JSM 2005 Online Program

Abstract #304487

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Activity Number:	515
Type:	Contributed
Date/Time:	Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Biometrics Section
Abstract - #304487
Title:	Prediction of Random Intercepts and Slopes When Data Are Subject to a Detection Limit
Author(s):	Renee Moore*+
Companies:	Emory University
Address:	Department of Biostatistics, Atlanta, GA, 30322, United States
Keywords:
Abstract:	When repeated measures data are subject to nondetectable values, it can be challenging to obtain predictions of random effects corresponding to subject-specific characteristics. When the prediction of random effects in linear or nonlinear models is of interest, the posterior mean or Bayes predictor is widely accepted and optimal with respect to squared error loss. However, a recognized disadvantage of the Bayes predictor is its tendency to overshrink estimates toward the population mean. Alternative "constrained Bayes" predictors maintain favorable properties while reducing shrinkage. In this talk, we combine general methods found in the literature for computing constrained Bayes estimates with methods for computing Bayes estimates in the presence of nondetects. The resulting constrained Bayes predictor allows us to provide estimated predictions of the intercepts and slopes of HIV RNA levels pertaining to 528 individuals in an HIV cohort study. We also present results from a simulation study that compared the constrained Bayes predictors with the posterior mean and with ad hoc predictors found in the literature describing longitudinal studies with nondetectable values.

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