Estimating power for clinical trials with Patient Reported Outcomes-using Item Response Theory

Objectives: Patient reported outcomes (PRO) are widely used in quality of life (QOL) studies, health outcomes research, and clinical trials. The importance of PRO has been advocated by health authorities. Patient Reported Outcomes Measurement Information System (PROMIS) is a collection of standardized measures of PROs using Item Response Theory (IRT). However, in clinical trials with PROs as endpoints, observed scores are routinely used for power estimation rather than IRT scores. This paper aims to fill this gap and estimate power in a two-arm clinical trials with PROMIS measures as endpoints with IRT model. Study Design and Setting: We conducted a series of simulations to study the IRT power with validated PROMIS measures controlling factors including sample size, effect size, number of items, and missing data proportion. Results: Our results showed that sample size, effect size, and number of items are important indicators of IRT based power estimation for PROMIS measures. When effect size is small and sample size is limited, IRT model provides higher power than the closed form formula. Conclusion: IRT based simulation should be used for power estimation in two-armed clinical, especially when there is small effect size or small sample size. Published by Elsevier Inc.

Automated summary

This study aimed to estimate power in a two-arm clinical trial with PROMIS measures as endpoints using IRT model. Results indicated that sample size, effect size, and number of items are important indicators for IRT based power estimation. When effect size is small and sample size is limited, IRT model provides higher power.