2026/05/31 by Lola Etievant, Lola Étiévant
Immunology and Microbiology · Mathematics · Medicine · #Advanced Causal Inference Techniques #Cervical Cancer and HPV Research #Reproductive tract infections research #stat.ME
paper · pdf · doi:10.1515/ijb-2025-0087
Accepted in the International Journal of Biostatistics, 2026
openalex publication_date 2026/07/16 · openalex created_date 2026/07/17 · openalex updated_date 2026/07/25 · arxiv created 2026/07/30 · arxiv updated 2026/07/31
Studies of HPV vaccine efficacy usually record infections with vaccine targeted and non-targeted strains. Contrary to blinded randomized controlled trials, confounding bias can be a threat and risk compensation may occur in observational studies. Etievant et al. (Biometrics, 2023) proposed to use cervical infections with non-targeted HPV strains to remove or reduce confounding bias in estimates of vaccine efficacy on targeted strains. However, they assumed that vaccinated women could not change their behavior after vaccination. This work investigates if the quantity estimated in practice with the method of Etievant et al. has a clear causal meaning under a more plausible setting where unmeasured sexual behavior acts as both a confounder and a mediator. Under certain assumptions, using non-targeted HPV infections can remove both confounding bias and the portion of the vaccine effect on the targeted HPV strains that is mediated through the change of behavior. In that case, the estimated quantity has a clear causal interpretation as it represents the direct immunological effect of the vaccine. Infections with non-targeted HPV strains could also be used to isolate the indirect behavioral effect of the vaccine in unblinded randomized controlled trial.