Missing quality of life data in cancer clinical trials: serious problems and challenges

Stat Med. 1998 Mar 15-Apr 15;17(5-7):517-32. doi: 10.1002/(sici)1097-0258(19980315/15)17:5/7<517::aid-sim799>3.0.co;2-s.


Measurement of quality of life (QOL) in cancer clinical trials has increased in recent years as more groups realize the importance of such endpoints. A key problem has been missing data. Some QOL data may unavoidably be missing, as for example when patients are too ill to complete forms. Other important sources are potentially avoidable and can broadly be divided into three categories: (i) methodological factors; (ii) logistic and administrative factors; (iii) patient-related factors. Logistic and administrative factors, for example, staff oversights, have proven to be most important. Since most QOL measurements require patient self-report, it is usually not possible to rectify the failure to collect baseline data or any follow-up assessments. There is strong evidence that such data are not 'missing at random', and cannot be ignored without introducing bias. Although several approaches to the analysis of partly missing data have been described, none is entirely satisfactory. Prevention of avoidable missing data is better than attempted cure. In July 1996, an international conference on missing QOL data in cancer clinical trials reported the experience of most major groups involved. This paper will serve as an introduction to the problem and provide an estimation of its magnitude, and approaches to its prevention and solution.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Bias
  • Clinical Trials as Topic / methods*
  • Data Collection / methods
  • Humans
  • Neoplasms / drug therapy
  • Neoplasms / psychology*
  • Quality of Life*
  • Research Design