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

Food, U., & Administration, D. (2009). Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims. Fed Regist, 65132.

Lohr, K. N., & Zebrack, B. J. (2009). Using patient-reported outcomes in clinical practice: Challenges and opportunities. Quality of Life Research, 18, 99–107.

Article  PubMed  Google Scholar 

Rothman, M. L., Beltran, P., Cappelleri, J. C., Lipscomb, J., & Teschendorf, B. (2007). Patient-reported outcomes: Conceptual issues. Value in Health, 10, S66–S75.

Article  PubMed  Google Scholar 

Deshpande, P. R., Rajan, S., Sudeepthi, B. L., & Nazir, C. A. (2011). Patient-reported outcomes: a new era in clinical research. Perspectives in Clinical Research, 2(4), 137. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3227331/pdf/PCR-2-137.pdf

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, 109-117

Article  CAS  PubMed  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

Patient-reported outcomes measurement information system (PROMIS). Retrieved February 23, from https://www.commonfund.nih.gov/promis/index

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).

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.

Article  PubMed  PubMed Central  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  CAS  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  CAS  PubMed  Google Scholar 

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.

Reise, S. P., & Waller, N. G. (2009). Item response theory and clinical measurement. Annual Review of Clinical Psychology, 5, 27–48.

Article  PubMed  Google Scholar 

Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15, 625–632.

Article  PubMed  Google Scholar 

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.

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.

Article  PubMed  Google Scholar 

Thissen, D., & Steinberg, L. (2009). Item response theory. The Sage Handbook of Quantitative Methods in Psychology, 148–177.

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).

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46(4), 443–459.

Article  Google Scholar 

Bock, R. D., Gibbons, R., & Muraki, E. (1988). Full-information item factor analysis. Applied Psychological Measurement, 12(3), 261–280.

Article  Google Scholar 

Lord, F. M. (1986). Maximum likelihood and Bayesian parameter estimation in item response theory. Journal of Educational Measurement, 157–162.

Fox, J.-P. (2010). Bayesian item response modeling: Theory and applications. Springer.

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.

Article  Google Scholar 

Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometric Society, 34, 1–97.

Article  Google Scholar 

Samejima, F. (1997). Graded response model. In Handbook of modern item response theory (pp. 85–100). Springer.

Embretson, S. E., & Reise, S. P. (2013). Item response theory. Psychology Press.

Book  Google Scholar 

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.

Article  Google Scholar 

Luo, Y., & Jiao, H. (2018). Using the Stan program for Bayesian item response theory. Educational and Psychological Measurement, 78(3), 384–408.

Article  PubMed  Google Scholar 

Bürkner, P.-C. (2019). Bayesian item response modeling in R with brms and Stan. arXiv preprint arXiv:1905.09501.

Team, S. D. (2020). RStan: The R interface to Stan. R package version 2.17. 3.

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.

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.

Article  Google Scholar 

Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472.

Article  Google Scholar 

Assessment Center API. Retrieved October 16, from https://www.assessmentcenter.net/ac_api/demo/defaultv2.htm

Ree, M. J., & Carretta, T. R. (2006). The role of measurement error in familiar statistics. Organizational Research Methods, 9(1), 99–112.

Article  Google Scholar 

McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling: A Multidisciplinary Journal, 23(5), 750–773.

Article  Google Scholar 

Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48, 1–29.

Article  Google Scholar 

R Core Team, R. (2023). R: A language and environment for statistical computing. https://www.R-project.org/

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.

Article  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed 

留言 (0)

沒有登入
gif