An empirical comparison of methods for analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening

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• 作者：Ji Cheng (1) (2)
  Eleanor Pullenayegum (1) (2)
  Deborah A Marshall (3)
  John K Marshall (4)
  Lehana Thabane (1) (2) (5)
  
• 关键词：Discrete choice experiment ; Intra ; class correlation ; Statistical model ; Patient preference
• 刊名：BMC Medical Research Methodology
• 出版年：2012
• 出版时间：December 2012
• 年：2012
• 卷：12
• 期：1
• 全文大小：331KB
• 参考文献：1. Longo MF, Cohen DR, Hood K, Edwards A, Robling M, Elwyn G, Russell IT: Involving patients in primary care consultations: assessing preferences using discrete choice experiments. / Br J Gen Pract 2006,56(522):35-2.
  2. Ryan M, Major K, Skatun D: Using discrete choice experiments to go beyond clinical outcomes when evaluating clinical practice. / J Eval Clin Pract 2005,11(4):328-38. CrossRef
  3. Lancaster KJ: A new approach to consumer theory. / J Polit Econ 1966,74(2):132-57. CrossRef
  4. Montgomery DC: / Design and analysis of experiments. 5th edition. New York: Wiley; 2000.
  5. Louviere J, Hensher D: On the design and analysis of simulated choice or allocation experiments in travel choice modelling. / Transp Res Rec 1982, 890:11-7.
  6. Louviere J, Woodworth G: Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregate data. / J Mark Res 1983, 20:350-67. CrossRef
  7. de Bekker-Grob EW, Ryan M, Gerard K: Discrete choice experiments in health economics: a review of the literature. / Health Econ 2010. doi:10.1002/hec.1697
  8. Ryan M, Gerard K: Using discrete choice experiments to value health care programs: current practice and future research reflections. / Appl Health Econ Health Policy 2003,2(1):55-4.
  9. Marshall DA, Bridges JFP, Hauber B, Cameron RA, Donnalley L, Fyie KA, Johnson FR: Conjoint analysis applications in health--how are studies being designed and reported? an update on current practice in the published literature between 2005 and 2008. / The Patient: Patient-Centered Outcomes Research 2010, 3:249-56. CrossRef
  10. Louviere JJ, Lancsar E: Choice experiments in health: the good, the bad, the ugly and toward a brighter future. / Health Econ Policy Law 2009,4(Pt 4):527-46. CrossRef
  11. Bryan S, Dolan P: Discrete choice experiments in health economics. For better or for worse? / Eur J Health Econ 2004,5(3):199-02. CrossRef
  12. Louviere JJ, Pihlens D, Carson R: Design of discrete choice experiments: a discussion of issues that matter in future applied research. / Journal of Choice Modelling 2010, 4:1-.
  13. Ryan M, Gerard K, Amaya-Amaya M: / Using discrete choice experiments to value health and health care. Dordrecht, The Netherlands: Springer; 2008. CrossRef
  14. Bridges JFP, Hauber B, Marshall DA, Lloyd A, Prosser LA, Regier DA, Johnson FR, Mauskopf J: Conjoint analysis applications in health--a checklist: a report of the ISPOR good research practices for conjoint Analysis task force. / Value Health 2011, 14:403-13. CrossRef
  15. Lancsar E, Louviere J: Conducting discrete choice experiments to inform healthcare decision making: a user's guide. / PharmacoEconomics 2008,26(8):661-77. CrossRef
  16. Mehndiratta SR, Hansen M: Analysis of discrete choice data with repeated observations: comparison of three Techniques in intercity travel Case. / Transp Res Rec 1997, 1607:69. CrossRef
  17. Marshall DA, Johnson FR, Phillips KA, Marshall JK, Thabane L, Kulin NA: Measuring patient preferences for colorectal cancer screening using a choice-format survey. / Value Health 2007,10(5):415-30. CrossRef
  18. Colorectal Cancer Association of Canada [http://www.colorectal-cancer.ca/en/just-the-facts/colorectal/] and [http://www.colorectal-cancer.ca/en/screening/fobt-and-fit/] [] and []
  19. Anonymous From the Centers for Disease Control and Prevention: Colorectal cancer test use among persons aged > or = 50 years-United States, 2001. / JAMA 2003,289(19):2492-493. CrossRef
  20. Walsh JM, Terdiman JP: Colorectal cancer screening: scientific review. / JAMA 2003,289(10):1288-296. CrossRef
  21. Slomski A: Expert panel offers advice to improve screening rates for colorectal cancer. / JAMA 2010,303(14):1356-357. CrossRef
  22. Labianca R, Merelli B: Screening and diagnosis for colorectal cancer: present and future. / Tumori 2010,96(6):889-01.
  23. Kuhfeld WF: Discrete choice (SAS Technical Papers: Marketing research, MR2010F). [http://support.sas.com/techsup/technote/mr2010f.pdf] (Date of last access: January 7, 2012)
  24. Street DJ, Burgess L: Optimal and near-optimal pairs for the estimation of effects in 2-level choice experiments. / Journal of Statistics Planning and Inference 2004, 118:185-99. CrossRef
  25. Louviere J, Flynn F, Carson R: Discrete choice experiments are not conjoint analysis. / Journal of Choice Modelling 2010,2(2):57-2.
  26. Neuhaus JM: Statistical methods for longitudinal and clustered designs with binary responses. / Stat Methods Med Res 1992,1(3):249-73. CrossRef
  27. Pendergast JF, Gange SJ, Newton MA, Lindstrom MJ, Palta M, Fisher MR: A survey of methods for analyzing clustered binary response data. / Int Stat Rev 1998, 64:89-18.
  28. Huber JS, Ervin LH: Using heteroscedastic consistent standard errors in the linear regression model. / Am Stat 2000, 54:795-06.
