G R Norman1, F G Sridhar, G H Guyatt, S D Walter. 1. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. norman@mcmaster.ca
Abstract
BACKGROUND: Approaches to interpretation of quality of life changes in clinical 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 anchor, such as patient judgments of change, which is then used to compute a Minimally Important Difference (MID), the proportion benefiting from treatment, p(B), and the Number Needed to Treat (NNT). OBJECTIVE: To examine the relationship between the ES and p(B), and the impact 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 designs, for various values of the ES and the MID. The agreement of the simulation 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 differed for crossover and parallel group designs, the general form was similar. Introducing moderate skew into the distributions had minimal impact on the 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 information in this situation.
BACKGROUND: Approaches to interpretation of quality of life changes in clinical 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 anchor, such as patient judgments of change, which is then used to compute a Minimally Important Difference (MID), the proportion benefiting from treatment, p(B), and the Number Needed to Treat (NNT). OBJECTIVE: To examine the relationship between the ES and p(B), and the impact 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 designs, for various values of the ES and the MID. The agreement of the simulation 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 differed for crossover and parallel group designs, the general form was similar. Introducing moderate skew into the distributions had minimal impact on the 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 information in this situation.
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