, Volume 36, Issue 2, pp 175–187 | Cite as

Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review

  • Mo Zhou
  • Winter Maxwell Thayer
  • John F. P. Bridges
Systematic Review



Latent class analysis (LCA) has been increasingly used to explore preference heterogeneity, but the literature has not been systematically explored and hence best practices are not understood.


We sought to document all applications of LCA in the stated-preference literature in health and to inform future studies by identifying current norms in published applications.


We conducted a systematic review of the MEDLINE, EMBASE, EconLit, Web of Science, and PsycINFO databases. We included stated-preference studies that used LCA to explore preference heterogeneity in healthcare or public health. Two co-authors independently evaluated titles, abstracts, and full-text articles. Abstracted key outcomes included segmentation methods, preference elicitation methods, number of attributes and levels, sample size, model selection criteria, number of classes reported, and hypotheses tests. Study data quality and validity were assessed with the Purpose, Respondents, Explanation, Findings, and Significance (PREFS) quality checklist.


We identified 2560 titles, 99 of which met the inclusion criteria for the review. Two-thirds of the studies focused on the preferences of patients and the general population. In total, 80% of the studies used discrete choice experiments. Studies used between three and 20 attributes, most commonly four to six. Sample size in LCAs ranged from 47 to 2068, with one-third between 100 and 300. Over 90% of the studies used latent class logit models for segmentation. Bayesian information criterion (BIC), Akaike information criterion (AIC), and log-likelihood (LL) were commonly used for model selection, and class size and interpretability were also considered in some studies. About 80% of studies reported two to three classes. The number of classes reported was not correlated with any study characteristics or study population characteristics (p > 0.05). Only 30% of the studies reported using statistical tests to detect significant variations in preferences between classes. Less than half of the studies reported that individual characteristics were included in the segmentation models, and 30% reported that post-estimation analyses were conducted to examine class characteristics. While a higher percentage of studies discussed clinical implications of the segmentation results, an increasing number of studies proposed policy recommendations based on segmentation results since 2010.


LCA is increasingly used to study preference heterogeneity in health and support decision-making. However, there is little consensus on best practices as its application in health is relatively new. With an increasing demand to study preference heterogeneity, guidance is needed to improve the quality of applications of segmentation methods in health to support policy development and clinical practice.



Mo Zhou (MZ) and John Bridges (JB) conceptualized this paper; MZ, JB, and Winter Thayer (WT) developed the search terms and inclusion/exclusion criteria for identifying relevant studies; MZ and WT conducted the title, abstract, and full-text review; JB served as the third reviewer in case of disagreement between MZ and WT; MZ led the writing of the manuscript; MZ, WT, and JB all contributed to the writing of the manuscript and approved the final version.

Compliance with Ethical Standards


Funding relating to this systematic review was received from a Patient-Centered Outcomes Research Institute (PCORI) Methods Program Award (ME-1303-5946).

Conflict of interest

Mo Zhou, Winter Thayer, and John Bridges report no conflicts of interest.

Data availability statement

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Supplementary material

40273_2017_575_MOESM1_ESM.xlsx (38 kb)
Supplementary material 1 (XLSX 37 kb)
40273_2017_575_MOESM2_ESM.xlsx (66 kb)
Supplementary material 2 (XLSX 65 kb)


