Sensitivity analyses of unmeasured and partially-measured confounders using multiple imputation in a vaccine safety study.

Abstract

Sensitivity analyses have played an important role in pharmacoepidemiology studies using electronic health records data. Despite the existence of quantitative bias analysis in pharmacoepidemiologic studies, simultaneously adjusting for unmeasured and partially measured confounders is challenging in vaccine safety studies. Our objective was to develop a flexible approach for conducting sensitivity analyses of unmeasured and partially-measured confounders concurrently for a vaccine safety study. We derived conditional probabilities for an unmeasured confounder based on bias parameters, used these conditional probabilities and Monte Carlo simulations to impute the unmeasured confounder, and re-constructed the analytic datasets as if the unmeasured confounder had been observed. We simultaneously imputed a partially measured confounder using a prediction model. We considered unmeasured breastfeeding and partially measured family history of Type 1 diabetes (T1DM) in a study examining the association between exposure to rotavirus vaccination and T1DM. Before sensitivity analyses, the hazard ratios (HR) were 1.50 (95% CI, 0.81-2.77) for those partially exposed and 1.03 (95% CI, 0.62-1.72) for those fully exposed with unexposed children as the referent group. When breastfeeding and family history of T1DM were adjusted, the HR was 1.55 (95% CI, 0.84-2.87) for the partially exposed group; the HR was 0.98 (95% CI, 0.58-1.63) for the fully exposed group. We conclude that adjusting for unmeasured breastfeeding and partially measured family history of T1DM did not alter the conclusion that there was no evidence of association between rotavirus vaccination and developing T1DM. This novel approach allows for simultaneous adjustment for multiple unmeasured and partially-measured confounders.