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.
Rothman, M. L., Beltran, P., Cappelleri, J. C., Lipscomb, J., & Teschendorf, B. (2007). Patient-reported outcomes: Conceptual issues. Value in Health, 10, S66–S75.
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.
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.
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.
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.
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.
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.
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.
Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15, 625–632.
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.
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.
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.
Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46(4), 443–459.
Bock, R. D., Gibbons, R., & Muraki, E. (1988). Full-information item factor analysis. Applied Psychological Measurement, 12(3), 261–280.
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.
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometric Society, 34, 1–97.
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.
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.
Luo, Y., & Jiao, H. (2018). Using the Stan program for Bayesian item response theory. Educational and Psychological Measurement, 78(3), 384–408.
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.
Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472.
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.
McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling: A Multidisciplinary Journal, 23(5), 750–773.
Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48, 1–29.
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.
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.
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.
留言 (0)