriately modelled as a parametric function of covariate effects. Our framework builds on the application of instrumental variables methods to all-or-none treatment noncompliance. We derive a computational method to estimate model parameters via the EM algorithm and provide guidance on its implementation in standard software packages. The research is illustrated through an analysis of a seminal melanoma trial that proposed a new standard of care for the disease and involved a biopsy that is available only on patients in the treatment arm.
当我们希望估计特定人群亚组的治疗效果时，就会出现亚组分析。这些人群亚组可以通过基线特征来区分。许多临床试验设计会产生潜在的亚组，使得亚组归属在试验的一个臂中是可观察的，而在另一个臂中则无法识别。例如，在肿瘤学试验中，只有对随机分配到活性治疗的受试者进行活检或解剖。当生存结果是右删失的，并且可以适当建模为协变量效果的参数函数时，我们讨论了一种估计潜在亚组中生物学治疗效果的一般框架。我们的框架基于将工具变量方法应用于全或无治疗不合规的情况。我们通过 EM 算法推导出一种用于估计模型参数的计算方法，并提供了在标准软件包中实施的指导。该研究通过对黑色素瘤试验的分析得到了说明，该试验提出了一种新的疾病治疗标准，涉及仅在治疗臂中的患者中进行的活检。
