Combining components of quality of life to increase precision and evaluatetrade-offs

Methods for combining measurements on multiple dimensions of quality of lif e can reduce the dimensionality of the data and increase the precision of e stimation. When the dimensions are weighted according to their importance t o patients, the resulting estimate is clinically useful and provides a step towards a true utility estimate. We derive two such weighting methods usin g 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 lif e measures into utility measures are demonstrated. Copyright (C) 2001 John Wiley & Sons, Ltd.