  1. 1.
    Clark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics. 2014;32(9):883–902.PubMedCrossRefGoogle Scholar
  2. 2.
    Mandeville KL, Lagarde M, Hanson K. The use of discrete choice experiments to inform health workforce policy: a systematic review. BMC Health Serv Res. 2014;14(1):367.PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Vass C, Gray E, Payne K. Discrete choice experiments of pharmacy services: a systematic review. Int J Clin Pharmacy. 2016;38(3):620–30.Google Scholar
  4. 4.
    Adamowicz W, Swait J, Boxall P, Louviere J, Williams M. Perceptions versus objective measures of environmental quality in combined revealed and stated preference models of environmental valuation. J Environ Econ Manag. 1997;32(1):65–84.CrossRefGoogle Scholar
  5. 5.
    Boxall PC, Englin J, Adamowicz WL. Valuing aboriginal artifacts: a combined revealed-stated preference approach. J Environ Econ Manag. 2003;45(2):213–30.CrossRefGoogle Scholar
  6. 6.
    Morey E, Rossmann KG. Using stated-preference questions to investigate variations in willingness to pay for preserving marble monuments: classic heterogeneity, random parameters, and mixture models. J Cult Econ. 2003;27(3–4):215–29.CrossRefGoogle Scholar
  7. 7.
    Iragüen P, de Dios Ortúzar J. Willingness-to-pay for reducing fatal accident risk in urban areas: an internet-based web page stated preference survey. Accid Anal Prev. 2004;36(4):513–24.PubMedCrossRefGoogle Scholar
  8. 8.
    Deal K. Segmenting patients and physicians using preferences from discrete choice experiments. Patient. 2014;7(1):5–21.PubMedCrossRefGoogle Scholar
  9. 9.
    McFadden D. The choice theory approach to market research. Market Sci. 1986;5(4):275–97.CrossRefGoogle Scholar
  10. 10.
    Hilger J, Hanemann M. Heterogeneous preferences for water quality: a finite mixture model of beach recreation in Southern California. UC Sandiego, California Sea Grant College Program; 2006. Accessed 23 Sept 2017.
  11. 11.
    Joy SM, Little E, Maruthur NM, Purnell TS, Bridges JF. Patient preferences for the treatment of type 2 diabetes: a scoping review. Pharmacoeconomics. 2013;31(10):877–92.PubMedCrossRefGoogle Scholar
  12. 12.
    Spoth R, Ball AD, Klose A, Redmond C. Illustration of a market segmentation technique using family-focused prevention program preference data. Health Educ Res. 1996;11(2):259–67.CrossRefGoogle Scholar
  13. 13.
    Singh J, Cuttler L, Shin M, Silvers J, Neuhauser D. Medical decision-making and the patient: understanding preference patterns for growth hormone therapy using conjoint analysis. Med Care. 1998;36(8):AS31-AS45.Google Scholar
  14. 14.
    Stanek EJ, Oates MB, McGhan WF, Denofrio D, Loh E. Preferences for treatment outcomes in patients with heart failure: symptoms versus survival. J Card Fail. 2000;6(3):225–32.PubMedCrossRefGoogle Scholar
  15. 15.
    Cunningham CE, Deal K, Neville A, Rimas H, Lohfeld L. Modeling the problem-based learning preferences of McMaster University undergraduate medical students using a discrete choice conjoint experiment. Adv Health Sci Educ. 2006;11(3):245–66.CrossRefGoogle Scholar
  16. 16.
    Aspinall PA, Johnson ZK, Azuara-Blanco A, Montarzino A, Brice R, Vickers A. Evaluation of quality of life and priorities of patients with glaucoma. Invest Ophthalmol Vis Sci. 2008;49(5):1907–15.PubMedCrossRefGoogle Scholar
  17. 17.
    Cunningham CE, Deal K, Rimas H, Buchanan DH, Gold M, Sdao-Jarvie K, et al. Modeling the information preferences of parents of children with mental health problems: a discrete choice conjoint experiment. J Abnorm Child Psychol. 2008;36(7):1123.PubMedCrossRefGoogle Scholar
  18. 18.
    Cunningham CE, Deal K, Rimas H, Campbell H, Russell A, Henderson J, et al. Using conjoint analysis to model the preferences of different patient segments for attributes of patient-centered care. Patient. 2008;1(4):317–30.PubMedCrossRefGoogle Scholar
  19. 19.
    Hole AR. Modelling heterogeneity in patients’ preferences for the attributes of a general practitioner appointment. J Health Econ. 2008;27(4):1078–94.PubMedCrossRefGoogle Scholar
