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Accepted (AM)Open Access (License Unspecified), Open
Journal article
The impact of covariate misclassification using generalized linear regression under covariate-adaptive randomization
Statistical methods in medical research, v 27(1), pp 20-34
01 Jan 2018
PMID: 26596352
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Under covariate adaptive randomization, the covariate is tied to both randomization and analysis. Misclassification of such covariate will impact the intended treatment assignment; further, it is unclear what the appropriate analysis strategy should be. We explore the impact of such misclassification on the trial's statistical operating characteristics. Simulation scenarios were created based on the misclassification rate and the covariate effect on the outcome. Models including unadjusted, adjusted for the misclassified, or adjusted for the corrected covariate were compared using logistic regression for a binary outcome and Poisson regression for a count outcome. For the binary outcome using logistic regression, type I error can be maintained in the adjusted model, but the test is conservative using an unadjusted model. Power decreased with both increasing covariate effect on the outcome as well as the misclassification rate. Treatment effect estimates were biased towards the null for both the misclassified and unadjusted models. For the count outcome using a Poisson model, covariate misclassification led to inflated type I error probabilities and reduced power in the misclassified and the unadjusted model. The impact of covariate misclassification under covariate-adaptive randomization differs depending on the underlying distribution of the outcome.
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Details
- Title
- The impact of covariate misclassification using generalized linear regression under covariate-adaptive randomization
- Creators
- Liqiong Fan - Medical University of South CarolinaSharon D. Yeatts - Medical University of South CarolinaBethany J. Wolf - Medical University of South CarolinaLeslie A. McClure - Drexel UniversityMagdy Selim - Beth Israel Deaconess Medical CenterYuko Y. Palesch - Medical University of South Carolina
- Publication Details
- Statistical methods in medical research, v 27(1), pp 20-34
- Publisher
- Sage
- Number of pages
- 15
- Grant note
- U01 NS059041 / Neurological Emergencies Treatment Trials (NETT) Network U01 NS054630 / National Institute of Neurological Disorders and Stroke (NINDS); United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Neurological Disorders & Stroke (NINDS) U01NS054630 / NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Neurological Disorders & Stroke (NINDS)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000419874400002
- Scopus ID
- 2-s2.0-85041376303
- Other Identifier
- 991019169572204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Health Care Sciences & Services
- Mathematical & Computational Biology
- Medical Informatics
- Statistics & Probability