Channeling Bias in the Analysis of Risk of Myocardial Infarction, Stroke, Gastrointestinal Bleeding, and Acute Renal Failure with the Use of Paracetamol Compared with Ibuprofen

Rachel B. Weinstein (), Patrick B. Ryan, Jesse A. Berlin, Martijn J. Schuemie, Joel Swerdel and Daniel Fife
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Rachel B. Weinstein: Janssen Research and Development, LLC
Patrick B. Ryan: Janssen Research and Development, LLC
Jesse A. Berlin: Johnson and Johnson
Martijn J. Schuemie: Janssen Research and Development, LLC
Joel Swerdel: Janssen Research and Development, LLC
Daniel Fife: Janssen Research and Development, LLC

Drug Safety, 2020, vol. 43, issue 9, No 9, 927-942

Abstract: Abstract Introduction Observational studies estimating severe outcomes for paracetamol versus ibuprofen use have acknowledged the specific challenge of channeling bias. A previous study relying on negative controls suggested that using large-scale propensity score (LSPS) matching may mitigate bias better than models using limited lists of covariates. Objective The aim was to assess whether using LSPS matching would enable the evaluation of paracetamol, compared to ibuprofen, and increased risk of myocardial infarction, stroke, gastrointestinal (GI) bleeding, or acute renal failure. Study design and setting In a new-user cohort study, we used two propensity score model strategies for confounder controls. One replicated the approach of controlling for a hand-picked list. The second used LSPSs based on all available covariates for matching. Positive and negative controls assessed residual confounding and calibrated confidence intervals. The data source was the Clinical Practices Research Datalink (CPRD). Results A substantial proportion of negative controls were statistically significant after propensity score matching on the publication covariates, indicating considerable systematic error. LSPS adjustment was less biased, but residual error remained. The calibrated estimates resulted in very wide confidence intervals, indicating large uncertainty in effect estimates once residual error was incorporated. Conclusions For paracetamol versus ibuprofen, when using LSPS methods in the CPRD, it is only possible to distinguish true effects if those effects are large (hazard ratio > 2). Due to their smaller hazard ratios, the outcomes under study cannot be differentiated from null effects (represented by negative controls) even if there were a true effect. Based on these data, we conclude that we are unable to determine whether paracetamol is associated with an increased risk of myocardial infarction, stroke, GI bleeding, and acute renal failure compared to ibuprofen, due to residual confounding.

Date: 2020
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DOI: 10.1007/s40264-020-00950-3

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