Zhang Min, Gilbert Peter B
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, 48109, U.S.A.
Stat Commun Infect Dis. 2010 Jan 1;2(1). doi: 10.2202/1948-4690.1002.
Most randomized efficacy trials of interventions to prevent HIV or other infectious diseases have assessed intervention efficacy by a method that either does not incorporate baseline covariates, or that incorporates them in a non-robust or inefficient way. Yet, it has long been known that randomized treatment effects can be assessed with greater efficiency by incorporating baseline covariates that predict the response variable. Tsiatis et al. (2007) and Zhang et al. (2008) advocated a semiparametric efficient approach, based on the theory of Robins et al. (1994), for consistently estimating randomized treatment effects that optimally incorporates predictive baseline covariates, without any parametric assumptions. They stressed the objectivity of the approach, which is achieved by separating the modeling of baseline predictors from the estimation of the treatment effect. While their work adequately justifies implementation of the method for large Phase 3 trials (because its optimality is in terms of asymptotic properties), its performance for intermediate-sized screening Phase 2b efficacy trials, which are increasing in frequency, is unknown. Furthermore, the past work did not consider a right-censored time-to-event endpoint, which is the usual primary endpoint for a prevention trial. For Phase 2b HIV vaccine efficacy trials, we study finite-sample performance of Zhang et al.'s (2008) method for a dichotomous endpoint, and develop and study an adaptation of this method to a discrete right-censored time-to-event endpoint. We show that, given the predictive capacity of baseline covariates collected in real HIV prevention trials, the methods achieve 5-15% gains in efficiency compared to methods in current use. We apply the methods to the first HIV vaccine efficacy trial. This work supports implementation of the discrete failure time method for prevention trials.
大多数预防艾滋病毒或其他传染病干预措施的随机疗效试验，评估干预效果所采用的方法，要么未纳入基线协变量，要么以一种不可靠或低效的方式纳入这些协变量。然而，长期以来人们都知道，通过纳入预测反应变量的基线协变量，可以更有效地评估随机治疗效果。Tsiatis等人（2007年）和Zhang等人（2008年）基于Robins等人（1994年）的理论，倡导了一种半参数有效方法，用于一致地估计随机治疗效果，该方法能以最佳方式纳入预测性基线协变量，且无需任何参数假设。他们强调了该方法的客观性，这是通过将基线预测因子的建模与治疗效果的估计分开来实现的。虽然他们的工作充分证明了该方法在大型3期试验中的实施合理性（因为其最优性是基于渐近性质），但其在频率不断增加的中等规模筛选2b期疗效试验中的表现尚不清楚。此外，过去的工作没有考虑右删失的事件发生时间终点，而这是预防试验通常的主要终点。对于2b期艾滋病毒疫苗疗效试验，我们研究了Zhang等人（2008年）的方法对于二分终点的有限样本性能，并开发并研究了该方法对离散右删失事件发生时间终点的一种改编。我们表明，鉴于在实际艾滋病毒预防试验中收集的基线协变量的预测能力，与当前使用的方法相比，这些方法在效率上提高了5% - 15%。我们将这些方法应用于首个艾滋病毒疫苗疗效试验。这项工作支持在预防试验中实施离散失败时间方法。