  20. 20.
    Cunningham CE, Deal K, Rimas H, Chen Y, Buchanan DH, Sdao-Jarvie K. Providing information to parents of children with mental health problems: a discrete choice conjoint analysis of professional preferences. J Abnorm Child Psychol. 2009;37(8):1089.PubMedCrossRefGoogle Scholar
  21. 21.
    Cunningham CE, Vaillancourt T, Rimas H, Deal K, Cunningham L, Short K, et al. Modeling the bullying prevention program preferences of educators: a discrete choice conjoint experiment. J Abnorm Child Psychol. 2009;37(7):929–43.PubMedCrossRefGoogle Scholar
  22. 22.
    Flynn TN, Louviere JJ, Peters TJ, Coast J. Using discrete choice experiments to understand preferences for quality of life. Variance-scale heterogeneity matters. Soc Sci Med. 2010;70(12):1957–65.PubMedCrossRefGoogle Scholar
  23. 23.
    Grindrod KA, Marra CA, Colley L, Tsuyuki RT, Lynd LD. Pharmacists’ preferences for providing patient-centered services: a discrete choice experiment to guide health policy. Ann Pharmacother. 2010;44(10):1554–64.PubMedCrossRefGoogle Scholar
  24. 24.
    Cunningham CE, Bruce BS, Snowdon AW, Chen Y, Kolga C, Piotrowski C, et al. Modeling improvements in booster seat use: a discrete choice conjoint experiment. Accid Anal Prev. 2011;43(6):1999–2009.PubMedCrossRefGoogle Scholar
  25. 25.
    Cunningham CE, Vaillancourt T, Cunningham LJ, Chen Y, Ratcliffe J. Modeling the bullying prevention program design recommendations of students from grades five to eight: a discrete choice conjoint experiment. Aggress Behav. 2011;37(6):521–37.PubMedCrossRefGoogle Scholar
  26. 26.
    Guo N, Marra CA, FitzGerald JM, Elwood RK, Anis AH, Marra F. Patient preference for latent tuberculosis infection preventive treatment: a discrete choice experiment. Value Health. 2011;14(6):937–43.PubMedCrossRefGoogle Scholar
  27. 27.
    Lacanilao RD, Cash SB, Adamowicz WL. Heterogeneous consumer responses to snack food taxes and warning labels. J Consum Aff. 2011;45(1):108–22.CrossRefGoogle Scholar
  28. 28.
    Mentzakis E, Ryan M, McNamee P. Using discrete choice experiments to value informal care tasks: exploring preference heterogeneity. Health Econ. 2011;20(8):930–44.PubMedCrossRefGoogle Scholar
  29. 29.
    Mentzakis E, Stefanowska P, Hurley J. A discrete choice experiment investigating preferences for funding drugs used to treat orphan diseases: an exploratory study. Health Econ Policy Law. 2011;6(03):405–33.PubMedCrossRefGoogle Scholar
  30. 30.
    Waschbusch DA, Cunningham CE, Pelham WE Jr, Rimas HL, Greiner AR, Gnagy EM, et al. A discrete choice conjoint experiment to evaluate parent preferences for treatment of young, medication naive children with ADHD. J Clin Child Adolesc Psychol. 2011;40(4):546–61.PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Cunningham CE, Henderson J, Niccols A, Dobbins M, Sword W, Chen Y, et al. Preferences for evidence-based practice dissemination in addiction agencies serving women: a discrete-choice conjoint experiment. Addiction. 2012;107(8):1512–24.PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    Fuller-Tyszkiewicz M, Reynard K, Skouteris H, McCabe M. An examination of the contextual determinants of self-objectification. Psychol Women Q. 2012;36(1):76–87.CrossRefGoogle Scholar
  33. 33.
    Haegeli P, Gunn M, Haider W. Identifying a high-risk cohort in a complex and dynamic risk environment: out-of-bounds skiing—an example from avalanche safety. Prev Sci. 2012;13(6):562–73.PubMedCrossRefGoogle Scholar
  34. 34.
    Kuzmanovic M, Vujosevic M, Martic M. Using conjoint analysis to elicit patients’ preferences for public primary care service in Serbia. HealthMED. 2012;6(2):496–504.Google Scholar
  35. 35.
    Lau MA, Colley L, Willett BR, Lynd LD. Employee’s preferences for access to mindfulness-based cognitive therapy to reduce the risk of depressive relapse—a discrete choice experiment. Mindfulness. 2012;3(4):318–26.CrossRefGoogle Scholar
  36. 36.
    Naik-Panvelkar P, Armour C, Rose JM, Saini B. Patient preferences for community pharmacy asthma services. Pharmacoeconomics. 2012;30(10):961–76.PubMedCrossRefGoogle Scholar
