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/1942/2008

Title: Analyzing incomplete discrete longitudinal clinical trial data
Authors: Jansen, Ivy
Beunckens, Caroline
Molenberghs, Geert
Verbeke, Geert
Mallinckrodt, Craig
Issue Date: 2006
Publisher: INST MATHEMATICAL STATISTICS
Citation: STATISTICAL SCIENCE, 21(1). p. 52-69
Abstract: Commonly used methods to analyze incomplete longitudinal clinical trial data include complete case analysis (CC) and last observation carried forward (LOCF). However, such methods rest on strong assumptions, including missing completely at random (MCAR) for CC and unchanging profile after dropout for LOCF. Such assumptions are too strong to generally hold. Over the last decades, a number of full longitudinal data analysis methods have become available, such as the linear mixed model for Gaussian outcomes, that are valid under the much weaker missing at random (MAR) assumption. Such a method is useful, even if the scientific question is in terms of a single time point, for example, the last planned measurement occasion, and it is generally consistent with the intention-to-treat principle. The validity of such a method rests on the use of maximum likelihood, under which the missing data mechanism is ignorable as soon as it is MAR. In this paper, we will focus on non-Gaussian outcomes, such as binary, categorical or count data. This setting is less straightforward since there is no unambiguous counterpart to the linear mixed model. We first provide an overview of the various modeling frameworks for non-Gaussian longitudinal data, and subsequently focus on generalized linear mixed-effects models, on the one hand, of which the parameters can be estimated using full likelihood, and on generalized estimating equations, on the other hand, which is a nonlikelihood method and hence requires a modification to be valid under MAR. We briefly comment on the position of models that assume missingness not at random and argue they are most useful to perform sensitivity analysis. Our developments are underscored using data from two studies. While the case studies feature binary outcomes, the methodology applies equally well to other discrete-data settings, hence the qualifier "discrete" in the title.
Notes: Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. Katholieke Univ Leuven, Ctr Biostat, B-3000 Louvain, Belgium.Jansen, I, Hasselt Univ, Ctr Stat, Agoralaan Gebouw D, B-3590 Diepenbeek, Belgium.ivy.jansen@uhasselt.be caroline.beunckens@uhasselt.be geert.molenberghs@uhasselt.be geert.verbeke@med.kuleuven.be mallinckrodt_craig@Lilly.com
URI: http://hdl.handle.net/1942/2008
Link to publication: https://projecteuclid.org/euclid.ss/1149600846
DOI: 10.1214/088342305000000322
ISI #: 000238586200008
ISSN: 0883-4237
Category: A1
Type: Journal Contribution
Validation: ecoom, 2007
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version237.92 kBAdobe PDF
Peer-reviewed author version345.35 kBAdobe PDF

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