Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/340
Title: Incomplete quality of life data in randomized trials: Missing forms
Authors: Curran, Desmond
Molenberghs, Geert 
Fayers, Peter M.
Machin, David
Issue Date: 1998
Source: Statistics in Medicine, 17(5-7). p. 697-709
Abstract: Analysing quality of life (QOL) data may be complicated for several reasons, such as: repeated measures are obtained; data may be collected on ordered categorical responses; the instrument may have multidimensional scales, and complete data may not be available for all patients. In addition, it may be necessary to integrate QOL with length of life. The major undesirable effects of missing data, in QOL research, are the introduction of biases due to inadequate modes of analysis and the loss of efficiency due to reduced sample sizes. Currently, there is no standard method for handling missing data in QOL studies. In fact, there are very few references to methods of handling missing data in this context. The aim of this paper is to provide an overview of methods for analysing incomplete longitudinal QOL data which have either been presented in the QOL literature or in the missing data literature. These methods of analysis include complete case, available case, summary measures, imputation and likelihood-based approaches. We also discuss the issue of bias and the need for sensitivity analyses.
Document URI: http://hdl.handle.net/1942/340
ISSN: 0277-6715
e-ISSN: 1097-0258
DOI: 10.1002/(SICI)1097-0258(19980315/15)17:5/7<697::AID-SIM815>3.3.CO;2-P
ISI #: 000072447800018
Rights: (c) 1998 John Wiley & Sons, Ltd.
Type: Journal Contribution
Validations: ecoom 1999
Appears in Collections:Research publications

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