TY - JOUR
T1 - Estimating power for clinical trials with Patient Reported Outcomes - using Item Response Theory
AU - Hu, Jinxiang
AU - Thompson, Jeffrey
AU - Mudaranthakam, Dinesh Pal
AU - Hinton, Lynn Chollet
AU - Streeter, David
AU - Park, Michele
AU - Terluin, Berend
AU - Gajewski, Byron
N1 - Funding Information: This work was supported by the University of Kansas Cancer Center (P30 CA168524) and the University of Kansas Biostatistics & Informatics Shared Resources. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or NCATS. Funding Information: This work was supported by the University of Kansas Cancer Center (P30 CA168524) and the University of Kansas Biostatistics & Informatics Shared Resources. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or NCATS. Publisher Copyright: © 2021
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Clinical trials
KW - Item Response Theory
KW - PROMIS
KW - Patient reported outcomes
KW - Power
KW - Quality of life
UR - http://www.scopus.com/inward/record.url?scp=85119035528&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.jclinepi.2021.10.002
DO - https://doi.org/10.1016/j.jclinepi.2021.10.002
M3 - Article
C2 - 34648941
SN - 0895-4356
VL - 141
SP - 141
EP - 148
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
ER -