Methodology for quality-of-life assessment: a critical appraisal

The methodology for QOL assessment covers a wide range of topics. It involves a proper choice of instruments with appropriate psychometric properties, the administration of these instruments, frequency of measurements, missing data problems, and the method of analysis. There are currently debates on the meaning and interpretation of the HRQOL domains taking the form of arguing how to define minimal clinically meaningful difference and whether this can be used in regulatory approval for drug development. From a practical point of view, the authors proposed that a disease-specific checklist or symptom domains incorporated within a HRQOL questionnaire may be a middle ground to gain general agreements among academic institutions, manufacturers, and regulatory agencies to use a specific symptom checklist or domain as the primary end point for clinical trials together with other HRQOL domains as ancillary data for the study. Antiemetic trial with HRQOL assessments is an example. Most would agree, however, that no matter what HRQOL domains or symptoms are being studied, it should be based on a patient self-administered questionnaire as shown by the lack of sensitivity in the example in this article. Missing data are a problem in the data collection and handling. The authors have examined a few commonly used approaches and performed simulation to study their properties. The subscale-mean method when one has more than 50% of the information on a subscale generally reflects the true values. In practice, one still would have missing data that cannot be handled completely by imputation. The method of analysis must be flexible enough to incorporate the nature of these data. Two approaches have been discussed, and they are both flexible in terms of using all available information being obtained in a longitudinal fashion with variable visiting schedules and potential missing data. The HRQOL response variable approach is simple and easy to understand. The growth curve models approach provides more detailed information on average trends between treatment arms. In general, these two methods agree on the results of the example. They can be used to report clinical trial results using HRQOL data as end points.