Skip to main content
Mijn bibliotheek
Shop
Nieuws Boeken Tijdschriften Toetsvragen Web-tv Video Audio
Tips voor Mijn BSL
Inloggen

Welkom bij Scalda & Bohn Stafleu van Loghum

Scalda heeft ervoor gezorgd dat je Mijn BSL eenvoudig en snel kunt raadplegen.Je kunt de producten hieronder links aanschaffen en rechts inloggen.

» Andere revante producten voor Scalda studenten en medewerkers.

Registreer

Schaf de BSL Academy aan: 

BSL Academy mbo AG

Eenmaal aangeschaft kun je thuis, of waar ook ter wereld toegang krijgen tot Mijn BSL.

Heb je een vraag, neem dan contact op met Jan van der Velden.

Login

Als u al geregistreerd bent, hoeft u alleen maar in te loggen om onbeperkt toegang te krijgen tot Mijn BSL.

Inloggen
Top
Quality of Life Research
Gepubliceerd in:

08-01-2025

Bayesian item response theory to estimate power in clinical trials with patient-reported outcomes as endpoints

Auteurs: Xiaohang Mei, Joseph C. Cappelleri, Jinxiang Hu

Gepubliceerd in: Quality of Life Research | Uitgave 4/2025

Log in om toegang te krijgen
share
DELEN

Deel dit onderdeel of sectie (kopieer de link)

  • Optie A:
    Klik op de rechtermuisknop op de link en selecteer de optie “linkadres kopiëren”
  • Optie B:
    Deel de link per e-mail
    Link delen
publish
Publiceer nu

Abstract

Purpose

Patient-Reported Outcomes (PROs) are widely used in clinical trials, epidemiological research, quality of life (QOL) studies, routine clinical care, and medical surveillance. The Patient Reported Outcomes Measurement Information System (PROMIS) is a system of reliable and standardized measures of PROs developed with Item Response Theory (IRT) using latent scores. Power estimation is critical to clinical trials and research designs. However, in clinical trials with PROs as endpoints, observed scores are often used to calculate power rather than latent scores.

Methods

In this paper, we conducted a series of simulations to compare the power obtained with IRT latent scores, including Bayesian IRT, Frequentist IRT, and observed scores, focusing on small sample size common in pilot studies and Phase I/II trials. Taking the PROMIS depression measures as an example, we simulated data and estimated power for two-armed clinical trials manipulating the following factors: sample size, effect size, and number of items. We also examined how misspecification of effect size affected power estimation.

Results

Our results showed that the Bayesian IRT, which incorporated prior information into latent score estimation, yielded the highest power, especially when sample size was small. The effect of misspecification diminished as sample size increased.

