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Brown JP, Hunnicutt JN, Ali MS, Bhaskaran K, Cole A, Langan SM, Nitsch D, Rentsch CT, Galwey NW, Wing K, Douglas IJ. Core Concepts in Pharmacoepidemiology: Quantitative Bias Analysis. Pharmacoepidemiol Drug Saf 2024; 33:e70026. [PMID: 39375940 DOI: 10.1002/pds.70026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/13/2024] [Accepted: 09/16/2024] [Indexed: 10/09/2024]
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 minimized 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.
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Affiliation(s)
- Jeremy P Brown
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Jacob N Hunnicutt
- Epidemiology, Value Evidence and Outcomes, R&D Global Medical, GSK plc, Collegeville, Pennsylvania, USA
| | - M Sanni Ali
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Krishnan Bhaskaran
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Ashley Cole
- Real-World Analytics, Value Evidence and Outcomes, R&D Global Medical, GSK plc, Collegeville, Pennsylvania, USA
| | - Sinead M Langan
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Christopher T Rentsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Kevin Wing
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Ian J Douglas
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
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Greenland S. Invited Commentary: Dealing With the Inevitable Deficiencies of Bias Analysis-and All Analyses. Am J Epidemiol 2021; 190:1617-1621. [PMID: 33778862 DOI: 10.1093/aje/kwab069] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 01/26/2021] [Accepted: 02/10/2021] [Indexed: 12/22/2022] Open
Abstract
Lash et al. (Am J Epidemiol. 2021;190(8):1604-1612) have presented detailed critiques of 3 bias analyses that they identify as "suboptimal." This identification raises the question of what "optimal" means for bias analysis, because it is practically impossible to do statistically optimal analyses of typical population studies-with or without bias analysis. At best the analysis can only attempt to satisfy practice guidelines and account for available information both within and outside the study. One should not expect a full accounting for all sources of uncertainty; hence, interval estimates and distributions for causal effects should never be treated as valid uncertainty assessments-they are instead only example analyses that follow from collections of often questionable assumptions. These observations reinforce those of Lash et al. and point to the need for more development of methods for judging bias-parameter distributions and utilization of available information.
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