Using R and WinBUGS to fit a generalized partial credit model for developing and evaluating patient-reported outcomes assessments

Statistics in Medicine

Volumn 31, Issue 18, 2012, Pages 2010-2026

  Li, Yuelin ^a   Baser, Ray ^a  

^a MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

Author keywords

Generalized partial credit model; Item response theory; Rasch model

Indexed keywords

ACCURACY; ARTICLE; CALCULATION; DISEASE SEVERITY; GENERALIZED PARTIAL CREDIT MODEL; MATHEMATICAL MODEL; NEUROSIS; PATIENT ASSESSMENT; PATIENT INFORMATION; PROBABILITY; PSYCHOLOGIC ASSESSMENT; PSYCHOMETRY; SELF REPORT; STATISTICAL ANALYSIS;

BAYES THEOREM; DATA INTERPRETATION, STATISTICAL; HUMANS; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; OUTCOME ASSESSMENT (HEALTH CARE); QUESTIONNAIRES; SOFTWARE;

EID: 84864009552     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4475     Document Type: Article

References (72)

1. de Ayala RJ. The Theory and Practice of Item Response Theory. The Guilford Press: New York, NY, 2009.
2. Embretson SE, Reise SP. Item Response Theory for Psychologists. LEA: Mahwah, NJ, 2000.
3. Lord FM. Application of Item Response Theory to Practical Testing Problems. Erlbaum: Hillsdale, NJ, 1980.
4. van der Linden W, Hambleton RK. Handbook of Modern Item Response Theory. Springer-Verlag: New York, 1997.
5. Baker FB, Kim S-H. Item Response Theory: Parameter Estimation Techniques. Marcel Dekker, Inc.: New York, 2004.
6. Bjorner JB, Chang CH, Thissen D, Reeve BB. Developing tailored instruments: item banking and computerized adaptive assessment. Quality of Life Research 2007; 16 Suppl 1:95-108.
7. Reeve BB, Hays RD, Bjorner JB, Cook KF, Crane PK, Teresi JA, Thissen D, Revicki DA, Weiss DJ, Hambleton RK, Liu H, Gershon R, Reise SP, Lai JS, Cella D. Psychometric evaluation and calibration of health-related quality of life item banks: plans for the patient-reported outcomes measurement information system (PROMIS). Medical Care 2007; 45:S22-31.
8. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S, Amtmann D, Bode R, Buysse D, Choi S, Cook K, Devellis R, DeWalt D, Fries JF, Gershon R, Hahn EA, Lai JS, Pilkonis P, Revicki D, Rose M, Weinfurt K, Hays R. The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. Journal of Clinical Epidemiology 2010; 63(11):1179-94.
9. Baldwin P, Bernstein J, Wainer H. Hip psychometrics. Statistics in Medicine 2009; 28:2277-2292.
10. Li H, Hong F. Cluster-Rasch models for microarray gene expression data. Genome Biology 2001; 2(8):RESEARCH0031.
11. R Development Core Team. R: a language and environment for statistical computing, R Foundation for Statistical Computing: Vienna, Austria, 2010., ISBN 3-900051-07-0.
12. Lunn DJ, Thomas A, Best N, Spiegelhalter D. Winbugs - a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 2000; 10:325-337.
13. Food and Drug Administration. Guidance for industry. Patient-reported outcome measures: use in medical product development to support labeling claims, 2009. Last accessed: December, 2009.
14. Food and Drug Administration. Guidance for industry. Patient-reported outcome measures: use in medical product development to support labeling claims, 2006. Last accessed: December, 2009.
15. European Medicines Agency Committee for medicinal products for human use (CHMP). Reflection paper on the regulatory guidance for the use of health-related quality of life (HRQL) measures in the evaluation of medicinal products, 2005. Last accessed: December, 2009).
16. Rasch G. Probabilistic Models for Some Intelligence and Attainment Tests. The University of Chicago Press: Chicago, 1980.
17. Weesie J. The Rasch model in Stata, 1999., Last accessed: December, 2009.