  37. 37.
    Najafzadeh M, Lynd LD, Davis JC, Bryan S, Anis A, Marra M, et al. Barriers to integrating personalized medicine into clinical practice: a best-worst scaling choice experiment. Genet Med. 2012;14(5):520–6.PubMedCrossRefGoogle Scholar
  38. 38.
    Carroll FE, Al-Janabi H, Flynn T, Montgomery AA. Women and their partners’ preferences for Down’s syndrome screening tests: a discrete choice experiment. Prenat Diagn. 2013;33(5):449–56.PubMedCrossRefGoogle Scholar
  39. 39.
    Cunningham CE, Chen Y, Deal K, Rimas H, McGrath P, Reid G, et al. The interim service preferences of parents waiting for children’s mental health treatment: a discrete choice conjoint experiment. J Abnorm Child Psychol. 2013;41(6):865–77.PubMedCrossRefGoogle Scholar
  40. 40.
    Cunningham CE, Kostrzewa L, Rimas H, Chen Y, Deal K, Blatz S, et al. Modeling organizational justice improvements in a pediatric health service. Patient. 2013;6(1):45–59.PubMedCrossRefGoogle Scholar
  41. 41.
    de Bekker-Grob EW, Rose J, Donkers B, Essink-Bot M-L, Bangma C, Steyerberg E. Men’s preferences for prostate cancer screening: a discrete choice experiment. Br J Cancer. 2013;108(3):533–41.PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Flynn TN, Peters TJ, Coast J. Quantifying response shift or adaptation effects in quality of life by synthesising best-worst scaling and discrete choice data. J Choice Model. 2013;6:34–43.CrossRefGoogle Scholar
  43. 43.
    Jarvis W, Pettigrew S. The relative influence of alcohol warning statement type on young drinkers’ stated choices. Food Qual Prefer. 2013;28(1):244–52.CrossRefGoogle Scholar
  44. 44.
    Lagarde M. Investigating attribute non-attendance and its consequences in choice experiments with latent class models. Health Econ. 2013;22(5):554–67.PubMedCrossRefGoogle Scholar
  45. 45.
    Whitty JA, Stewart S, Carrington MJ, Calderone A, Marwick T, Horowitz JD, et al. Patient preferences and willingness-to-pay for a home or clinic based program of chronic heart failure management: findings from the Which? trial. PLoS One. 2013;8(3):e58347.PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Wong Y-N, Egleston BL, Sachdeva K, Eghan N, Pirollo M, Stump TK, et al. Cancer patients’ trade-offs among efficacy, toxicity and out-of-pocket cost in the curative and non-curative setting. Med Care. 2013;51(9):838–45.PubMedCrossRefGoogle Scholar
  47. 47.
    Yoo HI, Doiron D. The use of alternative preference elicitation methods in complex discrete choice experiments. J Health Econ. 2013;32(6):1166–79.PubMedCrossRefGoogle Scholar
  48. 48.
    Zimmermann TM, Clouth J, Elosge M, Heurich M, Schneider E, Wilhelm S, et al. Patient preferences for outcomes of depression treatment in Germany: a choice-based conjoint analysis study. J Affect Disord. 2013;148(2):210–9.PubMedCrossRefGoogle Scholar
  49. 49.
    Brown DS, Poulos C, Johnson FR, Chamiec-Case L, Messonnier ML. Adolescent girls’ preferences for HPV vaccines: a discrete choice experiment. Adv Health Econ Health Serv Res. 2014;24:93–121.PubMedCrossRefGoogle Scholar
  50. 50.
    Cai Q, Wan F, Dong X, Liao X, Zheng J, Wang R, et al. Fertility clinicians and infertile patients in China have different preferences in fertility care. Hum Reprod. 2014;29(4):712–9.PubMedCrossRefGoogle Scholar
  51. 51.
    Cunningham CE, Barwick M, Short K, Chen Y, Rimas H, Ratcliffe J, et al. Modeling the mental health practice change preferences of educators: a discrete-choice conjoint experiment. School Ment Health. 2014;6(1):1–14.PubMedCrossRefGoogle Scholar
  52. 52.
    Cunningham CE, Walker JR, Eastwood JD, Westra H, Rimas H, Chen Y, et al. Modeling mental health information preferences during the early adult years: a discrete choice conjoint experiment. J Health Commun. 2014;19(4):413–40.PubMedCrossRefGoogle Scholar
  53. 53.
    Deal K, Keshavjee K, Troyan S, Kyba R, Holbrook AM. Physician and patient willingness to pay for electronic cardiovascular disease management. Int J Med Inform. 2014;83(7):517–28.PubMedCrossRefGoogle Scholar
