Integrating health profile with survival for quality of life assessment

Abstract

In cohort studies or clinical trials, measurements of quality of life (QoL) were averaged across available individuals for each group at given points in time to produce single measures for comparisons. However, estimates of these single measures may be severely biased if substantial mortality occurs over time. The objective of this study is to develop a method that integrates QoL measurement and survival for long-term evaluation of health services. We defined a mean QoL score function over time for an index population as the average QoL score of all individuals both alive and dead at each time point in the population. While a living subject's QoL can be assessed by asking one's subjective preference, the score of a decedent can be assigned a fixed value depending on the specific facet on health profile. The mean QoL score function over time is reduced to a single measure of expected cumulative QoL score, which is the area under the curve of mean QoL score function over a given time interval and can be estimated by taking a random sample from a cross-sectional survey. For the QoL score function to be extrapolated to life-long, it requires the assumption that the disease causes premature death or a long-term moderate impairment of QoL. We provided methods and computer programs for estimating mean QoL score functions and the reduced single measures for use in comparisons. A cohort of 779 breast cancer patients from Chiangmai, Thailand were followed for 12 years to demonstrate the proposed methods. The data included the 12-year complete survival records and QoL scores on 233 patients collected from a cross-sectional survey using WHOQOL questionnaire and standard gamble method. The expected cumulative QoL scores using utility and psychometric scales were compared among patients in four groups of clinical stages in this cohort for time from onset up to 12 years and life-long. We conclude that such an integration of QoL measurement with survival can be useful for the evaluation of health service and clinical decision.