Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 679-696 |
| Number of pages | 18 |
| Journal | Statistics in Medicine |
| Volume | 17 |
| Issue number | 5-7 |
| DOIs | |
| Publication status | Published - 15 Mar 1998 |
Bibliographical note
ACKNOWLEDGEMENTSWe wish to thank the MRC Cancer Therapy Committee and its Working Parties for access to data from MRC trials CR04, LU16, and the TE17 study. Copies of MRC protocols are available upon request to MRC Cancer Trials Office, 5 Shaftesbury Road, Cambridge CB2 2BW, U.K.