Combining Components of Quality of Life to Increase Precision and Evaluate Trade-offs

Methods for combining measurements on multiple dimensions of quality of life can reduce the dimensionality of the data and increase the precision of estimation. When the dimensions are weighted according to their importance to patients, the resulting estimate is clinically useful and provides a step towards a true utility estimate. We derive two such weighting methods using linear regression on a measure of overall quality of life and demonstrate their usefulness in the analysis of quality of life data from two clinical trials of cancer therapies. Procedures for transforming the quality of life measures into utility measures are demonstrated.