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Core concepts in pharmacoepidemiology: quantitative bias analysis
  • +8
  • Jeremy Brown,
  • Jacob Hunnicutt N,
  • Sanni Ali M,
  • Krishnan Bhaskaran,
  • Ashley Cole,
  • Sinead Langan,
  • Dorothea Nitsch,
  • Christopher Rentsch,
  • Nicholas Galwey,
  • Kevin Wing,
  • Ian Douglas
Jeremy Brown
Harvard T H Chan School of Public Health Department of Epidemiology

Corresponding Author:[email protected]

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Jacob Hunnicutt N
GlaxoSmithKline USA Collegeville
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Sanni Ali M
London School of Hygiene & Tropical Medicine Department of Non-communicable Disease Epidemiology
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Krishnan Bhaskaran
London School of Hygiene & Tropical Medicine Department of Non-communicable Disease Epidemiology
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Ashley Cole
GlaxoSmithKline USA Collegeville
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Sinead Langan
London School of Hygiene & Tropical Medicine Department of Non-communicable Disease Epidemiology
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Dorothea Nitsch
London School of Hygiene & Tropical Medicine Department of Non-communicable Disease Epidemiology
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Christopher Rentsch
London School of Hygiene & Tropical Medicine Department of Non-communicable Disease Epidemiology
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Nicholas Galwey
GSK plc
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Kevin Wing
London School of Hygiene & Tropical Medicine Department of Non-communicable Disease Epidemiology
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Ian Douglas
London School of Hygiene & Tropical Medicine Department of Non-communicable Disease Epidemiology
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Abstract

Pharmacoepidemiological studies provide important information on the safety and effectiveness of medications, but the validity of study findings can be threatened by residual bias. Ideally, biases would be minimised through appropriate study design and statistical analysis methods. However, residual biases can remain, for example due to unmeasured confounders, measurement error, or selection into the study. A group of sensitivity analysis methods, termed quantitative bias analyses, are available to assess, quantitatively and transparently, the robustness of study results to these residual biases. These approaches include methods to quantify how the estimated effect would be altered under specified assumptions about the potential bias, and methods to calculate bounds on effect estimates. This article introduces quantitative bias analyses for unmeasured confounding, misclassification, and selection bias, with a focus on their relevance and application to pharmacoepidemiological studies.
Submitted to Pharmacoepidemiology and Drug Safety
17 Jun 2024Review(s) Completed, Editorial Evaluation Pending
03 Jul 2024Editorial Decision: Revise Minor
14 Aug 20241st Revision Received
14 Aug 2024Review(s) Completed, Editorial Evaluation Pending
14 Aug 2024Submission Checks Completed
14 Aug 2024Assigned to Editor
16 Aug 2024Reviewer(s) Assigned
16 Sep 2024Editorial Decision: Accept