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/1942/13001

Title: Analysis of non-ignorable missing and left-censored longitudinal data using a weighted random effects tobit model
Authors: Sattar, Abdus
Weissfeld, Lisa A.
Molenberghs, Geert
Issue Date: 2011
Publisher: WILEY-BLACKWELL
Citation: STATISTICS IN MEDICINE, 30 (27), p. 3167-3180
Abstract: In a longitudinal study with response data collected during a hospital stay, observations may be missing because of the subject's discharge from the hospital prior to completion of the study or the death of the subject, resulting in non-ignorable missing data. In addition to non-ignorable missingness, there is left-censoring in the response measurements because of the inherent limit of detection. For analyzing non-ignorable missing and left-censored longitudinal data, we have proposed to extend the theory of random effects tobit regression model to weighted random effects tobit regression model. The weights are computed on the basis of inverse probability weighted augmented methodology. An extensive simulation study was performed to compare the performance of the proposed model with a number of competitive models. The simulation study shows that the estimates are consistent and that the root mean square errors of the estimates are minimal for the use of augmented inverse probability weights in the random effects tobit model. The proposed method is also applied to the non-ignorable missing and left-censored interleukin-6 biomarker data obtained from the Genetic and Inflammatory Markers of Sepsis study. Copyright (C) 2011 JohnWiley & Sons, Ltd.
Notes: [Sattar, Abdus] Case Western Reserve Univ, Dept Epidemiol & Biostat, Cleveland, OH 44106 USA. [Weissfeld, Lisa A.] Univ Pittsburgh, Dept Biostat, Pittsburgh, PA 15261 USA. [Molenberghs, Geert] Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. sattar@case.edu
URI: http://hdl.handle.net/1942/13001
DOI: 10.1002/sim.4344
ISI #: 000297973100001
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2013
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
published version241.44 kBAdobe PDF

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