Literature DB >> 33454352

Quantifying bias in epidemiologic studies evaluating the association between acetaminophen use and cancer.

Martijn J Schuemie1, Rachel Weinstein2, Patrick B Ryan2, Jesse A Berlin3.   

Abstract

Many observational studies explore the association between acetaminophen and cancer, but known limitations such as vulnerability to channeling, protopathic bias, and uncontrolled confounding hamper the interpretability of results. To help understand the potential magnitude of bias, we identify key design choices in these observational studies and specify 10 study design variants that represent different combinations of these design choices. We evaluate these variants by applying them to 37 negative controls - outcome presumed not to be caused by acetaminophen - as well as 4 cancer outcomes in the Clinical Practice Research Datalink (CPRD) database. The estimated odds and hazards ratios for the negative controls show substantial bias in the evaluated design variants, with far fewer of the 95% confidence intervals containing 1 than the nominal 95% expected for negative controls. The effect-size estimates for the cancer outcomes are comparable to those observed for the negative controls. A comparison of exposed and unexposed reveals many differences at baseline for which most studies do not correct. We observe that the design choices made in many of the published observational studies can lead to substantial bias. Thus, caution in the interpretation of published studies of acetaminophen and cancer is recommended.
Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Acetaminophen; Bias; Cancer; Negative controls

Year:  2021        PMID: 33454352     DOI: 10.1016/j.yrtph.2021.104866

Source DB:  PubMed          Journal:  Regul Toxicol Pharmacol        ISSN: 0273-2300            Impact factor:   3.271


  2 in total

1.  Comments on "DNA-binding activities of compounds acting as enzyme inhibitors, ion channel blockers and receptor binders."

Authors:  Hartmut Jaeschke
Journal:  Chem Biol Interact       Date:  2021-11-26       Impact factor: 5.192

Review 2.  OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review.

Authors:  Najia Ahmadi; Yuan Peng; Markus Wolfien; Michéle Zoch; Martin Sedlmayr
Journal:  Int J Mol Sci       Date:  2022-10-05       Impact factor: 6.208

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.