18. Agresti A. Categorical Data Analysis. Wiley: Hoboken, NJ, 2002.
19. Muraki E. A generalized partial credit model: application of an em algorithm. Applied Psychological Measurement 1992; 16:159-176.
20. Fox JP. Bayesian Item Response Modeling: Theory and Applications. Springer: New York, 2010.
21. Curtis SM. Bugs code for item response theory. Journal of Statistical Software 2010; 36:1-34.
22. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of complexity and fit. Journal of the Royal Statistical Society, SeriesB 2002; 64:583-639.
23. Masters GN. A Rasch model for partial credit scoring. Psychometrika 1982; 47(2):149-174.
24. Thissen D. Marginal maximum likelihood estimation for the one-parameter logistic model. Psychometrika 1982; 47(2):175-186.
25. Birnbaum A. Some latent trait models and their use in inferring an examinee's ability. In Statistical Theories of Mental Test Scores, Lord FM, Novick MR (eds). Addison-Wesley: Reading, MA, 1968.
26. Thissen D, Steinberg L. A taxonomy of item response models. Psychometrika 1986; 51(4):567-577.
27. Lord FM, Novick MR (eds). Statistical Theories of Mental Test Scores. Addison-Wesley: Reading, MA, 1968.
28. Bock RD, Aitkin M. Marginal maximum likelihood estimation of item parameters: application of an EM algorithm. Psychometrika 1981; 46(4):443-459.
29. Albert JH. Bayesian estimation of normal ogive item response curves using Gibbs sampling. Journal of Educational Statistics 1992; 17:251-269.
30. Bafumi J, Gelman A, Park DK, Kaplan N. Practical issues in implementing and understanding Bayesian ideal point estimation. Political Analysis 2005; 13(2):171-187.
31. Lee PM. Bayesian Statistics: An Introduction, 3rd ed. Arnold Publishers: London, 2004.
32. Patz RJ, Junker BW. Applications and extensions of MCMC in IRT: multiple item types, missing data, and rated responses. Journal of Educational and Behavioral Statistics 1999; 24(4):342-366.
33. de la Torre J, Stark S, Chernyshenko OS. Markov chain Monte Carlo estimation of item parameters for the generalized graded unfolding model. Applied Psychological Measurement 2006; 30(3):216-232.
34. Martin AD, Quinn KM, Park JH. MCMCpack: Markov chain Monte Carlo (MCMC) package, 2010.
35. Congdon P. Bayesian Statistical Modeling. John Wiley & Sons Ltd.: Chichester, England, 2006.
36. Revelle W. psych: Procedures for psychological, psychometric, and personality research, Northwestern University: Evanston, Illinois, 2010., Rpackageversion1.0-90.
37. Goldberg LR. The development of markers for the big-five factor structure. Psychological Assessment 1992; 4:26-42.
38. Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Statistical Science 1992; 7(4):457-511.
39. Gelman A, Hill J. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press: New York, 2007.
40. Jackman S. Bayesian Analysis for the Social Sciences. John Wiley & Sons, Ltd: Chichester, United Kingdom, 2009.
41. Ntzoufras I. Bayesian Modeling Using WinBUGS. John Wiley & Sons, Inc.: Hoboken, NJ, 2009.
42. Brooks SP, Roberts GO. Assessing convergence of Markov chain Monte Carlo algorithms. Statistics and Computing 1998; 8(319-335).
43. Cowles MK, Carlin BP. Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association 1996; 91:883-904.
44. Geweke J. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In Bayesian Statistics, Vol. 4, Bernardo J, Berger J, Dawid A, Smith A (eds). Claredon Press: Oxford, UK, 1992.
45. Heidelberger P, Welch PD. A spectral method for confidence interval generation and run length control in simulations. Communication of the ACM 1981; 24:233-245.
46. Raftery A, Lewis S. How many iterations in the Gibbs sampler? In Bayesian Statistics, Vol. 4, Bernardo J, Berger J, Dawid A, Smith A (eds). Claredon Press: Oxford, UK. 763-774, 1992.
