Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/433

Title: Sensitivity analysis for pattern mixture models
Authors: Curran, Desmond
Molenberghs, Geert
Thijs, Herbert
Verbeke, Geert
Keywords: Longitudinal data
Missing data
Issue Date: 2003
Citation: Journal of Biopharmaceutical Statistics, 14. p. 125-143
Abstract: Incomplete series of data is a common feature in quality-of-life studies, in particular in chronic diseases where attrition of patients is high. Two alternative approaches to modeling longitudinal data with incomplete measurements have frequently been proposed in the literature, selection models and pattern-mixture models. In this paper we focus on, by way of sensitivity analysis, extrapolating incomplete patterns using identifying restrictions. Perhaps the best known ones are so-called complete case missing value restrictions (CCMV), where for a given pattern, the conditional distribution of the missing data, given the observed data, is equated to its counterpart in the completers. Available case missing value (ACMV) restrictions equate this conditional density to the one calculated from the subgroup of all patterns for which all required components have been observed. Neighboring case missing value restrictions (NCMV) equate this conditional density to the one calculated from the pattern with one additional measurement obtained. In this paper, these three identifying restriction strategies are used to multiply impute missing data in a study in metastatic prostate cancer. Multiple imputation is employed to reduce the uncertainty of single imputation. It is shown how hypothesis testing and sensitivity analyses are carried out in this setting.
URI: http://hdl.handle.net/1942/433
DOI: 10.1081/BIP-120028510
ISSN: 1054-3406
Category: A2
Type: Journal Contribution
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version215.5 kBAdobe PDF
Peer-reviewed author version2.5 MBAdobe PDF

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