  54. 54.
    Determann D, Korfage IJ, Lambooij MS, Bliemer M, Richardus JH, Steyerberg EW, et al. Acceptance of vaccinations in pandemic outbreaks: a discrete choice experiment. PLoS One. 2014;9(7):e102505.PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Erdem S, Campbell D, Thompson C. Elimination and selection by aspects in health choice experiments: prioritising health service innovations. J Health Econ. 2014;38:10–22.PubMedCrossRefGoogle Scholar
  56. 56.
    Erdem S, Thompson C. Prioritising health service innovation investments using public preferences: a discrete choice experiment. BMC Health Serv Res. 2014;14(1):360.PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Fraenkel L, Suter L, Cunningham CE, Hawker G. Understanding preferences for disease-modifying drugs in osteoarthritis. Arthritis Care Res (Hoboken). 2014;66(8):1186–92.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Goossens LM, Utens CM, Smeenk FW, Donkers B, van Schayck OC, Rutten-van Mölken MP. Should I stay or should I go home? A latent class analysis of a discrete choice experiment on hospital-at-home. Value Health. 2014;17(5):588–96.PubMedCrossRefGoogle Scholar
  59. 59.
    Hofman R, de Bekker-Grob EW, Richardus JH, de Koning HJ, van Ballegooijen M, Korfage IJ. Have preferences of girls changed almost 3 years after the much debated start of the HPV vaccination program in the Netherlands? A discrete choice experiment. PLoS One. 2014;9(8):e104772.PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Hole AR, Norman R, Viney R. Response patterns in health state valuation using endogenous attribute attendance and latent class analysis. Health Econ. 2016;25(2):212–24.PubMedCrossRefGoogle Scholar
  61. 61.
    Najafzadeh M, Gagne JJ, Choudhry NK, Polinski JM, Avorn J, Schneeweiss SS. Patients’ preferences in anticoagulant therapy. Circ Cardiovasc Qual Outcomes. 2014;7(6):912–9.PubMedCrossRefGoogle Scholar
  62. 62.
    Paolucci F, Mentzakis E, Defechereux T, Niessen LW. Equity and efficiency preferences of health policy makers in China—a stated preference analysis. Health Policy Plan. 2015;30(8):1059–66.PubMedCrossRefGoogle Scholar
  63. 63.
    Ungar WJ, Hadioonzadeh A, Najafzadeh M, Tsao NW, Dell S, Lynd LD. Quantifying preferences for asthma control in parents and adolescents using best–worst scaling. Respir Med. 2014;108(6):842–51.PubMedCrossRefGoogle Scholar
  64. 64.
    Bailey K, Cunningham C, Pemberton J, Rimas H, Morrison KM. Understanding academic clinicians’ decision making for the treatment of childhood obesity. Child Obes. 2015;11(6):696–706.PubMedCrossRefGoogle Scholar
  65. 65.
    Cunningham CE, Rimas H, Chen Y, Deal K, McGrath P, Lingley-Pottie P, et al. Modeling parenting programs as an interim service for families waiting for children’s mental health treatment. J Clin Child Adolesc Psychol. 2015;44(4):616–29.PubMedCrossRefGoogle Scholar
  66. 66.
    Feudtner C, Walter JK, Faerber JA, Hill DL, Carroll KW, Mollen CJ, et al. Good-parent beliefs of parents of seriously ill children. JAMA Pediatr. 2015;169(1):39–47.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Flynn TN, Huynh E, Peters TJ, Al-Janabi H, Clemens S, Moody A, et al. Scoring the Icecap—a capability instrument. Estimation of a UK general population tariff. Health Econ. 2015;24(3):258–69.PubMedCrossRefGoogle Scholar
  68. 68.
    Gallego G, Dew A, Lincoln M, Bundy A, Chedid RJ, Bulkeley K, et al. Should I stay or should I go? Exploring the job preferences of allied health professionals working with people with disability in rural Australia. Hum Resour Health. 2015;13(1):53.PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Grisolía JM, Longo A, Hutchinson G, Kee F. Applying health locus of control and latent class modelling to food and physical activity choices affecting CVD risk. Soc Sci Med. 2015;132:1–10.PubMedCrossRefGoogle Scholar
  70. 70.
    Lagarde M, Erens B, Mays N. Determinants of the choice of GP practice registration in England: evidence from a discrete choice experiment. Health Policy. 2015;119(4):427–36.PubMedCrossRefGoogle Scholar
