Improving point predictions of random effects for subjects at high risk.

Improving point predictions of random effects for subjects at high risk. Research Abstract Details 

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Improving point predictions of random effects for subjects at high risk. Abstract Text:

Robert H Lyles,Amita K Manatunga, Moore,F DuBois Bowman,Curtiss B Cook,

The prediction of random effects corresponding to subject-specific characteristics (e.g. means or rates of change) can be very useful in medical and epidemiologic research. At times, one may be most interested in obtaining accurate and/or precise predictions for subjects whose characteristic places them in a tail of the distribution. While the typical posterior mean predictor dominates others in terms of overall mean squared error of prediction (MSEP), its tendency to 'overshrink' has motivated research into alternatives emphasizing other criteria. Here, we specifically target MSEP within a certain region (e.g. above a known cut-off for high risk or a specified percentile of the random effect distribution), and we consider minimizing this quantity with and without constraints on overall MSEP efficiency. We use the normal-theory random intercept model to derive prediction methods with potential to yield markedly better performance for subjects in the specified region, given a well-controlled and (if desired) modest concession of overall MSEP. Criteria geared toward classification as well as overall and regional prediction unbiasedness are also provided. We evaluate the proposed techniques and illustrate them using repeated measures data on fasting blood glucose from type 2 diabetes patients. A simulation study verifies that theoretical properties and relative performances of the proposed predictors are essentially maintained when calculating them in practice based on estimated mixed linear model parameters. Straightforward extensions to incorporate covariates and additional random effects are briefly outlined.

Improving point predictions of random effects for subjects at high risk. Publishing Authors By Initials

RH Lyles,AK Manatunga,RH Moore,F DuBois Bowman,CB Cook,

For similar geographic locations: americas: north america: united states research abstracts see: geographic locations: americas: north america: united states research

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Improving point predictions of random effects for subjects at high risk. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: Statistics in medicine

VOLUME: 26

Page Numbers: 1285-300

Journal Abbreviation:

ISSN: 0277-6715

DAY: 15

MONTH: Mar

YEAR: 2007

Improving point predictions of random effects for subjects at high risk. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 8215016

Improving point predictions of random effects for subjects at high risk. Keywords Mesh Terms:

KEYWORDS: United States

MESH TERMS: analysis

Chemical & Substance for Abstract: Improving point predictions of random effects for subjects at high risk. Information

Substance Name: Blood Glucose

Registry Number: 0

Grant and Affiliation Information for Improving point predictions of random effects for subjects at high risk.

AFFILIATION: Department of Biostatistics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Atlanta, GA 30322, USA. rlyles@sph.emory.edu

Country: England

AGENCY: United States NIEHS

GRANT: R01 ES012458

ACRONYM: ES

MEDLINETA: Stat Med

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