Biostatistics Research Branch, National Institute of Allergy and Infectious Disease, Bethesda, Maryland, USA.
Vaccines and Infectious Diseases Division, Fred Hutch Cancer Research Center, Seattle, Washington, USA.
Stat Med. 2022 Jul 20;41(16):3076-3089. doi: 10.1002/sim.9405. Epub 2022 Apr 8.
SARS-CoV-2 continues to evolve and the vaccine efficacy against variants is challenging to estimate. It is now common in phase III vaccine trials to provide vaccine to those randomized to placebo once efficacy has been demonstrated, precluding a direct assessment of placebo controlled vaccine efficacy after placebo vaccination. In this work, we extend methods developed for estimating vaccine efficacy post placebo vaccination to allow variant specific time varying vaccine efficacy, where time is measured since vaccination. The key idea is to infer counterfactual strain specific placebo case counts by using surveillance data that provide the proportions of the different strains. This blending of clinical trial and observational data allows estimation of strain-specific time varying vaccine efficacy, or sieve effects, including for strains that emerge after placebo vaccination. The key requirements are that the surveillance strain distribution accurately reflects the strain distribution for a placebo group throughout follow-up after placebo group vaccination, and that at least one strain is present before and after placebo vaccination. For illustration, we develop a Poisson approach for an idealized design under a rare disease assumption and then use a proportional hazards model to address staggered entry, staggered crossover, and smoothly varying strain specific vaccine efficacy. We evaluate these methods by theoretical work and simulations, and demonstrate that useful estimation of the efficacy profile is possible for strains that emerge after vaccination of the placebo group. An important principle is to incorporate sensitivity analyses to guard against misspecification of the strain distribution.
SARS-CoV-2 仍在不断进化，针对变异体的疫苗效力难以估计。目前，在 III 期疫苗试验中，一旦证明疗效，就会向随机分配到安慰剂的人群提供疫苗，从而无法直接评估安慰剂接种后的安慰剂对照疫苗效力。在这项工作中，我们扩展了用于估计安慰剂接种后疫苗效力的方法，以允许针对特定变异体的时间变化的疫苗效力，其中时间是自接种以来测量的。关键思想是通过使用提供不同菌株比例的监测数据来推断假设的特定菌株的安慰剂病例数。这种临床试验和观察数据的混合允许估计特定菌株的时间变化的疫苗效力，或筛选效果，包括在安慰剂接种后出现的菌株。关键要求是，监测菌株分布在安慰剂组接种后整个随访期间准确反映安慰剂组的菌株分布，并且至少有一种菌株在安慰剂接种前后存在。为了说明这一点，我们在罕见疾病假设下为理想设计开发了泊松方法，然后使用比例风险模型来解决交错进入、交错交叉和逐渐变化的特定菌株疫苗效力。我们通过理论工作和模拟评估这些方法，并证明对于在安慰剂组接种后出现的菌株，有可能对疗效曲线进行有用的估计。一个重要原则是进行敏感性分析，以防止菌株分布的错误指定。