  71. 71.
    Mühlbacher AC, Bethge S. Patients’ preferences: a discrete-choice experiment for treatment of non-small-cell lung cancer. Eur J Health Econ. 2015;16(6):657–70.PubMedCrossRefGoogle Scholar
  72. 72.
    O’Hara NN, Roy L, O’Hara LM, Spiegel JM, Lynd LD, FitzGerald JM, et al. Healthcare worker preferences for active tuberculosis case finding programs in South Africa: a best-worst scaling choice experiment. PLoS One. 2015;10(7):e0133304.PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    Rischatsch M. Who joins the network? Physicians’ resistance to take budgetary co-responsibility. J Health Econ. 2015;40:109–21.PubMedCrossRefGoogle Scholar
  74. 74.
    Rosato R, Testa S, Oggero A, Molinengo G, Bertolotto A. Quality of life and patient preferences: identification of subgroups of multiple sclerosis patients. Qual Life Res. 2015;24(9):2173–82.PubMedCrossRefGoogle Scholar
  75. 75.
    Skedgel C, Wailoo A, Akehurst R. Societal preferences for distributive justice in the allocation of health care resources: a latent class discrete choice experiment. Med Decis Mak. 2015;35(1):94–105.CrossRefGoogle Scholar
  76. 76.
    van de Wetering L, van Exel J, Bobinac A, Brouwer WB. Valuing QALYs in relation to equity considerations using a discrete choice experiment. Pharmacoeconomics. 2015;33(12):1289–300.PubMedPubMedCentralCrossRefGoogle Scholar
  77. 77.
    Veldwijk J, van der Heide I, Rademakers J, Schuit AJ, de Wit GA, Uiters E, et al. Preferences for vaccination: does health literacy make a difference? Med Decis Mak. 2015;35(8):948–58.CrossRefGoogle Scholar
  78. 78.
    Whitty JA, Ratcliffe J, Kendall E, Burton P, Wilson A, Littlejohns P, et al. Prioritising patients for bariatric surgery: building public preferences from a discrete choice experiment into public policy. BMJ Open. 2015;5(10):e008919.PubMedPubMedCentralCrossRefGoogle Scholar
  79. 79.
    Yan K, Bridges JF, Augustin S, Laine L, Garcia-Tsao G, Fraenkel L. Factors impacting physicians’ decisions to prevent variceal hemorrhage. BMC Gastroenterol. 2015;15(1):55.PubMedPubMedCentralCrossRefGoogle Scholar
  80. 80.
    Ammi M, Peyron C. Heterogeneity in general practitioners’ preferences for quality improvement programs: a choice experiment and policy simulation in France. Health Econ Rev. 2016;6(1):44.PubMedPubMedCentralCrossRefGoogle Scholar
  81. 81.
    Becker MP, Christensen BK, Cunningham CE, Furimsky I, Rimas H, Wilson F, et al. Preferences for early intervention mental health services: a discrete-choice conjoint experiment. Psychiatr Serv. 2016;67(2):184–91.PubMedCrossRefGoogle Scholar
  82. 82.
    Brown ZS, Kramer RA, Ocan D, Oryema C. Household perceptions and subjective valuations of indoor residual spraying programmes to control malaria in northern Uganda. Infect Dis Poverty. 2016;5(1):100.PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Cunningham CE, Hutchings T, Henderson J, Rimas H, Chen Y. Modeling the hospital safety partnership preferences of patients and their families: a discrete choice conjoint experiment. Patient Prefer Adherence. 2016;10:1359–72.PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    de-Magistris T, Lopéz-Galán B. Consumers’ willingness to pay for nutritional claims fighting the obesity epidemic: the case of reduced-fat and low salt cheese in Spain. Public Health. 2016;135:83–90.Google Scholar
  85. 85.
    Determann D, Korfage IJ, Fagerlin A, Steyerberg EW, Bliemer MC, Voeten HA, et al. Public preferences for vaccination programmes during pandemics caused by pathogens transmitted through respiratory droplets–a discrete choice experiment in four European countries, 2013. Euro Surveill. 2016;21(22):30247.CrossRefGoogle Scholar
  86. 86.
    Determann D, Lambooij MS, de Bekker-Grob EW, Hayen AP, Varkevisser M, Schut FT, et al. What health plans do people prefer? The trade-off between premium and provider choice. Soc Sci Med. 2016;165:10–8.PubMedCrossRefGoogle Scholar
  87. 87.
