A Medical Application of the General Random Coefficient Regression Model

Bondeson, J.; Lanke, Jan

doi:10.1002/bimj.4710320404

Biometrical J

1990

DOI: 10.1002/bimj.4710320404

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A Medical Application of the General Random Coefficient Regression Model

J. Bondeson

Jan Lanke

Abstract: SummaryRCR models are reviewed. Various variance estimators are described, among them a new one.Thew variance eathatore are compared in a simulation study. An obahtric data aet is subjected to a detailed analysis by meana of RCR techniques. In particular, interval estimation ia considered.

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Cited by 3 publications

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Prediction in Random Coefficient Regression Models

Bondeson

1990

Biometrical J

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SummaryMuch attention has been given to the problem ofpredicting future obeervatiomfor some individual within a random coefficient regreasion (RCR) model, using the previous observations on that individual aa well es the information from the re& of the data material. In thia paper, the literature on this subject ie critically reviewed and new methode of linear prediction are proposed for the g e n d RCR model. Exact reaulte am derived for the mean squared errom of Bome predictors in a spacial case, but this ie not poeaible in the general RCR model when ite parametem are not known. In this model, the old and new predictom are compared in a simulation efudy, and further illustrated by prediction in a medical data material.

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Prediction in Random Coefficient Regression Models

Bondeson

1990

Biometrical J

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A random coefficient growth curve analysis of mental development in low‐birth‐weight infants

Carter¹,

Resnick²,

Ariet³

et al. 1992

Statistics in Medicine

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In many medical studies, longitudinal data are collected on each of a sample of patients. The objectives of such studies often are: to estimate and test bivariate or multivariate relationships within each of several groups of patients from these repeated measures data; to compare these relationships among groups; and to test for the effects of baseline covariates on the relationships. This paper illustrates the use of statistical methods for growth curve analysis recently proposed by Vonesh and Carter for achieving these goals by relating a measure of preschool cognitive development to age in four race by sex groups of low-birth-weight infants. Significant declines in Bayley's Mental Development Index (MDI) with increasing age were found in all groups. Birth-weight did not significantly influence the rate of decline but did influence the overall level of performance. Even so, in the group most comparable to Bayley's normative population, predicted MDI was near the norm even for extremely low-birth-weight infants (that is, 1000 grams). Although there is some risk of mental deficit associated with prematurity, eventual developmental delays in low-birth-weight infants frequently are acquired with age. The rate of decline in MDI was significantly associated with race and mother's education. Assumptions required for the valid application of these methods are discussed and tested in the setting of this applied problem. The assumptions appeared valid in this application. We conclude with a brief discussion of available alternatives when the assumptions are violated and point to areas for future research.

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A Comparison of Noniterative Generalized Least Squares and Iterative Maximum Likelihood Estimators When Testing Hypotheses in Random Coefficient Growth Curve Models

Goldschmidt

Timm

2003

Communications in Statistics - Simulation and Computation

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A Medical Application of the General Random Coefficient Regression Model

Abstract: SummaryRCR models are reviewed. Various variance estimators are described, among them a new one.Thew variance eathatore are compared in a simulation study. An obahtric data aet is subjected to a detailed analysis by meana of RCR techniques. In particular, interval estimation ia considered.

Cited by 3 publications

References 9 publications

Prediction in Random Coefficient Regression Models

Prediction in Random Coefficient Regression Models

A random coefficient growth curve analysis of mental development in low‐birth‐weight infants

A Comparison of Noniterative Generalized Least Squares and Iterative Maximum Likelihood Estimators When Testing Hypotheses in Random Coefficient Growth Curve Models

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