Main Title |
Analyzing Repeated Measurements with Possibly Missing Observations by Modelling Marginal Distributions. |
Author |
Wei, L. J. ;
Stram., D. O. ;
|
CORP Author |
Michigan Univ., Ann Arbor. Dept. of Biostatistics. ;Radiation Effects Research Foundation, Hiroshima (Japan).;Health Effects Research Lab., Research Triangle Park, NC. |
Publisher |
c1988 |
Year Published |
1988 |
Report Number |
EPA-R-813495; EPA/600/J-88/523; |
Stock Number |
PB90-232299 |
Additional Subjects |
Biostatistics ;
Mathematical models ;
Biomathematics ;
Statistical analysis ;
Statistical distributions ;
Reprints ;
Health statistics
|
Holdings |
Library |
Call Number |
Additional Info |
Location |
Last Modified |
Checkout Status |
NTIS |
PB90-232299 |
Some EPA libraries have a fiche copy filed under the call number shown. |
|
07/26/2022 |
|
Collation |
12p |
Abstract |
Suppose that subjects are observed repeatedly over a common set of time points with possibly time-dependent covariates and possibly missing observations. At each time point, the authors model the marginal distribution of the response variable and the effect of the covariates on that distribution using a class of quasi-likelihood models. No parametric model of dependence of the repeated observations of the subject is assumed. For large samples, the quasi-likelihood estimates of the time-specific regression coefficients over the set of predetermined time points are shown to be approximately jointly normal. This, coupled with various inference procedures, provides a global picture about the effects of the covariates on the response variable over the entire study period. A lack-of-fit test for testing the adequacy of the assumed quasi-likelihood model is also provided. All the methods considered are illustrated with real-life examples. (Copyright (c) 1988 by John Wiley & Sons, Ltd.) |