www.uhasselt.be
DSpace

Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/368

Title: Using a Box-Cox transformation in the analysis of longitudinal data with incomplete responses
Authors: Lipsitz, Stuart R.
Ibrahim, Joseph G.
Molenberghs, Geert
Keywords: Longitudinal data
Multivariate data
Missing data
Issue Date: 2000
Publisher: BLACKWELL PUBLISHING
Citation: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 49(3). p. 287-296
Abstract: We analyse longitudinal data on CD4 cell counts from patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. The investigators were interested in modelling the CD4 cell count as a function of treatment, age at base-line and disease stage at base-line. Serious concerns can be raised about the normality assumption of CD4 cell counts that is implicit in many methods and therefore an analysis may have to start with a transformation. Instead of assuming that we know the transformation (e.g. logarithmic) that makes the outcome normal and linearly related to the covariates, we estimate the transformation, by using maximum likelihood, within the Box–Cox family. There has been considerable work on the Box–Cox transformation for univariate regression models. Here, we discuss the Box–Cox transformation for longitudinal regression models when the outcome can be missing over time, and we also implement a maximization method for the likelihood, assumming that the missing data are missing at random.
URI: http://hdl.handle.net/1942/368
DOI: 10.1111/1467-9876.00192
ISI #: 000087038300008
ISSN: 0035-9254
Category: A1
Type: Journal Contribution
Validation: ecoom, 2001
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version169.73 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.