Improving the efficiency of infectious disease prevention trials using negative control outcome event times

2026-08-05

AI Summary

This study addresses the limited efficiency of conventional covariate adjustment in infectious disease prevention trials due to unobserved baseline pathogen exposure. The authors introduce, for the first time, a negative control outcome (NCO)—an event time that shares the same exposure mechanism as the primary endpoint but is unaffected by the intervention—and develop an efficient estimation method tailored for doubly right-censored data. Leveraging semiparametric inference, the efficient influence function, and cross-fitting, they propose a one-step estimator that achieves multiple robustness and asymptotic efficiency, while rigorously formalizing its identification assumptions. Applied to the HVTN 704/HPTN 085 antibody-mediated prevention trial, using time to bacterial sexually transmitted infection as the NCO reduces the variance of the HIV prevention efficacy estimate by approximately 27% compared to standard approaches.

Abstract

Baseline covariate adjustment can enhance the efficiency of randomized trials by improving precision of treatment effect estimates. However, the precision gain depends on how strongly the baseline covariates are prognostic for the primary outcome. In randomized trials of infectious disease prevention interventions (e.g., vaccines or passively administered antibodies), an individual's exposure to the pathogen is a leading prognostic factor but is rarely measurable at baseline. Hence, conventional covariate adjustment offers limited precision gain in prevention trials. We propose adjusting for a negative control outcome (NCO) event time, which is causally unaffected by the intervention but shares overlapping exposure mechanisms with the primary outcome. We formalize assumptions under which adjustment for the NCO event time is valid, and show that right-censoring of the NCO event time further complicates adjustment. We derive the efficient influence function for the treatment-arm-specific survivor function of the primary outcome when both the primary outcome and the NCO event time are right-censored, and use it to construct a cross-fitted, one-step estimator that is multiply robust to nuisance misspecification and asymptotically efficient when the nuisances are estimated accurately. In numerical experiments, our estimator compares comparably to benchmarks when the NCO event time is uninformative, and gains precision as the NCO event time is more prognostic for the primary outcome. We apply our method to HVTN 704/HPTN 085, a randomized, double-blinded trial of VRC01, a broadly neutralizing antibody against HIV-1. Adjusting for the time to a bacterial sexually transmitted infection --- a negative control outcome for HIV-1 acquisition --- reduced the estimated variance of the prevention efficacy estimate by approximately 27%, compared to roughly 2.5% for baseline covariate adjustment.

Problem

• infectious disease prevention trials
• treatment effect estimation
• precision gain
• negative control outcome
• baseline covariate adjustment

Innovation

• negative control outcome
• covariate adjustment
• infectious disease prevention trials
• efficient influence function
• multiply robust estimator