Elwyn, G., Crowe, S., Fenton, M., Firkins, L., Versnel, J., Walker, S., Cook, I., Holgate, S., Higgins, B., & Gelder, C. (2010). Identifying and prioritizing uncertainties: Patient and clinician engagement in the identification of research questions. Journal of Evaluation in Clinical Practice, 16(3), 627–631. https://doi.org/10.1111/j.1365-2753.2009.01262.x
Fleurence, R. L., Forsythe, L. P., Lauer, M., Rotter, J., Ioannidis, J. P. A., Beal, A., Frank, L., & Selby, J. V. (2014). Engaging patients and stakeholders in research proposal review: The Patient-Centered Outcomes Research Institute. In Annals of Internal Medicine (Vol. 161, Issue 2, pp. 122–130). American College of Physicians. https://doi.org/10.7326/M13-2412
Lloyd, K., & White, J. (2011). Lloyd and White, 2011, democratizing clinical research. Nature, 474, 277–278. https://doi.org/10.1038/474277a
Article CAS PubMed Google Scholar
Sacristán, J. A., Aguarón, A., Avendaño-Solá, C., Garrido, P., Carrión, J., Gutiérrez, A., Kroes, R., & Flores, A. (2016). Patient involvement in clinical research: Why, when, and how. Patient preference and adherence (Vol. 10, pp. 631–640). Dove Medical Press Ltd. https://doi.org/10.2147/PPA.S104259
van der Scheer, L., Garcia, E., van der Laan, A. L., van der Burg, S., & Boenink, M. (2017). The benefits of patient involvement for Translational Research. Health Care Analysis, 25(3), 225–241. https://doi.org/10.1007/s10728-014-0289-0
Brundage, M., Blazeby, J., Revicki, D., Bass, B., De Vet, H., Duffy, H., Efficace, F., King, M., Lam, C. L. K., Moher, D., Scott, J., Sloan, J., Snyder, C., Yount, S., & Calvert, M. (2013). Patient-reported outcomes in randomized clinical trials: Development of ISOQOL reporting standards. Quality of Life Research, 22(6), 1161–1175. https://doi.org/10.1007/s11136-012-0252-1
Cappelleri, J. C., Lundy, J. J., & Hays, R. D. (2014). Overview of classical test theory and item response theory for the quantitative assessment of items in developing patient-reported outcomes measures. Clinical Therapeutics, 36(5), 648–662. https://doi.org/10.1016/j.clinthera.2014.04.006
Article PubMed PubMed Central Google Scholar
Center for Drug Evaluation and Research (CDER), & Center for Biologics Evaluation and Research (CBER) (2020). Patient-Focused Drug Development: Collecting Comprehensive and Representative Input Guidance for Industry, Food and Drug Administration Staff, and Other Stakeholders. https://www.fda.gov/drugs/guidance-compliance-regulatory-information/guidances-drugsand/or
Center for Drug Evaluation and Research (CDER), & Center for Biologics Evaluation and Research (CBER) (2022). Patient-Focused Drug Development: Methods to Identify What Is Important to Patients Guidance for Industry, Food and Drug Administration Staff, and Other Stakeholders. https://www.fda.gov/drugs/guidance-compliance-regulatory-information/guidances-drugsand/or
Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER), & Center for Devices and Radiological Health (CDRH) (2022). Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments Guidance for Industry, Food and Drug Administration Staff, and Other Stakeholders. https://www.fda.gov/vaccines-blood-biologics/guidance-compliance-regulatory-information-biologics/biologics-guidances
Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER), & Center for Devices and Radiological Health (CDRH) (2023). Patient-Focused Drug Development: Incorporating Clinical Outcome Assessments Into Endpoints For Regulatory Decision-Making Guidance for Industry, Food and Drug Administration Staff, and Other Stakeholders. https://www.fda.gov/drugs/guidance-compliance-regulatory-information/guidances-drugsand/or
Gnanasakthy, A., Mordin, M., Clark, M., Demuro, C., Fehnel, S., & Copley-Merriman, C. (2012). A review of patient-reported outcome labels in the United States: 2006 to 2010. Value in Health, 15(3), 437–442. https://doi.org/10.1016/j.jval.2011.11.032
Gnanasakthy, A., Barrett, A., Evans, E., D’Alessio, D., Romano, C., & (De, M. (2019). A review of patient-reported outcomes labeling for Oncology drugs approved by the FDA and the EMA (2012–2016). Value in Health, 22(2), 203–209. https://doi.org/10.1016/j.jval.2018.09.2842
Mercieca-Bebber, R., King, M. T., Calvert, M. J., Stockler, M. R., & Friedlander, M. (2018). The importance of patient-reported outcomes in clinical trials and strategies for future optimization. Patient Related Outcome Measures, 9, 353–367. https://doi.org/10.2147/prom.s156279
Article PubMed PubMed Central Google Scholar
Petrillo, J., Cano, S. J., McLeod, L. D., & Coon, C. D. (2015). Using classical test theory, item response theory, and rasch measurement theory to evaluate patient-reported outcome measures: A comparison of worked examples. Value in Health, 18(1), 25–34. https://doi.org/10.1016/j.jval.2014.10.005
Stover, A. M., McLeod, L. D., Langer, M. M., Chen, W. H., & Reeve, B. B. (2019). State of the psychometric methods: Patient-reported outcome measure development and refinement using item response theory. Journal of Patient-Reported Outcomes, 3(1). https://doi.org/10.1186/s41687-019-0130-5
Wilson, I. B., & Cleary, P. D. (1995). Linking clinical variables with health-related quality of life: A conceptual model of patient outcomes. Journal of the American Medical Association, 273(1), 59–65. https://jamanetwork.com/
Article CAS PubMed Google Scholar
Turner, R. R., Quittner, A. L., Parasuraman, B. M., Kallich, J. D., & Cleeland, C. S. (2007). Patient-reported outcomes: Instrument development and selection issues. Value in Health, 10(SUPPL. 2). https://doi.org/10.1111/j.1524-4733.2007.00271.x
McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates, Inc.
