Relation of distribution- and anchor-based approaches in interpretation ofchanges in health-related quality of life

Background. Approaches to interpretation of quality of life changes in clin ical trials have fallen into two camps: those that rely on the distribution of changes and the Effect Size (ES), and those that use some external anch or, such as patient judgments of change, which is then used to compute a Mi nimally Important Difference (MID), the proportion benefiting from treatmen t, p(B), and the Number Needed to Treat (NNT). OBJECTIVE. To examine the relationship between the ES and p(B), and the imp act of the MID on this relationship. METHODS. Simulation was used based on a normal distribution to compute the proportion of patients benefiting in both parallel group and crossover desi gns, for various values of the ES and the MID. The agreement of the simulat ion with empirical data from four studies of asthma and respiratory disease was assessed. The effect of skewness in the distributions of change scores on the relationship between ES and p(B) was also examined. RESULTS. The simulation showed a near-linear relationship between ES and p( B), which was nearly independent of the value of the MID. Agreement of the simulation with the empirical data were excellent. Although the curves diff ered for crossover and parallel group designs, the general form was similar . Introducing moderate skew into the distributions had minimal impact on th e relationship. CONCLUSIONS. The proportion of patients who will benefit from treatment can be directly estimated from the ES, and is nearly independent of the choice of MID. Effect size and anchor based approaches provide equivalent informa tion in this situation.