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
Incomplete quality of life data in randomized trials: missing items
β Scribed by Peter M. Fayers; Desmond Curran; David Machin
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 258 KB
- Volume
- 17
- Category
- Article
- ISSN
- 0277-6715
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β¦ Synopsis
Missing data has been a problem in many quality of life studies. This paper focuses upon the issues involved in handling forms which contain one or more missing items, and reviews the alternative procedures. One of the most widely practised approaches is imputation using the mean of all observed items in the same subscale. This, together with the related estimation of the subscale score, is based upon traditional psychometric approaches to scale design and analysis. We show that it may be an inappropriate method for many of the items in quality of life questionnaires, and would result in biased or misleading estimates. We provide examples of items and subscales which violate the psychometric foundations that underpin simple mean imputation. A checklist is proposed for examining the adequacy of simple imputation, and some alternative procedures are indicated.
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