Conclusion

For power estimation in two-armed clinical trials with standardized PRO endpoints, if a medium effect size or larger is expected, we recommend BIRT simulation with well-grounded informative priors and a total sample size of at least 40.
vorige artikel Long‑term physical and mental Health-Related Quality of Life in Italian patients post COVID-19 hospitalisation
volgende artikel Investigating item response theory model performance in the context of evaluating clinical outcome assessments in clinical trials
Bijlagen
Alleen toegankelijk voor geautoriseerde gebruikers
Literatuur
1.
Food, U., & Administration, D. (2009). Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims. Fed Regist, 65132.
2.
Lohr, K. N., & Zebrack, B. J. (2009). Using patient-reported outcomes in clinical practice: Challenges and opportunities. Quality of Life Research, 18, 99–107.CrossRefPubMed
3.
Rothman, M. L., Beltran, P., Cappelleri, J. C., Lipscomb, J., & Teschendorf, B. (2007). Patient-reported outcomes: Conceptual issues. Value in Health, 10, S66–S75.CrossRefPubMed
4.
5.
Julious, S. A., George, S., Machin, D., & Stephens, R. J. (1997). Sample sizes for randomized trials measuring quality of life in cancer patients. Quality of Life Research, 6, 109-117CrossRefPubMed
6.
Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., & Cella, D. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, anxiety, and anger. Assessment, 18(3), 263–283.CrossRefPubMedPubMedCentral
7.
Nicklin, J., Cramp, F., Kirwan, J., Urban, M., & Hewlett, S. (2010). Collaboration with patients in the design of patient-reported outcome measures: Capturing the experience of fatigue in rheumatoid arthritis. Arthritis Care & Research, 62(11), 1552–1558.CrossRef
8.
Gotay, C. C., Kawamoto, C. T., Bottomley, A., & Efficace, F. (2008). The prognostic significance of patient-reported outcomes in cancer clinical trials. Journal of Clinical Oncology, 26(8), 1355–1363.CrossRefPubMed
9.
10.
Broderick, J. E., DeWitt, E. M., Rothrock, N., Crane, P. K., & Forrest, C. B. (2013). Advances in patient-reported outcomes: The NIH PROMIS® measures. Egems, 1(1).
11.
Spiegel, B. M., Hays, R. D., Bolus, R., Melmed, G. Y., Chang, L., Whitman, C., Khanna, P. P., Paz, S. H., Hays, T., & Reise, S. (2014). Development of the NIH patient-reported outcomes measurement information system (PROMIS) gastrointestinal symptom scales. The American Journal of Gastroenterology, 109(11), 1804.CrossRefPubMedPubMedCentral
12.
Cella, D., Beaumont, J. L., Webster, K. A., Lai, J.-S., & Elting, L. (2006). Measuring the concerns of cancer patients with low platelet counts: The Functional Assessment of Cancer Therapy-Thrombocytopenia (FACT-Th) questionnaire. Supportive Care in Cancer, 14, 1220–1231.CrossRefPubMed
13.
Garcia, S. F., Cella, D., Clauser, S. B., Flynn, K. E., Lad, T., Lai, J.-S., Reeve, B. B., Smith, A. W., Stone, A. A., & Weinfurt, K. (2007). Standardizing patient-reported outcomes assessment in cancer clinical trials: A patient-reported outcomes measurement information system initiative. Journal of Clinical Oncology, 25(32), 5106–5112.CrossRefPubMed
14.
Frost, M. H., Reeve, B. B., Liepa, A. M., Stauffer, J. W., & Hays, R. D. (2007). What is sufficient evidence for the reliability and validity of patient-reported outcome measures? Value in Health, 10, S94–S105.CrossRefPubMed
15.
Tunis, S. R., Stryer, D. B., & Clancy, C. M. (2003). Practical clinical trials: Increasing the value of clinical research for decision making in clinical and health policy. JAMA, 290(12), 1624–1632.CrossRefPubMed
16.
Edelen, M. O., & Reeve, B. B. (2007). Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, 16, 5–18.CrossRefPubMed
17.
Bjorner, J., Petersen, M. A., Groenvold, M., Aaronson, N., Ahlner-Elmqvist, M., Arraras, J., Brédart, A., Fayers, P., Jordhoy, M., & Sprangers, M. (2004). Use of item response theory to develop a shortened version of the EORTC QLQ-C30 emotional functioning scale. Quality of Life Research, 13, 1683–1697.CrossRefPubMed
18.
Van Der Linden, W. J., & Hambleton, R. K. (1997). Item response theory: Brief history, common models, and extensions. In Handbook of modern item response theory (pp. 1–28). Springer.
19.
Reise, S. P., & Waller, N. G. (2009). Item response theory and clinical measurement. Annual Review of Clinical Psychology, 5, 27–48.CrossRefPubMed
20.
Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15, 625–632.CrossRefPubMed
21.
An, X., & Yung, Y.-F. (2014). Item response theory: What it is and how you can use the IRT procedure to apply it. SAS Institute Inc. SAS364-2014, 10(4), 1–14.
22.
Holman, R., Glas, C. A., & de Haan, R. J. (2003). Power analysis in randomized clinical trials based on item response theory. Controlled Clinical Trials, 24(4), 390–410.CrossRefPubMed
23.
Thissen, D., & Steinberg, L. (2009). Item response theory. The Sage Handbook of Quantitative Methods in Psychology, 148–177.
24.
Kean, J., & Reilly, J. (2014). Item response theory. In Hammond, F. M., Malec, J. M., Nick, T. G., & Buschbacher, R. M. (Eds.), Handbook for clinical research: Design, statistics and implementation (pp. 195–198).
25.
Nguyen, T. H., Han, H.-R., Kim, M. T., & Chan, K. S. (2014). An introduction to item response theory for patient-reported outcome measurement. Patient-Patient-Centered Outcomes Research, 7, 23–35.CrossRefPubMed
26.
Kohli, N., Koran, J., & Henn, L. (2015). Relationships among classical test theory and item response theory frameworks via factor analytic models. Educational and Psychological Measurement, 75(3), 389–405.CrossRefPubMed
