Evaluating Treatment Efficacy in Clinical Trials with Two-phase Designs Using Stochastic-interventional Causal Effects


In clinical trials randomizing participants to active vs. control conditions and following units until the occurrence of a primary clinical endpoint, evaluating the efficacy of a quantitative treatment (e.g., drug dosage) is often difficult. Stochastic-interventional effects, which measure the causal effect of perturbing the treatment’s observed value, provide an interpretable solution; yet, their use in vaccine trials requires care, for such trials measure immunologic biomarkers – useful for understanding the mechanisms by which vaccines confer protection or as surrogate endpoints – via outcome-dependent two-phase sampling (e.g., case-cohort) designs. These biased sampling designs have earned their popularity: they circumvent the economic burden of measuring biomarkers on all study units without limiting opportunities to detect mechanistically informative biomarkers. We discuss a semiparametric biased sampling correction allowing for asymptotically efficient inference on a causal vaccine efficacy measure, defined by contrasting assignments of study units to active vs. control while also shifting observed biomarker expression in the active condition, yielding a causal dose-response analysis informative of next-generation vaccine efficacy and of transporting efficacy from a source pathogen strain (e.g., SARS-CoV-2 at outbreak) to variants of concern (e.g., Omicron BA.5). We present the results of applying this approach in the Moderna COVE COVID-19 vaccine efficacy trial.

Mon, Dec 19, 2022 4:20 PM
London, England, UK
Nima Hejazi
Nima Hejazi
Assistant Professor of Biostatistics

My research lies at the intersection of causal inference and machine learning, developing flexible methodology for statistical inference tailored to modern experiments and observational studies in the biomedical and public health sciences.