Main Title |
Linear Models for the Analysis of Longitudinal Studies. |
Author |
Ware, J. H. ;
|
CORP Author |
Harvard School of Public Health, Boston, MA.;Health Effects Research Lab., Research Triangle Park, NC.;National Inst. of General Medical Sciences, Bethesda, MD. |
Year Published |
1985 |
Report Number |
EPA-R-811151 ;PHS-GM-29745; EPA/600/J-85/508; |
Stock Number |
PB88-140421 |
Additional Subjects |
Numerical analysis ;
Multivariate analysis ;
Randomization ;
Sampling ;
Linear programming ;
Statistical analysis ;
Reprints ;
Autoregressive processes ;
Biomedical research ;
Longitudinal studies
|
Holdings |
Library |
Call Number |
Additional Info |
Location |
Last Modified |
Checkout Status |
NTIS |
PB88-140421 |
Some EPA libraries have a fiche copy filed under the call number shown. |
|
07/26/2022 |
|
Collation |
9p |
Abstract |
Longitudinal investigations play an increasingly prominent role in biomedical research. Much of the literature on specifying and fitting linear models for serial measurements uses methods based on the standard multivariate linear model. The article proposes a more flexible approach that permits specification of the expected response as an arbitrary linear function of fixed and time-varying covariates so that mean-value functions can be derived from subject matter considerations rather than methodological constraints. Three families of models for the covariance function are discussed: multivariate, autoregressive, and random effects. Illustrations demonstrate the flexibility and utility of the proposed approach to longitudinal analysis. (Copyright (c) 1985 American Statistical Association.) |