Abstract
BackgroundCase–control studies based on pharmaco-epidemiological databases typically use decision rules to determine exposure status from information on dates of prescription redemptions, although this induces misclassification. The reverse Waiting Time Distribution has been suggested as a likelihood based model to estimate the latent exposure status, and we therefore suggest to extend this into a joint likelihood based model, which incorporates both the latent exposure status and the exposure-outcome association. This will achieve consistency and efficiency of the estimates, i.e. they can be expected to be asymptotically unbiased and have optimal precision.MethodsWe established a joint likelihood for the observed case–control status and last prescription redemption before the index date. The likelihood combines the ordinary logistic regression likelihood and the reverse Waiting Time Distribution, and allows inclusion of covariates in both parts to adjust for observed confounders. We conducted a simulation study of the new model and standard models based on decision rules for exposure and the probability of being exposed, respectively, to assess the relative bias and variability of estimates. Lastly, we applied the models to a case–control study on use of nonsteroidal anti-inflammatory drugs and risk of upper-gastrointestinal bleeding.ResultsIn simulation studies the new model had low relative bias (< 1.4%) and largely retained nominal coverage probabilities (90.2% to 95.1% of nominal 95% confidence intervals), also when moderate misspecification was introduced. All standard methods generally had substantial bias (-21.1% to 17.0%) and low coverage probabilities (0.0% to 68.9%). When analyzing the empirical case–control study, the new method estimated the effect of nonsteroidal anti-inflammatory drugs on risk of with upper-gastrointestinal bleeding hospitalization to 2.52 (1.59 – 3.45), whereas the other methods had estimates ranging from 3.52 (2.19 – 5.65) to 5.17 (2.40 – 11.11).ConclusionsUnlike standard methods, the proposed model gave nearly unbiased estimates with adequate coverage probabilities in simulation studies. The developed model demonstrates the potential for the reverse Waiting Time Distribution to be integrated with existing likelihood based analyses in pharmacoepidemiological studies.
Highlights
Case–control studies based on pharmaco-epidemiological databases typically use decision rules to determine exposure status from information on dates of prescription redemptions, this induces misclassification
We found that the approach improved statistical efficiency when studying the risk of upper gastrointestinal bleeding (UGIB) associated with use of a nonsteroidal anti-inflammatory drug (NSAID) [3]
In a simulation study we compare the performance of the new joint likelihood model with classic decision-rule based analyses, as well as the previous suggestion of a two-step model based on exposure probabilities, and we investigate the impact of misspecification
Summary
Case–control studies based on pharmaco-epidemiological databases typically use decision rules to determine exposure status from information on dates of prescription redemptions, this induces misclassification. Støvring et al BMC Med Res Methodol (2021) 21:144 drug effects in real-world settings In such studies, exposure information is obtained from databases recording all individual prescription redemptions for a population and linked with information on for example hospital admissions for each individual in the population. Since the actual treatment after redeeming a prescription is not recorded, such studies routinely rely on decision rules for defining drug exposure status at the index time for both cases and controls. To avoid using such decision rules, we have previously developed the reverse Waiting Time Distribution (WTD) approach to estimate treatment duration after redeeming a prescription. We used the predicted probability as covariate in a logistic regression with case–control status as outcome
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