Estimation of expected quality adjusted survival by cross-sectional survey

Abstract

To compare both mortality and quality of life (QOL) across different illnesses, we propose an estimator to calculate the expected quality adjusted survival (QAS) by multiplying the QOL into the survival function. While the survival function can be determined by the usual life table method, the QOL data can be collected by a cross-sectional survey among patients who are currently surviving. The area under the QAS curve is thus the expected utility of health of the specific illness, which may take a common unit of quality adjusted life year ready for outcome evaluation and policy decision. A simulation is performed to demonstrate that the proposed estimator and its standard error are relatively accurate. The limitations and guidelines for using this estimator are also discussed.