Gilbert Peter B, Bosch Ronald J, Hudgens Michael G
Fred Hutchinson Cancer Research Center and Department of Biostatistics, University of Washington, Seattle, Washington 98109, USA.
Biometrics. 2003 Sep;59(3):531-41. doi: 10.1111/1541-0420.00063.
Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002, Biometrics 58, 21-29), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.
预防HIV感染能力有限的疫苗，可能通过预防已感染疫苗接种者的二次传播和疾病，对HIV/AIDS大流行产生积极影响。为评估疫苗接种对二次传播和疾病的影响，疗效试验评估疫苗对感染后测量的HIV病毒载量及其他替代终点的作用。一项比较疫苗接种者和安慰剂接种者感染亚组之间病毒载量分布的标准试验，并未评估疫苗的因果效应，因为比较组是在随机分组后选定的。为解决这一问题，我们使用Frangakis和Rubin（2002年，《生物统计学》58卷，21 - 29页）开发的主分层框架，制定临床相关的因果估计量，并提出一类逻辑选择偏倚模型，其成员可识别这些估计量。给定该类中的一个选择模型，我们开发了用于检验和估计疫苗接种对无论随机分组如何都会被感染的受试者主层中病毒载量的因果效应的程序。我们展示了如何将这些程序用于敏感性分析，以量化疫苗接种的因果效应如何随假定的选择偏倚大小而变化。
