Two-stage method of estimation for general linear growth curve models.

Abstract:

:We extend the linear random-effects growth curve model (REGCM) (Laird and Ware, 1982, Biometrics 38, 963-974) to study the effects of population covariates on one or more characteristics of the growth curve when the characteristics are expressed as linear combinations of the growth curve parameters. This definition includes the actual growth curve parameters (the usual model) or any subset of these parameters. Such an analysis would be cumbersome using standard growth curve methods because it would require reparameterization of the original growth curve. We implement a two-stage method of estimation based on the two-stage growth curve model used to describe the response. The resulting generalized least squares (GLS) estimator for the population parameters is consistent, asymptotically efficient, and multivariate normal when the number of individuals is large. It is also robust to model misspecification in terms of bias and efficiency of the parameter estimates compared to maximum likelihood with the usual REGCM. We apply the method to a study of factors affecting the growth rate of salmonellae in a cubic growth model, a characteristic that cannot be analyzed easily using standard techniques.

journal_name

Biometrics

journal_title

Biometrics

authors

Stukel TA,Demidenko E

subject

Has Abstract

pub_date

1997-06-01 00:00:00

pages

720-8

issue

2

eissn

0006-341X

issn

1541-0420

journal_volume

53

pub_type

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