    Dong D, Ozdemir S, Bee YM, Toh S-A, Bilger M, Finkelstein E. Measuring high-risk patients’ preferences for pharmacogenetic testing to reduce severe adverse drug reaction: a discrete choice experiment. Value Health. 2016;19(6):767–75.PubMedCrossRefGoogle Scholar
  88. 88.
    Finkelstein E, Malhotra C, Chay J, Ozdemir S, Chopra A, Kanesvaran R. Impact of treatment subsidies and cash payouts on treatment choices at the end of life. Value Health. 2016;19(6):788–94.PubMedCrossRefGoogle Scholar
  89. 89.
    Flynn TN, Bilger M, Malhotra C, Finkelstein EA. Are efficient designs used in discrete choice experiments too difficult for some respondents? A case study eliciting preferences for end-of-life care. Pharmacoeconomics. 2016;34(3):273–84.PubMedCrossRefGoogle Scholar
  90. 90.
    Fraenkel L, Lim J, Garcia-Tsao G, Reyna V, Monto A. Examining hepatitis C virus treatment preference heterogeneity using segmentation analysis: treat now or defer? J Clin Gastroenterol. 2016;50(3):252–7.PubMedPubMedCentralCrossRefGoogle Scholar
  91. 91.
    Fraenkel L, Lim J, Garcia-Tsao G, Reyna V, Monto A, Bridges JF. Variation in treatment priorities for chronic hepatitis C: a latent class analysis. Patient. 2016;9(3):241–9.PubMedCrossRefGoogle Scholar
  92. 92.
    Gong J, Zhang Y, Feng J, Zhang W, Yin W, Wu X, et al. How best to obtain consent to thrombolysis Individualized decision-making. Neurology. 2016;86(11):1045–52.PubMedCrossRefGoogle Scholar
  93. 93.
    Hazlewood GS, Bombardier C, Tomlinson G, Thorne C, Bykerk VP, Thompson A, et al. Treatment preferences of patients with early rheumatoid arthritis: a discrete-choice experiment. Rheumatology. 2016;55(11):1959–68.PubMedCrossRefGoogle Scholar
  94. 94.
    Hifinger M, Hiligsmann M, Ramiro S, Watson V, Severens J, Fautrel B, et al. Economic considerations and patients’ preferences affect treatment selection for patients with rheumatoid arthritis: a discrete choice experiment among European rheumatologists. Ann Rheum Dis. 2017;76(1):126–32.PubMedCrossRefGoogle Scholar
  95. 95.
    Howard K, Jan S, Rose JM, Wong G, Craig JC, Irving M, et al. Preferences for policy options for deceased organ donation for transplantation: a discrete choice experiment. Transplantation. 2016;100(5):1136–48.PubMedCrossRefGoogle Scholar
  96. 96.
    Kan HJ, de Bekker-Grob EW, Van Marion ES, Van Oijen GW, van Nieuwenhoven CA, Zhou C, et al. Patients’ preferences for treatment for Dupuytren’s disease: a discrete choice experiment. Plast Reconstr Surg. 2016;137(1):165–73.PubMedCrossRefGoogle Scholar
  97. 97.
    Liang D, Tang C-X. The specialty choice of medical students in China: a stated preference experiment. BMC Med Educ. 2016;16(1):107.PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Lock J, Bekker-Grob E, Urhan G, Peters M, Meijer K, Brons P, et al. Facilitating the implementation of pharmacokinetic-guided dosing of prophylaxis in haemophilia care by discrete choice experiment. Haemophilia. 2016;22(1):e1–10.PubMedCrossRefGoogle Scholar
  99. 99.
    Lynd LD, Traboulsee A, Marra CA, Mittmann N, Evans C, Li KH, et al. Quantitative analysis of multiple sclerosis patients’ preferences for drug treatment: a best–worst scaling study. Ther Adv Neurol Disord. 2016;9(4):287–96.PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Mandeville KL, Ulaya G, Lagarde M, Muula AS, Dzowela T, Hanson K. The use of specialty training to retain doctors in Malawi: a discrete choice experiment. Soc Sci Med. 2016;169:109–18.PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Ng X, Bridges JF, Ross MM, Frosch E, Reeves G, Cunningham CE. A Latent class analysis to identify variation in caregivers’ preferences for their child’s attention-deficit/hyperactivity disorder treatment: do stated preferences match current treatment? Patient. 2017;10(2):251–62.PubMedCrossRefGoogle Scholar
  102. 102.
    Pfarr C, Schmid A. Redistribution through social health insurance: evidence on citizen preferences. Eur J Health Econ. 2016;17(5):611–28.PubMedCrossRefGoogle Scholar
  103. 103.
