In clinical trials randomizing participants to active vs. control conditions and following study units until the occurrence of a primary clinical endpoint, evaluating the efficacy of a quantitative exposure (e.g., drug dosage, drug-induced biomarker activity) is often challenging, as statistical innovations in causal inference have historically focused on estimands compatible only with binary or categorical exposures. Stochastic-interventional effects, which measure the causal effect attributable to perturbing the exposure’s natural (i.e., observed) value, provide an interpretable solution. Unfortunately, their use in vaccine efficacy trials requires extra care, for such trials measure immunologic biomarkers – useful for understanding the mechanisms by which vaccines confer protection or as surrogate endpoints in future trials – via outcome-dependent two-phase sampling (e.g., case-cohort) designs. These biased, outcome-dependent sampling designs have earned their popularity: they circumvent the administrative burden of collecting potentially expensive biomarker measurements on all study units without limiting opportunities to detect important biomarkers that may be mechanistically informative of the disease or infection process. We outline a semiparametric biased sampling correction that allows for asymptotically efficient inference on a causal vaccine efficacy measure defined by contrasting assignments of study units to active vs. control while simultaneously hypothetically shifting biomarker expression in the active condition, yielding a causal dose-response analysis informative of next-generation vaccine efficacy and useful for transporting efficacy from a source pathogen strain (e.g., SARS-CoV-2 at outbreak) to variants of concern (e.g., Omicron BA.4/BA.5). We present the results of applying this approach in an analysis of the U.S. Government / COVID-19 Prevention Network’s COVE (Moderna) COVID-19 vaccine efficacy clinical trial.