EFFECT SIZE AND POWER FOR CLINICAL-TRIALS THAT MEASURE YEARS OF HEALTHY LIFE

Some clinical trials perform repeated measurements on patients over ti me, plot those measures against time, and summarize the results in ter ms of the area under the curve. If the measured variable is health sta tus, the summary outcome is sometimes referred to as years of healthy life (YHL), or quality-adjusted life years (QALY). This paper investig ates some theoretical and practical aspects of randomized trials desig ned to assess measures such as YHL. We first derived algebraic express ions for the effect size of YHL measures under several theoretical mod els of the treatment's effect on health. We used these expressions to examine how the length of the study, the number of measurements per pe rson and the correlations among health measurements over time influenc e the effect size. We also explored the relative statistical power of analyses based on YHL versus analyses based on change-scores using the same data. We present an example. Findings suggest that: (i) the numb er of measurements per person need not be large; (ii) high correlation among measures over time tends to lower the power of a study using YH L; (iii) a longer study will not always provide more power than a shor ter study, and (iv) analyses based on YHL may have less power than cha nge-score analyses. Some of these findings depend on the model of chan ge in health status caused by the treatment. Such models require furth er study. (C) 1997 by John Wiley & Sons, Ltd.