Increasing Efficiency and Reducing Bias When Assessing HPV Vaccination Efficacy by Using Nontargeted HPV Strains

Abstract

Studies of vaccine efficacy often record both the incidence of vaccine‐targeted virus strains (primary outcome) and the incidence of nontargeted strains (secondary outcome). However, standard estimates of vaccine efficacy on targeted strains ignore the data on nontargeted strains. Assuming nontargeted strains are unaffected by vaccination, we regard the secondary outcome as a negative control outcome and show how using such data can (i) increase the precision of the estimated vaccine efficacy against targeted strains in randomized trials and (ii) reduce confounding bias of that same estimate in observational studies. For objective (i), we augment the primary outcome estimating equation with a function of the secondary outcome that is unbiased for zero. For objective (ii), we jointly estimate the treatment effects on the primary and secondary outcomes. If the bias induced by the unmeasured confounders is similar for both types of outcomes, as is plausible for factors that influence the general risk of infection, then we can use the estimated efficacy against the secondary outcomes to remove the bias from estimated efficacy against the primary outcome. We demonstrate the utility of these approaches in studies of HPV vaccines that only target a few highly carcinogenic strains. In this example, using nontargeted strains increased precision in randomized trials modestly but reduced bias in observational studies substantially.

Pragmatic considerations for negative control outcome studies to guide non‐randomized comparative analyses: A narrative review

“…13 Studies of NCOs are increasingly used to detect and, in some cases, reduce or remove bias due to uncontrolled confounding, arising as a function of unmeasured or mismeasured confounders. [14][15][16][17] A benefit of randomization is its ability to balance all risk factors between treatment groups (in expectation, as imbalances may still occur by chance). However, in medical practice, patients are prescribed medicines based on clinical indications, and comparative analyses can only expect to achieve balance on measured and controlled confounders.…”

“…Studies of NCOs are increasingly used to detect and, in some cases, reduce or remove bias due to uncontrolled confounding, arising as a function of unmeasured or mismeasured confounders 14–17 . A benefit of randomization is its ability to balance all risk factors between treatment groups (in expectation, as imbalances may still occur by chance).…”