Identifying the types of missingness in quality of life data from clinical trials

✍ Scribed by D. Curran; M. Bacchi; S. F. Hsu Schmitz; G. Molenberghs; R. J. Sylvester

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 268 KB
• Volume: 17
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(19980315/15)17:5/7<739::aid-sim818>3.0.co;2-m

✦ Synopsis

This paper discusses methods of identifying the types of missingness in quality of life (QOL) data in cancer clinical trials. The first approach involves collecting information on why the QOL questionnaires were not completed. Based on the reasons provided one may be able to distinguish the mechanisms causing missing data. The second approach is to model the missing data mechanism and perform hypothesis testing to determine the missing data processes. Two methods of testing if missing data are missing completely at random (MCAR) are presented and applied to incomplete longitudinal QOL data obtained from international multi-centre cancer clinical trials. The first method (Ridout, 1991) is based on a logistic regression and the second method (Park and Davis, 1993) is based on an adaptation of weighted least squares. In one application (advanced breast cancer) missing data was not likely to be MCAR. In the second application (adjuvant breast cancer) the missing mechanism was dependent on the QOL scale under study. MCAR and missing at random (MAR) have distinct consequences for data analysis. Therefore it is relevant to distinguish between them. However, if either MCAR or MAR hold, likelihood or Bayesian inferences can be based solely on the observed data, although for MAR, depending on the research question, modelling the dropout mechanism may still be necessary. Distinguishing between MAR and missing not at random (MNAR) is not trivial and relies on fundamentally untestable assumptions.