Using probability of drug use as independent variable in a register‐based pharmacoepidemiological cause‐effect study—An application of the reverse waiting time distribution. Issue 12 (12th October 2017)

• Record Type: Journal Article
• Authors: Hallas, Jesper; Pottegård, Anton; Støvring, Henrik
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 26:Issue 12(2017)
• Page Start: 1520
• Page End: 1526
• Publication Date: 2017-10-12
• Subjects: databases -- exposure -- misclassification -- NSAID -- pharmacoepidemiology -- upper gastrointestinal bleeding; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.4326
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: Background: In register‐based pharmacoepidemiological studies, each day of follow‐up is usually categorized either as exposed or unexposed. However, there is an underlying continuous probability of exposure, and by insisting on a dichotomy, researchers unwillingly force a nondifferential misclassification into their analyses. We have recently developed a model whereby probability of exposure can be modeled, and we tested this on an empirical case of nonsteroidal anti‐inflammatory drug (NSAID)‐induced upper gastrointestinal bleeding (UGIB). Methods: We used a case‐controls data set, consisting of 3568 cases of severe UGIB and 35 552 matched controls. Exposure to NSAID was based on 3 different conventional dichotomous measures. In addition, we tested 3 probabilistic exposure measures, a simple univariate backward‐recurrence model, a "full" multivariable model, and a "reduced" multivariable model. Odds ratios (ORs) and 95% confidence intervals for the association between NSAID use and UGIB were calculated by conditional logistic regression, while adjusting for preselected confounders. Results: Compared to the conventional dichotomous exposure measures, the probabilistic exposure measures generated adjusted ORs in the upper range (4.37‐4.75) while at the same time having the most narrow confidence intervals (ratio between upper and lower confidence limit, 1.46‐1.50). Some ORs generated by conventional measures were higher than the probabilistic ORs, but only when the assumed period of intake was unrealistically short. Conclusion: The pattern of high ORs and narrow confidence intervals in probabilistic exposure measures is compatible with less nondifferential misclassification of exposure than in a dichotomous exposure model. Probabilistic exposure measures appear to be an attractive alternative to conventional exposure measures.