Estimating vaccine efficacy during open-label follow-up of COVID-19 vaccine trials based on population-level surveillance data

IF 3 3区 医学 Q2 INFECTIOUS DISEASES Epidemics Pub Date : 2024-04-15 DOI:10.1016/j.epidem.2024.100768
Mia Moore , Yifan Zhu , Ian Hirsch , Tom White , Robert C. Reiner , Ryan M. Barber , David Pigott , James K. Collins , Serena Santoni , Magdalena E. Sobieszczyk , Holly Janes
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Abstract

While rapid development and roll out of COVID-19 vaccines is necessary in a pandemic, the process limits the ability of clinical trials to assess longer-term vaccine efficacy. We leveraged COVID-19 surveillance data in the U.S. to evaluate vaccine efficacy in U.S. Government-funded COVID-19 vaccine efficacy trials with a three-step estimation process. First, we used a compartmental epidemiological model informed by county-level surveillance data, a “population model”, to estimate SARS-CoV-2 incidence among the unvaccinated. Second, a “cohort model” was used to adjust the population SARS-CoV-2 incidence to the vaccine trial cohort, taking into account individual participant characteristics and the difference between SARS-CoV-2 infection and COVID-19 disease. Third, we fit a regression model estimating the offset between the cohort-model-based COVID-19 incidence in the unvaccinated with the placebo-group COVID-19 incidence in the trial during blinded follow-up. Counterfactual placebo COVID-19 incidence was estimated during open-label follow-up by adjusting the cohort-model-based incidence rate by the estimated offset. Vaccine efficacy during open-label follow-up was estimated by contrasting the vaccine group COVID-19 incidence with the counterfactual placebo COVID-19 incidence. We documented good performance of the methodology in a simulation study. We also applied the methodology to estimate vaccine efficacy for the two-dose AZD1222 COVID-19 vaccine using data from the phase 3 U.S. trial (ClinicalTrials.gov # NCT04516746). We estimated AZD1222 vaccine efficacy of 59.1% (95% uncertainty interval (UI): 40.4%–74.3%) in April, 2021 (mean 106 days post-second dose), which reduced to 35.7% (95% UI: 15.0%–51.7%) in July, 2021 (mean 198 days post-second-dose). We developed and evaluated a methodology for estimating longer-term vaccine efficacy. This methodology could be applied to estimating counterfactual placebo incidence for future placebo-controlled vaccine efficacy trials of emerging pathogens with early termination of blinded follow-up, to active-controlled or uncontrolled COVID-19 vaccine efficacy trials, and to other clinical endpoints influenced by vaccination.

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根据人群监测数据估算 COVID-19 疫苗试验开放标签跟踪期间的疫苗效力
虽然 COVID-19 疫苗的快速开发和推广在大流行中是必要的,但这一过程限制了临床试验评估疫苗长期疗效的能力。我们利用美国的 COVID-19 监测数据来评估美国政府资助的 COVID-19 疫苗疗效试验中的疫苗疗效,评估过程分为三个步骤。首先,我们根据县级监测数据(即 "人群模型")使用分区流行病学模型来估计未接种者的 SARS-CoV-2 感染率。其次,我们使用 "队列模型 "将 SARS-CoV-2 群体发病率调整为疫苗试验队列发病率,同时考虑到参与者的个体特征以及 SARS-CoV-2 感染与 COVID-19 疾病之间的差异。第三,我们拟合了一个回归模型,以估计在盲法随访期间,基于队列模型的未接种者 COVID-19 发病率与试验中安慰剂组 COVID-19 发病率之间的偏移量。在开放标签随访期间,基于队列模型的 COVID-19 发生率与安慰剂组 COVID-19 发生率之间的偏移量通过估计的偏移量进行调整,从而估算出安慰剂组 COVID-19 的反事实发生率。在开放标签随访期间,通过对比疫苗组 COVID-19 发生率和反事实安慰剂 COVID-19 发生率来估算疫苗疗效。我们在模拟研究中证明了该方法的良好性能。我们还利用美国三期试验(ClinicalTrials.gov # NCT04516746)的数据,应用该方法估算了两剂 AZD1222 COVID-19 疫苗的疗效。我们估计 2021 年 4 月(第二剂后平均 106 天)的 AZD1222 疫苗有效率为 59.1%(95% 不确定区间 (UI):40.4%-74.3%),2021 年 7 月(第二剂后平均 198 天)的有效率降至 35.7%(95% UI:15.0%-51.7%)。我们开发并评估了一种估算长期疫苗效力的方法。该方法可用于估算未来提前终止盲法随访的新病原体安慰剂对照疫苗疗效试验的反事实安慰剂发病率、主动对照或非对照 COVID-19 疫苗疗效试验以及受疫苗接种影响的其他临床终点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Epidemics
Epidemics INFECTIOUS DISEASES-
CiteScore
6.00
自引率
7.90%
发文量
92
审稿时长
140 days
期刊介绍: Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.
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