27.
Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46(4), 443–459.CrossRef
28.
Bock, R. D., Gibbons, R., & Muraki, E. (1988). Full-information item factor analysis. Applied Psychological Measurement, 12(3), 261–280.CrossRef
29.
Lord, F. M. (1986). Maximum likelihood and Bayesian parameter estimation in item response theory. Journal of Educational Measurement, 157–162.
30.
Fox, J.-P. (2010). Bayesian item response modeling: Theory and applications. Springer.
31.
Finch, H., & French, B. F. (2019). A comparison of estimation techniques for IRT models with small samples. Applied Measurement in Education, 32(2), 77–96.CrossRef
32.
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometric Society, 34, 1–97.CrossRef
33.
Samejima, F. (1997). Graded response model. In Handbook of modern item response theory (pp. 85–100). Springer.
34.
Embretson, S. E., & Reise, S. P. (2013). Item response theory. Psychology Press.CrossRef
35.
Lameijer, C., Van Bruggen, S., Haan, E., Van Deurzen, D., Van der Elst, K., Stouten, V., Kaat, A., Roorda, L., & Terwee, C. (2020). Graded response model fit, measurement invariance and (comparative) precision of the Dutch-Flemish PROMIS® Upper Extremity V2.0 item bank in patients with upper extremity disorders. BMC Musculoskeletal Disorders, 21(1), 1–17.CrossRef
36.
Luo, Y., & Jiao, H. (2018). Using the Stan program for Bayesian item response theory. Educational and Psychological Measurement, 78(3), 384–408.CrossRefPubMed
37.
38.
Team, S. D. (2020). RStan: The R interface to Stan. R package version 2.17. 3.
39.
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M. A., Guo, J., Li, P., & Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76.
40.
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P.-C. (2021). Rank-normalization, folding, and localization: An improved R ̂ for assessing convergence of MCMC (with discussion). Bayesian Analysis, 16(2), 667–718.CrossRef
41.
Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472.CrossRef
42.
43.
Ree, M. J., & Carretta, T. R. (2006). The role of measurement error in familiar statistics. Organizational Research Methods, 9(1), 99–112.CrossRef
44.
McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling: A Multidisciplinary Journal, 23(5), 750–773.CrossRef
45.
Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48, 1–29.CrossRef
46.
47.
Murray, J. V., Goldizen, A. W., O’Leary, R. A., McAlpine, C. A., Possingham, H. P., & Choy, S. L. (2009). How useful is expert opinion for predicting the distribution of a species within and beyond the region of expertise? A case study using brush-tailed rock-wallabies Petrogale penicillata. Journal of Applied Ecology, 46(4), 842–851.CrossRef
48.
Martin, T. G., Burgman, M. A., Fidler, F., Kuhnert, P. M., Low-Choy, S., McBride, M., & Mengersen, K. (2012). Eliciting expert knowledge in conservation science. Conservation Biology, 26(1), 29–38.CrossRefPubMed
49.
Morris, W. K., Vesk, P. A., McCarthy, M. A., Bunyavejchewin, S., & Baker, P. J. (2015). The neglected tool in the Bayesian ecologist’s shed: A case study testing informative priors’ effect on model accuracy. Ecology and Evolution, 5(1), 102–108.CrossRefPubMed
50.
Hu, J., Thompson, J., Mudaranthakam, D. P., Hinton, L. C., Streeter, D., Park, M., Terluin, B., & Gajewski, B. (2022). Estimating power for clinical trials with patient reported outcomes-using item response theory. Journal of Clinical Epidemiology, 141, 141–148.CrossRefPubMed
51.
Doostfatemeh, M., Ayatollah, S. M. T., & Jafari, P. (2016). Power and sample size calculations in clinical trials with patient-reported outcomes under equal and unequal group sizes based on graded response model: A simulation study. Value in Health, 19(5), 639–647.CrossRefPubMed
52.
You, H. (2022). Bayesian versus frequentist estimation for item response theory models of interdisciplinary science assessment. Interdisciplinary Journal of Environmental and Science Education, 18(4), e2297.CrossRef
53.
Chen, D.-G., & Fraser, M. W. (2017). A Bayesian approach to sample size estimation and the decision to continue program development in intervention research. Journal of the Society for Social Work and Research, 8(3), 457–470.CrossRef
54.
Zondervan-Zwijnenburg, M., Depaoli, S., Peeters, M., & Van De Schoot, R. (2018). Pushing the limits. Methodology.
Metagegevens
Titel
Bayesian item response theory to estimate power in clinical trials with patient-reported outcomes as endpoints
Auteurs
Xiaohang Mei
Joseph C. Cappelleri
Jinxiang Hu
Publicatiedatum
08-01-2025
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 4/2025
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-024-03874-y

Andere artikelen Uitgave 4/2025

Qualitative evidence on EQ-5D-5L skin irritation and self-confidence bolt-ons in Darier’s disease and Hailey-Hailey disease

  • Open Access

Comparison of patient-reported symptoms with multi-item patient-reported outcome measures of fatigue, anxiety, and depression in the clinical care of women undergoing chemotherapy for early breast cancer

Correction: Understanding the impact of early onset colorectal cancer on quality of life: a qualitative analysis of online forum data

  • Open Access
  • Correction

Long‑term physical and mental Health-Related Quality of Life in Italian patients post COVID-19 hospitalisation

  • Open Access

A qualitative systematic review of the impact of hearing on quality of life

  • Open Access
  • Review

Validation of the FROM-16 in family members of patients receiving advanced therapy medicinal product (ATMP)

  • Open Access