ABSTRACT

Re-randomization inference is used as an alternative approach to more traditional statistical methods. Free from parametric assumptions, its use is particularly suited for studies incorporating covariate-adaptive randomization, and can provide additional evaluation and confirmation of inferences drawn from the original analyses, e.g., as a sensitivity analysis. We discuss methodological and computational aspects in the context of a Poisson regression and describe an approach to re-randomization inference. This is tested in a simulation study and then illustrated in a case study in which we evaluate vaccine efficacy data from a previously published influenza vaccine study. Our simulations indicate that re-randomization inference corrects for model misspecification. The case study, which accounted for the minimization factors, shows that the P-value and confidence limits from re-randomization inference agree with the original analysis. In conclusion re-randomization inference is a useful method that can be utilized to support vaccine clinical development.

摘要

重新随机化推断被用作更传统统计方法的替代方法。不受参数假设的影响，它的使用特别适合于纳入协变量自适应随机化的研究，并且可以对从原始分析中得出的推论进行额外的评估和确认，例如作为敏感性分析。我们在泊松回归的背景下讨论方法和计算方面，并描述重新随机化推理的方法。这是在模拟研究中进行测试，然后在案例研究中进行说明，在案例研究中我们评估了先前发表的流感疫苗研究中的疫苗功效数据。我们的模拟表明，重新随机化推断可以纠正模型错误指定。案例研究考虑了最小化因素，显示重新随机化推断的 P 值和置信限与原始分析一致。总之，重新随机化推断是一种可用于支持疫苗临床开发的有用方法。