The aim of this manuscript is to explore semiparametric methods for inferring subgroup-specific relative vaccine efficacy in a partially vaccinated population against multiple strains of a virus. We consider methods for observational case-only studies with informative missingness in viral strain type due to vaccination status, pre-vaccination variables, and also post-vaccination factors such as viral load. We establish general causal conditions under which the relative conditional vaccine efficacy between strains can be identified nonparametrically from the observed data-generating distribution. Assuming that the relative strain-specific conditional vaccine efficacy has a known parametric form, we propose semiparametric asymptotically linear estimators of the parameters based on targeted (debiased) machine learning estimators for partially linear logistic regression models. Finally, we apply our methods to estimate the relative strain-specific conditional vaccine efficacy in the ENSEMBLE COVID-19 vaccine trial.

翻译：本文旨在探索在部分接种人群中，针对病毒多种毒株的亚组特异性相对疫苗效力的半参数推断方法。我们针对观察性病例独有研究提出了一种方法，其中病毒株类型因疫苗接种状态、接种前变量及接种后因素（如病毒载量）存在信息缺失。我们建立了普适的因果条件，在此条件下，可以从观测数据生成分布中非参数地识别毒株间相对条件疫苗效力。假设毒株特异性相对条件疫苗效力具有已知参数形式，我们基于半线性逻辑回归模型的有针对性的（去偏）机器学习估计量，提出了参数的半参数渐近线性估计量。最后，我们将方法应用于ENSEMBLE COVID-19疫苗试验，估计毒株特异性条件相对疫苗效力。