QALY measures have become important tools in valuing health interventions so that resource allocations can be made. Current methods, however, may not adequately capture QALYs for cancer patients, particularly in the last years of life. This post reviews the issues and suggests next steps.
A quality-adjusted life year, or QALY, is a year of life adjusted for its quality or its value for the individual. A year in perfect health would be equal to 1 QALY; the value for less than perfect health would decline as health declines. QALYs are used to assess the value of medical interventions and may be used in decisions about allocating health care resources. In the UK for example, NICE uses QALYs to ‘compare different drugs and measure their clinical effectiveness’; NICE recommendations determine whether health care interventions are covered under the NHS. Clearly, then, ensuring that QALYs accurately reflect reality is crucial.
Koonal Shah, a health economist at the OHE, has been part of a team researching how well QALYs assess cancer patients’ health gains from treatment. He presented key findings in June 2010 at the annual conference of Health Technology Assessment International.
QALYs are calculated by multiplying the duration of each health state experienced by patients by the value ascribed to that state. Health states are measured using instruments such as the EQ-5D; valuations are based on the preferences of a sample of the general population.
According to Mr Shah, existing methods for constructing QALYs may be deficient for cancer patients in three respects: descriptions of health state, valuation of health state and the source of values upon which measures are based.
Existing measures of health are either not sensitive enough or not attuned to cancer patients’ actual preferences. For example, evidence suggests that the EQ-5D instrument does not capture the small changes in health that often are very important to cancer patients.
Valuation of health states for cancer patients using traditional methods also encounters problems. For example, the time trade-off (TTO) method assumes that the rate at which people are willing to trade life expectancy for improvements in quality of life is the same under all circumstances. Research has shown, however, that (1) severely ill patients often are willing to sacrifice more life expectancy for smaller gains in health-related quality of life and (2) patients with less than one year to live are often unwilling to trade any time.
Whose values form the basis of health state valuations also matters. NICE recommends that valuations of EQ-5D health states be based on the preferences of the general public. Descriptions of health states using EQ-5D, however, are too general to provide the public with a complete depiction of the patient experience. The public also tends to focus on the negative aspects of ill health, missing the fact that some areas of life are not seriously affected. Finally, a discrepancy exists between the way patients actually adapt to ill health and healthy individuals’ perceptions of their own abilities to adapt. Overall, using the general public to value health states may overvalue interventions aimed at achieving perfect health and undervalue those aimed at prolonging life or achieving small improvements in HRQL.
Mr Shah proposes three possible ways forward, given these concerns. First, improve descriptions by adding dimensions or levels to EQ-5D and/or develop cancer-specific instruments. Second, develop the TTO approach so that it captures the nuances of the relationship between remaining life and quality of life. And, third, make health state descriptions more comprehensive and/or realistic to achieve better-informed valuations.
Download Garau, M., Shah, K.K., Mason, A.R., Wang, Q., Towse, A. and Drummond, M.F. (2010) Using QALYs in cancer: Review of the methodological limitations. Research Paper. London: Office of Health Economics.
See also: Garau, M., Shah, K.K., Mason, A.R., Wang, Q., Towse, A. and Drummond, M.F. (2011) Using QALYs in cancer: A review of the methodological limitations. Pharmacoeconomics. 29(8), 673-685.
http://www.nice.org.uk/newsroom/features/measuringeffectivenessandcosteffectivenesstheqaly.jsp  The team also includes colleagues at OHE and Anne Masson and Professor Mike Drummond of the University of York’s Centre for Health Economics.