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Weighted Random Regression Models and Dropouts

Title
Weighted Random Regression Models and Dropouts
Authors
Ahn C.Jung S.-H.Kang S.-H.
Ewha Authors
강승호
Issue Date
2004
Journal Title
Therapeutic Innovation & Regulatory Science
ISSN
2168-4790JCR Link
Citation
vol. 38, no. 2, pp. 135 - 141
Keywords
DropoutsSimulationWeighted random regression
Indexed
SCIE; SCOPUS scopus
Abstract
In studies with repeated measurements, one of the popular primary interests is the comparison of the rates of change in a response variable between groups. The random regression model (RRM) has been offered as a potential solution to statistical problems posed by dropouts in clinical trials. However, the power of RRM tests for differences in rates of change can be seriously reduced due to dropouts. We examine the effect of dropouts on the power of RRM tests for testing differences in the rates of change between two groups through simulation. We examine the performance of weighted random regression models, which assign equal weights to subjects, equal weights to measurements, and optimal weights that minimize the variance of the regression coefficient. We perform the simulation study to evaluate the performance of the above three weighting schemes using type I errors and the power in repeated measurements data as affected by different dropout mechanisms such as random dropouts and treatment-dependent dropouts. © 2004, Drug Information Association. All rights reserved.
DOI
10.1177/009286150403800206
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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