Why are missing quality of life data a problem in clinical trials of cancer therapy?

Abstract

Assessment of health related quality of life has become an important endpoint in many cancer clinical trials. Because the participants of these trials often experience disease and treatment related morbidity and mortality, non-random missing assessments are inevitable. Examples are presented from several such trials that illustrate the impact of missing data on the analysis of QOL in these trials. The sensitivity of different analyses depends on the proportion of assessments that are missing and the strength of the association of the underlying reasons for missing data with disease and treatment related morbidity and mortality. In the setting of clinical trials of cancer therapy, the assumption that the data are missing completely at random (MCAR) and analyses of complete cases is usually unjustified. Further, the assumption of missing at random (MAR) may also be violated in many trials and models appropriate for non-ignorable missing data should be explored. Recommendations are presented to minimize missing data, to obtain useful documentation concerning the reasons for missing data and to perform sensitivity analyses.