  29. Rogers W: Regression standard errors in clustered samples. / Stata Technical Bulletin 1994, 3:19-3.
  30. Larsen K, Petersen JH, Budtz-J?rgensen E, Endahl L: Interpreting parameters in the logistic regression model with random effects. / Biometrics 2000, 56:909-14. CrossRef
  31. Hedeker D, Gibbons RD, Flay BR: Random-effects regression models for clustered data with an example from smoking prevention research. / J Consult Clin Psychol 1994,62(4):757-65. CrossRef
  32. Stata online help [http://www.stata.com/help.cgi?xtmelogit]
  33. Rabe-Hesketh S, Skrondal A: / Multilevel and longitudinal modeling using Stata. 2nd edition. USA: A Stata Press Publication; 2008.
  34. Shults J, Sun W, Tu X, Kim H, Amsterdam J, Hilbe JM, Ten-Have T: A comparison of several approaches for choosing between working correlation structures in generalized estimating equation analysis of longitudinal binary data. / Stat Med 2009,28(18):2338-355. CrossRef
  35. Hanley JA, Negassa A, Edwardes MD, Forrester JE: Statistical analysis of correlated data using generalized estimating equations: an orientation. / Am J Epidemiol 2003,157(4):364-75. CrossRef
  36. Ballinger GA: Using generalized estimating equations for longitudinal data analysis. / Organ Res Methods 2004, 7:127-50. CrossRef
  37. Long JS, Freese J: / Regression models for categorical dependent variables using STATA. 2nd edition. Texas, USA: Stata Press; 2006.
  38. Chib S, Greenberg E: Analysis of multivariate probit models. / Biometrika 1998, 85:347-61. CrossRef
  39. Pizzo E, Pezzoli A, Stockbrugger R, Bracci E, Vagnoni E, Gullini S: Screenee perception and health-related quality of life in colorectal cancer screening: a review. / Value Health 2011,14(1):152-59. CrossRef
  40. Cheng S, Long J: Testing for IIA in the multinomial logit model. / Sociological Methods Research 2007,35(4):583-00. CrossRef
  41. Crouchley R, Ganjali M: A comparison of GEE and random effects models for distinguishing heterogeneity, nonstationarity and state dependence in a collection of short binary event series. / Stat Model 2002, 2:39-2. CrossRef
  42. Hedeker D: A mixed-effects multinomial logistic regression model. / Stat Med 2003,22(9):1433-446. CrossRef
  43. Daganzo C: / Multinomial probit: the theory and its application to demand forecasting. New York: Academic; 1979.
  44. Kaplan D, Venezky RL: Literacy and voting behavior: a bivariate probit model with sample selection. / Soc Sci Res 1994, 23:350-67. CrossRef
  45. Watt DJ, Kayis B, Willey K: The relative importance of tender evaluation and contractor selection criteria. / International Journal of Project Management 2010, 28:51-0. CrossRef
  46. Heitman SJ, Hilsden RJ, Au F, Dowden S, Manns BJ: Colorectal cancer screening for average-risk North Americans: an economic evaluation. / PLoS Med 2010,7(11):e1000370. CrossRef
  47. Nelson RL, Schwartz A: A survey of individual preference for colorectal cancer screening technique. / BMC Cancer 2004, 4:76. CrossRef
  48. Campbell MJ, Donner A, Klar N: Developments in cluster randomized trials and Statistics in Medicine. / Stat Med 2007,26(1):2-9. CrossRef
  49. Ma J, Thabane L, Kaczorowski J, Chambers L, Dolovich L, Karwalajtys T, Levitt C: Comparison of Bayesian and classical methods in the analysis of cluster randomized controlled trials with a binary outcome: the Community Hypertension Assessment Trial (CHAT). / BMC Med Res Methodol 2009, 9:37. CrossRef
  50. Schukken YH, Grohn YT, McDermott B, McDermott JJ: Analysis of correlated discrete observations: background, examples and solutions. / Prev Vet Med 2003,59(4):223-40. CrossRef
  51. Neuhaus JM, Kalbfleisch JD, Hauck WW: A comparison of cluster-specific and population-averaged approaches for analyzing correlated binary data. / Int Stat Rev 1991, 59:25-5. CrossRef
  52. Hubbard AE, Ahern J, Fleischer NL, Van der Laan M, Lippman SA, Jewell N, Bruckner T, Satariano WA: To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. / Epidemiology 2010,21(4):467-74. CrossRef
  53. Munizaga MA, Heydecker BG, de Dios Ortúzar J: Representation of heteroskedasticity in discrete choice models. / Transp Res 2000, 34:219-40. CrossRef
  54. Peters TJ, Richards SH, Bankhead CR, Ades AE, Sterne JA: Comparison of methods for analysing cluster randomized trials: an example involving a factorial design. / Int J Epidemiol 2003,32(5):840-46. CrossRef
  55. Heil SF: A review of multilevel and longitudinal modeling using stata. / J Educ Behav Stat 2009, 34:559-60. CrossRef
  56. Burda M, Harding M, Hausman J: A Bayesian mixed logit-probit model for multinomial choice. / J Econ 2008, 147:232-46.
  57. Ferguson S, Olewnik A, Cormier P: / Proceedings of the exploring marketing to engineering information mapping in mass customization: a presentation of ideas, challenges and resulting questions: August 28-1; Washington, DC, USA. USA: ASME; 2011.
  58. Kato H: / Proceedings of the non-linearity of utility function and value of travel time savings: empirical analysis of inter-regional non-business travel mode choice of Japan: September 18-0; Strasbourg. European Transport Conference: France; 2006.
  59. Kjaer T, Bech M, Gyrd-Hansen D, Hart-Hansen K: Ordering effect and price sensitivity in discrete choice experiments: need we worry? / Health Econ 2006,15(11):1217-228. CrossRef
  60. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/12/15/prepub
  
