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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Empirical constrained Bayes predictors accounting for non-detects among repeated measures
Statistics in medicine, v 29(25), pp 2656-2668
10 Nov 2010
PMID: 20809486
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
When the prediction of subject-specific random effects is of interest, constrained Bayes predictors (CB) have been shown to reduce the shrinkage of the widely accepted Bayes predictor while still maintaining desirable properties, such as optimizing mean-square error subsequent to matching the first two moments of the random effects of interest. However, occupational exposure and other epidemiologic (e.g. HIV) studies often present a further challenge because data may fall below the measuring instrument's limit of detection. Although methodology exists in the literature to compute Bayes estimates in the presence of non-detects (Bayes(ND)), CB methodology has not been proposed in this setting. By combining methodologies for computing CBs and Bayes(ND), we introduce two novel CBs that accommodate an arbitrary number of observable and non-detectable measurements per subject. Based on application to real data sets (e.g. occupational exposure, HIV RNA) and simulation studies, these CB predictors are markedly superior to the Bayes predictor and to alternative predictors computed using ad hoc methods in terms of meeting the goal of matching the first two moments of the true random effects distribution.
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Details
- Title
- Empirical constrained Bayes predictors accounting for non-detects among repeated measures
- Creators
- Reneé H Moore - University of PennsylvaniaRobert H Lyles - Emory UniversityAmita K Manatunga - Emory University
- Publication Details
- Statistics in medicine, v 29(25), pp 2656-2668
- Publisher
- Wiley
- Grant note
- R01 ES012458 / NIEHS NIH HHS UL1 TR000454 / NCATS NIH HHS R01ES012458 / NIEHS NIH HHS R01 ES012458-04 / NIEHS NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000283670700008
- Scopus ID
- 2-s2.0-77958596040
- Other Identifier
- 991021448036904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Medical Informatics
- Medicine, Research & Experimental
- Public, Environmental & Occupational Health
- Statistics & Probability