Sample Size Estimation for EORTC QLQ-C30 Summary Score as Primary Endpoint

Background This paper discusses the importance of sample size in research, highlighting the ICH Guidance's requirement for clinical studies to clearly explain their sample size, particularly when using the EORTC QLQ-C30 Summary Score as the primary outcome measure. The idea behind this paper is to give the sample size when the outcome is Global Health Status and Out-of-Pocket Expenditure. Method The sample size calculation is based on a simulation technique using SAS software, assuming equal allocation between groups to yield a significant result with sufficient power. Results Sample sizes obtained using a simulation-based method require 25 patients per group to account for a minimum clinically significant difference of 14 units between the two groups, with 88% statistical power at a 5% level of significance. Conclusion The simulation-based technique, combined with validated software, can be helpful when the effect size and variability of the QoL score are not precisely known, particularly when the effect size is not sufficiently specified to determine the sample size accurately. Compared to conventional techniques, simulation-based sample size estimation increases confidence in reaching statistical power, particularly for complicated endpoints like the EORTC QLQ-C30 summary score.