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de Jong VMT, Campbell H, Maxwell L, Jaenisch T, Gustafson P, Debray TPA. Adjusting for misclassification of an exposure in an individual participant data meta-analysis. Res Synth Methods 2023; 14:193-210. [PMID: 36200133 DOI: 10.1002/jrsm.1606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 08/02/2022] [Accepted: 08/28/2022] [Indexed: 11/08/2022]
Abstract
A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate unbiased estimation of adjusted and unadjusted exposure-outcome associations and between-study heterogeneity in IPD-MA, where the extent and nature of exposure misclassification may vary across studies. We present Bayesian methods that allow misclassification of binary exposure variables to depend on study- and participant-level characteristics. In an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable was unavailable for some studies which only measured a surrogate prone to misclassification, our methods yielded more accurate estimates than analyses naive with regard to misclassification or based on gold standard measurements alone. In a simulation study, the evaluated misclassification model yielded valid estimates of the exposure-outcome association, and was more accurate than analyses restricted to gold standard measurements. Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD-MA. It requires that some studies supply IPD for the surrogate and gold standard exposure, and allows misclassification to follow a random effects distribution across studies conditional on observed covariates (and outcome). The proposed methods are most beneficial when few large studies that measured the gold standard are available, and when misclassification is frequent.
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Affiliation(s)
- Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Data Analytics and Methods Task Force, European Medicines Agency, Amsterdam, The Netherlands
| | - Harlan Campbell
- Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Lauren Maxwell
- Heidelberg Institute of Global Health, Heidelberg Medical School, Heidelberg University, Heidelberg, Germany
| | - Thomas Jaenisch
- Heidelberg Institute of Global Health, Heidelberg Medical School, Heidelberg University, Heidelberg, Germany.,Center for Global Health, Colorado School of Public Health, Aurora, Colorado, USA.,Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, USA
| | - Paul Gustafson
- Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Levenson M, He W, Chen L, Dharmarajan S, Izem R, Meng Z, Pang H, Rockhold F. Statistical consideration for fit-for-use real-world data to support regulatory decision making in drug development. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2120533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
| | - Weili He
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | - Li Chen
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | | | - Rima Izem
- Novartis Institutes for BioMedical Research Basel, Basel, Basel-Stadt, CH
| | | | | | - Frank Rockhold
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC
- Duke Clinical Research Institute, Duke University, Durham, NC
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Campbell H, de Jong VMT, Maxwell L, Jaenisch T, Debray TPA, Gustafson P. Measurement error in meta-analysis (MEMA)-A Bayesian framework for continuous outcome data subject to non-differential measurement error. Res Synth Methods 2021; 12:796-815. [PMID: 34312994 DOI: 10.1002/jrsm.1515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 06/16/2021] [Accepted: 06/18/2021] [Indexed: 11/11/2022]
Abstract
Ideally, a meta-analysis will summarize data from several unbiased studies. Here we look into the less than ideal situation in which contributing studies may be compromised by non-differential measurement error in the exposure variable. Specifically, we consider a meta-analysis for the association between a continuous outcome variable and one or more continuous exposure variables, where the associations may be quantified as regression coefficients of a linear regression model. A flexible Bayesian framework is developed which allows one to obtain appropriate point and interval estimates with varying degrees of prior knowledge about the magnitude of the measurement error. We also demonstrate how, if individual-participant data (IPD) are available, the Bayesian meta-analysis model can adjust for multiple participant-level covariates, these being measured with or without measurement error.
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Affiliation(s)
- Harlan Campbell
- Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, Utrecht University, Utrecht, The Netherlands
| | - Lauren Maxwell
- Heidelberg Institute for Global Health, Heidelberg University Hospital, Heidelberg, Germany
| | - Thomas Jaenisch
- Heidelberg Institute for Global Health, Heidelberg University Hospital, Heidelberg, Germany.,Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, USA
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, Utrecht University, Utrecht, The Netherlands.,Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Paul Gustafson
- Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
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Kim KE. Is correcting exposure misclassification bias an additional option in meta-analyses? Clin Exp Pediatr 2021; 64:117-118. [PMID: 33445829 PMCID: PMC7940087 DOI: 10.3345/cep.2020.00325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 12/29/2020] [Indexed: 11/28/2022] Open
Affiliation(s)
- Ki Eun Kim
- Department of Pediatrics, CHA Gangnam Medical Center, CHA University, Seoul, Korea
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Biffi A, Rea F, Locatelli A, Cetin I, Filippelli A, Corrao G. Misleading meta-analyses of observational studies may generate unjustified alarms: The case of medications for nausea and vomiting in pregnancy. Pharmacol Res 2020; 163:105229. [PMID: 33031909 DOI: 10.1016/j.phrs.2020.105229] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/19/2020] [Accepted: 09/30/2020] [Indexed: 01/11/2023]
Abstract
OBJECTIVES Because observational studies often use imperfect measurements, results are prone to misclassification errors. We used as a motivating example the possible teratogenic risks of antiemetic agents in pregnancy since a large observational study recently showed that first-trimester exposure to doxylamine-pyridoxine was associated with significantly increased risk of congenital malformations as a whole, as well as central nervous system defects, and previous observational studies did not show such associations. A meta-analysis on this issue was carried out with the aim to illustrate how differential exposure and outcome misclassifications may lead to uncertain conclusions. METHODS Medline, searched to October 2019 for full text papers in English. Summary Odds Ratios (ORs) with confidence intervals (CIs) were calculated using random-effect models. Probabilistic sensitivity analyses were performed for evaluating the extension of differential misclassification required to account for the exposure-outcome association. RESULTS Summary ORs were 1.02 (95 % CI, 0.92-1.15), 0.99 (0.82-1.19) and 1.25 (1.08-1.44) for overall congenital, cardiocirculatory, and central nervous system malformations respectively. By assuming exposure and outcome bias factor respectively of 0.95 (i.e., newborns with congenital defects had exposure specificity 5% lower than healthy newborns) and 1.12 (i.e., exposed newborns had outcome sensitivity 12 % higher than unexposed newborns), summary OR of central nervous system defects became 1.13 (95 % CI, 0.99-1.29) and 1.17 (95 % CI, 0.99-1.38). CONCLUSION Observational investigations and meta-analyses of observational studies need cautious interpretations. Their susceptibility to several, often sneaky, sources of bias should be carefully evaluated.
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Affiliation(s)
- Annalisa Biffi
- National Centre of Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy; Unit of Biostatistics, Epidemiology and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
| | - Federico Rea
- National Centre of Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy; Unit of Biostatistics, Epidemiology and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Anna Locatelli
- Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy; Department of Obstetrics and Gynecology, Ospedale San Gerardo, Monza, Italy
| | - Irene Cetin
- Scienze biomediche e cliniche 'Luigi Sacco', University of Milano, Milano, Italy; ASST Fatebenefratelli Sacco, Milano, Italy
| | - Amelia Filippelli
- National Centre of Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy; Department of Medicine and Surgery, University of Salerno, Salerno, Italy
| | - Giovanni Corrao
- National Centre of Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy; Unit of Biostatistics, Epidemiology and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
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