    Ratcliffe J, Huynh E, Chen G, Stevens K, Swait J, Brazier J, et al. Valuing the Child Health Utility 9D: using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm. Soc Sci Med. 2016;157:48–59.PubMedCrossRefGoogle Scholar
  104. 104.
    Tang C, Xu J, Zhang M. The choice and preference for public-private health care among urban residents in China: evidence from a discrete choice experiment. BMC Health Serv Res. 2016;16(1):580.PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Veldwijk J, Lambooij MS, Kallenberg FG, van Kranen HJ, Bredenoord AL, Dekker E, et al. Preferences for genetic testing for colorectal cancer within a population-based screening program: a discrete choice experiment. Eur J Hum Genet. 2016;24(3):361–6.PubMedCrossRefGoogle Scholar
  106. 106.
    Virudachalam S, Chung PJ, Faerber JA, Pian TM, Thomas K, Feudtner C. Quantifying parental preferences for interventions designed to improve home food preparation and home food environments during early childhood. Appetite. 2016;98:115–24.PubMedCrossRefGoogle Scholar
  107. 107.
    Weernink MG, van Til JA, van Vugt JP, Movig KL, Groothuis-Oudshoorn CG. IJzerman MJ. Involving patients in weighting benefits and harms of treatment in Parkinson’s disease. PLoS One. 2016;11(8):e0160771.PubMedPubMedCentralCrossRefGoogle Scholar
  108. 108.
    Miners A, Llewellyn C, Cooper V, Youssef E, Pollard A, Lagarde M, et al. A discrete choice experiment to assess people living with HIV’s (PLWHIV’s) preferences for GP or HIV clinic appointments. Sex Transm Infect. 2017;93(2):105–11.PubMedCrossRefGoogle Scholar
  109. 109.
    Richardson G, Bojke C, Kennedy A, Reeves D, Bower P, Lee V, et al. What outcomes are important to patients with long term conditions? A discrete choice experiment. Value Health. 2009;12(2):331–9.PubMedCrossRefGoogle Scholar
  110. 110.
    Showalter TN, Mishra MV, Bridges JF. Factors that influence patient preferences for prostate cancer management options: a systematic review. Patient Prefer Adherence. 2015;9:899–911.PubMedPubMedCentralCrossRefGoogle Scholar
  111. 111.
    Cheung KL, Wijnen BF, Hollin IL, Janssen EM, Bridges JF, Evers SM, et al. Using best–worst scaling to investigate preferences in health care. Pharmacoeconomics. 2016;34(12):1195–209.PubMedPubMedCentralCrossRefGoogle Scholar
  112. 112.
    Bien DR, Danner M, Vennedey V, Civello D, Evers SM, Hiligsmann M. Patients’ preferences for outcome, process and cost attributes in cancer treatment: a systematic review of discrete choice experiments. Patient. 2017. doi: 10.1007/s40271-017-0235-y (Epub 2017 Mar 31).PubMedPubMedCentralGoogle Scholar
  113. 113.
    Magidson J, Vermunt JK, editors. Removing the scale factor confound in multinomial logit choice models to obtain better estimates of preference. In: Sawtooth software conference proceedings. 2007.Google Scholar
  114. 114.
    Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct Equ Model. 2007;14(4):535–69.CrossRefGoogle Scholar
  115. 115.
    Swait J. A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data. J Retail Consum Serv. 1994;1(2):77–89.CrossRefGoogle Scholar
  116. 116.
    Boxall PC, Adamowicz WL. Understanding heterogeneous preferences in random utility models: a latent class approach. Environ Resour Econ. 2002;23(4):421–46.CrossRefGoogle Scholar
  117. 117.
    Huang G-H, Bandeen-Roche K. Building an identifiable latent class model with covariate effects on underlying and measured variables. Psychometrika. 2004;69(1):5–32.CrossRefGoogle Scholar
  118. 118.
    Yang C-C, Yang C-C. Separating latent classes by information criteria. J Classif. 2007;24(2):183–203.CrossRefGoogle Scholar
  119. 119.
    Lanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci. 2013;14(2):157–68.PubMedPubMedCentralCrossRefGoogle Scholar
  120. 120.
    Vass C, Rigby D, Payne K. The role of qualitative research methods in discrete choice experiments: a systematic review and survey of authors. Med Decis Mak. 2017;37(3):298–313.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Health Policy and ManagementJohns Hopkins University Bloomberg School of Public HealthBaltimoreUSA

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