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Comparison of several model‐based methods for analysing incomplete quality of life data in cancer clinical trials
Author(s) -
Fairclough Diane L.,
Peterson Harriet F.,
Cella David,
Bonomi Phil
Publication year - 1998
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19980315/15)17:5/7<781::aid-sim821>3.0.co;2-o
Subject(s) - missing data , clinical trial , statistics , medicine , quality of life (healthcare) , breast cancer , random effects model , cancer , lung cancer , oncology , mathematics , meta analysis , nursing
This paper considers five methods of analysis of longitudinal assessment of health related quality of life (QOL) in two clinical trials of cancer therapy. The primary difference in the two trials is the proportion of participants who experience disease progression or death during the period of QOL assessments. The sensitivity of estimation of parameters and hypothesis tests to the potential bias as a consequence of the assumptions of missing completely at random (MCAR), missing at random (MAR) and non‐ignorable mechanisms are examined. The methods include complete case analysis (MCAR), mixed‐effects models (MAR), a joint mixed‐effects and survival model and a pattern‐mixture model. Complete case analysis overestimated QOL in both trials. In the adjuvant breast cancer trial, with 15 per cent disease progression, estimates were consistent across the remaining four methods. In the advanced non‐small‐cell lung cancer trial, with 35 per cent mortality, estimates were sensitive to the missing data assumptions and methods of analysis. © 1998 John Wiley & Sons, Ltd.