Thissen, D., & Wainer, H. (Eds.). (n.d.-u). Test scoring. Lawrence Erlbaum Associates, Inc., Publishers.
Frost, M. H., Reeve, B. B., Liepa, A. M., Stauffer, J. W., Hays, R. D., & Sloan, J. A. (2007). What is sufficient evidence for the reliability and validity of patient-reported outcome measures? Value in Health, 10(SUPPL. 2). https://doi.org/10.1111/j.1524-4733.2007.00272.x
Morga, A., Dibenedetto, S., Adiutori, R., & Su, J. (2023). Patient-reported outcomes validated in phase 3 clinical trials: A targeted literature review. Current Medical Research and Opinion (Vol. 39, pp. 955–962). Taylor and Francis Ltd. https://doi.org/10.1080/03007995.2023.2224164
Dai, S., Vo, T. T., Kehinde, O. J., He, H., Xue, Y., Demir, C., & Wang, X. (2021). Performance of Polytomous IRT Models With Rating Scale Data: An Investigation Over Sample Size, Instrument Length, and Missing Data. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.721963
Doostfatemeh, M., Taghi Ayatollah, S. M., & 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. https://doi.org/10.1016/j.jval.2016.03.1857
Reise, S. P., & Yu, J. (1990). Parameter recovery in the graded response model using MULTILOG. Journal of Educational Measurement, 27(2), 133–144. https://doi.org/10.1111/j.1745-3984.1990.tb00738.x
Reeve, B. B., Hays, R. D., Chang, C. H., & Perfetto, E. M. (2007). Applying item response theory to enhance health outcomes assessment. Quality of Life Research, 16(SUPPL. 1), 1–3. https://doi.org/10.1007/s11136-007-9220-6
Mercieca-Bebber, R., Palmer, M. J., Brundage, M., Calvert, M., Stockler, M. R., & King, M. T. (2016). Design, implementation and reporting strategies to reduce the instance and impact of missing patient-reported outcome (PRO) data: A systematic review. British Medical Journal Open, 6(6). https://doi.org/10.1136/bmjopen-2015
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement. https://doi.org/10.1002/j.2333-8504.1968.tb00153.x
Masters, G. N. (1982). A Rasch model for partial credit scoring. PSYCHOMETRIKA, 47(2).
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests.
Andrich, D. (2004). Controversy and the Rasch model: A characteristic of incompatible paradigms? Medical Care, I7–I16.
Masters, G. N. (2016). Partial Credit Model. In Handbook of Item Response Theory (pp. 137–154). Chapman and Hall/CRC. https://doi.org/10.1201/9781315374512
Nguyen, T. H., Han, H. R., Kim, M. T., & Chan, K. S. (2014). An introduction to item response theory for patient-reported outcome measurement. The Patient – Patient-Centered Outcomes Research, 7, 23–35. https://doi.org/10.1007/s40271-013-0041-0
DeMars, C. (2010). Assumptions. In Item response theory (pp. 38–60). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195377033.003.0003
Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16, 159–l76. https://doi.org/10.1177/014662169201600206
Muraki, E., & Muraki, M. (2016). Generalized partial credit model. In Handbook of item response theory (pp. 127–137). Chapman and Hall/CRC. https://doi.org/10.1201/9781315374512
R Core Team. (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/
Chalmers, R. P. (2012). Mirt: A Multidimensional Item Response Theory Package for the R environment. Journal of Statistical Software, 48(6). https://doi.org/10.18637/jss.v048.i06
Cai, L., & Monroe, S. (2014). A New Statistic for Evaluating Item Response Theory Models for Ordinal Data. CRESST Report 839.
Tucker, L., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10. https://doi.org/10.1007/BF02291170
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage.
Maccallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample Size in Factor Analysis. In Psychological Methods (Vol. 4, Issue 1).
Chen, W. H., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22(3), 265–289. https://doi.org/10.3102/10769986022003265
Orlando, M., & Thissen, D. (2000). Likelihood-based item-fit indices for dichotomous item response theory models. Applied Psychological Measurement, 24(1), 50–64. https://doi.org/10.1177/01466216000241003
Orlando, M., & Thissen, D. (2003). Further investigation of the performance of S - X2: An Item Fit Index for Use with Dichotomous Item Response Theory models. Applied Psychological Measurement, 27(4), 289–298. https://doi.org/10.1177/0146621603027004004
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037//0033-2909.112.1.155
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587
Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561–573.
Darrell Bock, R. (1972). Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika, 37(1), 29–51.
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