• 作者单位：Ji Cheng (1) (2)
  Eleanor Pullenayegum (1) (2)
  Deborah A Marshall (3)
  John K Marshall (4)
  Lehana Thabane (1) (2) (5)
  
  1. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada
  2. Biostatistics Unit, St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada
  3. Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada
  4. Department of Medicine, Division of Gastroenterology, McMaster University, Hamilton, ON, Canada
  5. Biostatistics Unit/FSORC, St. Joseph’s Healthcare Hamilton, 3rd Floor Martha, Room H325, 50 Charlton Avenue East, Hamilton, ON, L8N 4A6, Canada
  

文摘

Background A discrete choice experiment (DCE) is a preference survey which asks participants to make a choice among product portfolios comparing the key product characteristics by performing several choice tasks. Analyzing DCE data needs to account for within-participant correlation because choices from the same participant are likely to be similar. In this study, we empirically compared some commonly-used statistical methods for analyzing DCE data while accounting for within-participant correlation based on a survey of patient preference for colorectal cancer (CRC) screening tests conducted in Hamilton, Ontario, Canada in 2002. Methods A two-stage DCE design was used to investigate the impact of six attributes on participants' preferences for CRC screening test and willingness to undertake the test. We compared six models for clustered binary outcomes (logistic and probit regressions using cluster-robust standard error (SE), random-effects and generalized estimating equation approaches) and three models for clustered nominal outcomes (multinomial logistic and probit regressions with cluster-robust SE and random-effects multinomial logistic model). We also fitted a bivariate probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes. The rank of relative importance between attributes and the estimates of β coefficient within attributes were used to assess the model robustness. Results In total 468 participants with each completing 10 choices were analyzed. Similar results were reported for the rank of relative importance and β coefficients across models for stage-one data on evaluating participants' preferences for the test. The six attributes ranked from high to low as follows: cost, specificity, process, sensitivity, preparation and pain. However, the results differed across models for stage-two data on evaluating participants' willingness to undertake the tests. Little within-patient correlation (ICC ?0) was found in stage-one data, but substantial within-patient correlation existed (ICC = 0.659) in stage-two data. Conclusions When small clustering effect presented in DCE data, results remained robust across statistical models. However, results varied when larger clustering effect presented. Therefore, it is important to assess the robustness of the estimates via sensitivity analysis using different models for analyzing clustered data from DCE studies.