Mitigation of biases in estimating hazard ratios under non-sensitive and non-specific observation of outcomes–applications to influenza vaccine effectiveness

摘要

The role of non-sensitive and non-specific observation of incident infection outcomes on the estimation of hazard ratios has thus not been fully covered in previous literature. We here study how outcome measurement errors affect the estimation of vaccine effectiveness based on hazard ratios. We modify the standard partial likelihood under the PH model [8] to adjust for outcome measurement errors in time-to-event data, assuming the sensitivity and the false-positive rate are known. We explore the magnitude of bias when the measurement errors are not corrected for and evaluate the robustness of effectiveness estimates to misspecification of the sensitivity and the false-positive rate. We implement the new method in R [9] (see Additional file 1: R script) and use simulated and Finnish register data to show its performance. Our work is motivated by the Finnish policy of estimating influenza vaccine effectiveness each season from register data [10], which do not include influenza-negative test results and thus do not allow for a retrospective design such as the widely used test-negative design [11]. Therefore, we here focus exclusively on cohort studies.