Should regression calibration or multiple imputation be used when calibrating different devices in a longitudinal study?
| Autor: | Loop MS; Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University., Lotspeich SC; Department of Statistical Sciences, Wake Forest University., Garcia TP; Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill., Meyer ML; Department of Emergency Medicine, UNC School of Medicine, University of North Carolina at Chapel Hill. |
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| Jazyk: | angličtina |
| Zdroj: | American journal of epidemiology [Am J Epidemiol] 2024 Jul 02. Date of Electronic Publication: 2024 Jul 02. |
| DOI: | 10.1093/aje/kwae169 |
| Abstrakt: | In longitudinal studies, the devices used to measure exposures can change from visit to visit. Calibration studies, wherein a subset of participants is measured using both devices at follow-up, may be used to assess between-device differences (i.e., errors). Then, statistical methods are needed to adjust for between-device differences and the missing measurement data that often appear in calibration studies. Regression calibration and multiple imputation are two possible methods. We compared both methods in linear regression with a simulation study, considering various real-world scenarios for a longitudinal study of pulse wave velocity. Regression calibration and multiple imputation were both essentially unbiased, but correctly estimating the standard errors posed challenges. Multiple imputation with predicted mean matching produced close agreement with the empirical standard error. Fully stochastic multiple imputation underestimated the standard error by up to 50%, and regression calibration with bootstrapped standard errors performed slightly better than fully stochastic multiple imputation. Regression calibration was slightly more efficient than either multiple imputation method. The results suggest use of multiple imputation with predictive mean matching over fully stochastic imputation or regression calibration in longitudinal studies where a new device at follow-up might be error-prone compared to the device used at baseline. (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.) |
| Databáze: | MEDLINE |
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