Adjusting for Misclassification of an Exposure in an Individual Participant Data Meta-Analysis

Autor/inn/en	de Jong, Valentijn M. T.; Campbell, Harlan; Maxwell, Lauren; Jaenisch, Thomas; Gustafson, Paul; Debray, Thomas P. A.
Titel	Adjusting for Misclassification of an Exposure in an Individual Participant Data Meta-Analysis
Quelle	In: Research Synthesis Methods, 14 (2023) 2, S.193-210 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (de Jong, Valentijn M. T.) ORCID (Maxwell, Lauren) ORCID (Debray, Thomas P. A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1606
Schlagwörter	Classification; Meta Analysis; Bayesian Statistics; Evaluation Methods; Participant Characteristics; Accuracy; Simulation + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Meta-analysis; Metaanalyse; Simulation program; Simulationsprogramm
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. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01