Abstract
BackgroundNon-sensitive and non-specific observation of outcomes in time-to-event data affects event counts as well as the risk sets, thus, biasing the estimation of hazard ratios. We investigate how imperfect observation of incident events affects the estimation of vaccine effectiveness based on hazard ratios.MethodsImperfect time-to-event data contain two classes of events: a portion of the true events of interest; and false-positive events mistakenly recorded as events of interest. We develop an estimation method utilising a weighted partial likelihood and probabilistic deletion of false-positive events and assuming the sensitivity and the false-positive rate are known. The performance of the method is evaluated using simulated and Finnish register data.ResultsThe novel method enables unbiased semiparametric estimation of hazard ratios from imperfect time-to-event data. False-positive rates that are small can be approximated to be zero without inducing bias. The method is robust to misspecification of the sensitivity as long as the ratio of the sensitivity in the vaccinated and the unvaccinated is specified correctly and the cumulative risk of the true event is small.ConclusionsThe weighted partial likelihood can be used to adjust for outcome measurement errors in the estimation of hazard ratios and effectiveness but requires specifying the sensitivity and the false-positive rate. In absence of exact information about these parameters, the method works as a tool for assessing the potential magnitude of bias given a range of likely parameter values.
Highlights
Outcome measurement errors are common in epidemiological studies and may bias the estimated effects of exposures or interventions on health outcomes
Yang et al [5] addressed estimation of vaccine effectiveness under non-specific observation of influenza infection using a subset of acute respiratory infections as a validation set on disease aetiology
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
Summary
True and false‐positive events We consider the sensitivity of outcome measurement as the conditional probability for the true event of interest being recorded in the data. Mean of the vaccine effectiveness estimates ( VE ), mean of th√e standard error estimates ( SE ), standard error of the vaccine effectiveness estimates ( SEVE ), rootmean-squared error of the vaccine effectiveness estimates ( MSEVE ), bias in percentage points, and empirical coverage probability (Cov) of the 95% confidence intervals when estimating vaccine effectiveness from 104 repeated data sets under non-differential sensitivity ( se0 = se1 ) of 0.04 and a cumulative risk of 0.81 in the unvaccinated in absence of false-positive events.
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