47. Nunnally JC, Bernstein IH. Psychometric Theory, 3rd ed. McGraw-Hill: New York, 1994.
48. Bond TG, Fox CM. Applying the Rasch Model: Fundamental Measurement in the Human Sciences. Lawrence Erlbaum Associates: Mahwah, NJ, 2001.
49. Karabatsos G. Comparing the aberrant response detection performance of thirty-six person-fit statistics. Applied Measurement in Education 2003; 16:277-298.
50. Meijer RR, Sijtsma K. Methodology review: evaluating person fit. Applied Psychological Measurement 2001; 25:107-135.
51. Muraki E. Information functions of the generalized partial credit model. Applied Psychological Measurement 1993; 17:351-363.
52. Samejima F. Estimation of latent ability using a response pattern of graded scores, 1969. Psychometrika Monograph, No. 17. Richmond, VA: Psychometric Society. Retrieved in August, 2011 from.
53. Hogg RV, Craig AT. Introduction to Mathematical Statistics. Prentice Hall: Englewood Cliffs, NJ, 1995.
54. Samejima F. Some critical observations of the test information function as a measure of local accuracy in ability. Psychometrika 1994; 59(3):307-329.
55. Samejima F. A general model for free-response data, 1972. Psychometrika Monograph, No. 18, 37 (1, Pt. 2).
56. Juniper EF, Guyatt GH, Feeny DH, Griffith LE, Ferrie PJ. Minimum skills required by children to complete health-related quality of life instruments for asthma: comparison of measurement properties. European Respiratory Journal 1997; 10(10):2285-2294. English.
57. Downing SM. Validity: on meaningful interpretation of assessment data. Medical Education 2003; 37(9):830-837.
58. De Boeck P, Wilson M. Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach. Spinger-Verlag: New York, 2004.
59. dela Torre J, Douglas JA. Model evaluation and multiple strategies in cognitive diagnosis: an analysis of fraction subtraction data. Psychometrika 2008; 73:595-624.
60. Junker BW, Sijtsma K. Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement 2001; 25:258-272.
61. Plummer M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), March 20 - 22, Vienna, Austria, 2003.
62. Lee S-H, Terry R. IRT-FIT: SAS macros for fitting item response theory (IRT) models. In Proceedings of the SAS Users Group International (SUGI 30) Conference, April 9-13, 2005, Philadelphia, PA, 2005.
63. Lee S-H, Terry R. MDIRT-FIT: SAS macros for fitting multidimensional item response, In Proceedings of the SAS Users Group International (SUGI 31) Conference, March 26-29, 2006, San Francisco, California, 2011.
64. Pan T, Chen Y. Using proc logistic to estimate the Rasch model. Proceedings of the SAS Global Forum 2011 Conference, April 4 - 7, 2011., Las Vegas, Neveda, 2011.
65. Hardouin JB. Rasch analysis: estimation and tests with raschtest. Stata Journal 2007; 7(1):22-44.
66. Weesie J. The Rasch model in Stata. Stata Corp. 1999. Accessed August 31, 2011, from.
67. Zajonc T. OpenIRT - Bayesian and maximum likelihood estimation of item response theory (IRT) models in Stata, 2011. Accessed August 31, 2011 at.
68. Sheu CF, Chen CT, Su YH, Wang WC. Using SAS PROC NLMIXED to fit item response theory models. Behavioral Research Methods 2005; 37:202-218.
69. Fisher RA. Theory of statistical estimation. Mathematical Proceedings of the Cambridge Philosophical Society 1925; 22:700-725.
70. Dodd BG, Koch WR. Effects of variations in item step values on item and test information in the partial credit model. Applied Psychological Measurement 1987; 11(4):371-384.
71. Berger JO. Statistical Decision Theory and Bayesian Analysis. Springer-Verlag: New York, 1985.
72. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis, 2nd ed. Chapman & Hall: New York, 2003.