A Systematic Review of Discrete Choice Experiments and Conjoint Analysis on Genetic Testing



Although genetic testing has the potential to offer promising medical benefits, concerns regarding its potential negative impacts may influence its acceptance. Users and providers need to weigh the benefits, costs and potential harms before deciding whether to take up or recommend genetic testing. Attribute-based stated-preference methods, such as discrete choice experiment (DCE) or conjoint analysis, can help to quantify how individuals value different features of genetic testing.


The aim of this paper was to conduct a systematic review of DCE and conjoint analysis studies on genetic testing, including genomic tests.


A systematic search was conducted in seven databases: Web of Science, CINAHL Plus with Full Text (EBSCO), PsycINFO, PubMed, Embase, The Cochrane Library and SCOPUS. The search was conducted in February 2021 and was limited to English peer-reviewed articles published until the search date. The search keywords included relevant keywords such as ‘genetic testing’, ‘genomic testing’, ‘pharmacogenetic testing’, ‘discrete choice experiment’ and ‘conjoint analysis’. Narrative synthesis of the studies was conducted on survey population, testing type, recruitment and data collection, survey development, questionnaire content, survey validity, analysis, outcomes and other design features.


Of the 292 articles retrieved, 38 full-text articles were included in this review. Nearly two-thirds of the studies were published since 2015 and all were conducted in high-income countries. Survey samples included patients, parents, general population and healthcare providers. The articles assessed preferences for pharmacogenetic testing (28.9%), predictive testing and diagnostic testing (18.4%), while only one (2.6%) study investigated preferences for carrier testing. The most common sampling method was convenience sampling (57.9%) and the majority recruited participants via web-enabled surveys (60.5%). Review of literature (84.6%), discussions with healthcare professionals (71.8%) and cognitive interviews (53.8%) were commonly used for attribute identification. A survey validity test was included in only one-quarter of the studies (28.2%). Cost attributes were the most studied attribute type (76.9%), followed by risk attributes (61.5%). Among those that reported relative attribute importance, attributes related to benefits were the most commonly reported attributes with the highest relative attribute importance. Preference heterogeneity was investigated in most studies by modelling, such as via mixed logit analysis (82.1%) and/or by using interaction effects with respondent characteristics (74.4%). Willingness to pay was the most commonly estimated outcome and was presented in about two-thirds (n = 25; 64.1%) of the studies.


With the continuous advancement in genetic technology resulting in expanding options for genetic testing and improvements in delivery methods, the application of genetic testing in clinical care is expected to rise. DCEs and conjoint analysis remain robust and useful methods to elicit preferences of potential stakeholders. This review serves as a summary for future researchers when designing similar studies.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

    Fulda KG, Lykens K. Ethical issues in predictive genetic testing: a public health perspective. J Med Ethics. 2006;32(3):143–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Evans WE, McLeod HL. Pharmacogenomics—drug disposition, drug targets, and side effects. N Engl J Med. 2003;348(6):538–49.

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Eccles DM. Uses of genetic testing for cancer prevention. Ann Oncol. 2019;30:vi4.

    Article  Google Scholar 

  4. 4.

    Arribas-Ayllon M, Sarangi S, Clarke A. Genetic testing: accounts of autonomy, responsibility and blame. Routledge; 2013.

    Book  Google Scholar 

  5. 5.

    Hall MA, Rich SS. Patients’ fear of genetic discrimination by health insurers: the impact of legal protections. Genet Med. 2000;2(4):214–21.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Eno CC, Barton SK, Dorrani N, Cederbaum SD, Deignan JL, Grody WW. Confidential genetic testing and electronic health records: a survey of current practices among Huntington disease testing centers. Mol Genet Genom Med. 2020;8(1):e1026.

    Google Scholar 

  7. 7.

    Burke W. Genetic testing. N Engl J Med. 2002;347(23):1867–75.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Phillips KA, Deverka PA, Hooker GW, Douglas MP. Genetic test availability and spending: where are we now? Where are we going? Health Aff. 2018;37(5):710–6.

    Article  Google Scholar 

  9. 9.

    Henneman L, Vermeulen E, Van El CG, Claassen L, Timmermans DR, Cornel MC. Public attitudes towards genetic testing revisited: comparing opinions between 2002 and 2010. Eur J Hum Genet. 2013;21(8):793–9.

    PubMed  Article  Google Scholar 

  10. 10.

    Ryan M, Gerard K, Amaya-Amaya M. Using discrete choice experiments to value health and health care, vol. 11. Springer; 2007.

    Google Scholar 

  11. 11.

    Ozdemir S, Wong TT, Allingham RR, Finkelstein EA. Predicted patient demand for a new delivery system for glaucoma medicine. Medicine. 2017;96(15):e6626.

    PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Ozdemir S, Bilger M, Finkelstein EA. Stated uptake of physical activity rewards programmes among active and insufficiently active full-time employees. Appl Health Econ Health Policy. 2017;15(5):647–56.

    PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Lancsar E, Savage E. Deriving welfare measures from discrete choice experiments: inconsistency between current methods and random utility and welfare theory. Health Econ. 2004;13(9):901–7.

    PubMed  Article  Google Scholar 

  14. 14.

    Powell G, Holmes EAF, Plumpton CO, Ring A, Baker GA, Jacoby A, Pirmohamed M, Marson AG, Hughes DA. Pharmacogenetic testing prior to carbamazepine treatment of epilepsy: patients’ and physicians’ preferences for testing and service delivery. Br J Clin Pharmacol. 2015;80(5):1149–59. https://doi.org/10.1111/bcp.12715.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Armstrong K, Putt M, Halbert CH, Grande D, Schwartz JS, Liao K, Marcus N, Demeter MB, Shea J. The influence of health care policies and health care system distrust on willingness to undergo genetic testing. Med Care. 2012;50(5):381–7. https://doi.org/10.1097/MLR.0b013e31824d748b.

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Huang M-Y, Huston SA, Perri M. Consumer preferences for the predictive genetic test for Alzheimer disease. J Genet Couns. 2014;23(2):172–8.

    PubMed  Article  Google Scholar 

  17. 17.

    Buchanan J, Blair E, Thomson KL, Ormondroyd E, Watkins H, Taylor JC, Wordsworth S. Do health professionals value genomic testing? A discrete choice experiment in inherited cardiovascular disease. Eur J Hum Genet. 2019;27(11):1639–48.

    PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Severin F, Hess W, Schmidtke J, Mühlbacher A, Rogowski W. Value judgments for priority setting criteria in genetic testing: a discrete choice experiment. Health Policy. 2015;119(2):164–73.

    PubMed  Article  Google Scholar 

  19. 19.

    Marshall DA, MacDonald KV, Heidenreich S, Hartley T, Bernier FP, Gillespie MK, McInnes B, Innes AM, Armour CM, Boycott KM. The value of diagnostic testing for parents of children with rare genetic diseases. Genet Med. 2019;21(12):2798–806.

    PubMed  Article  Google Scholar 

  20. 20.

    Najafzadeh M, Johnston KM, Peacock SJ, Connors JM, Marra MA, Lynd LD, Marra CA. Genomic testing to determine drug response: measuring preferences of the public and patients using discrete choice experiment (DCE). BMC Health Serv Res. 2013;13(1):454.

    PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Wong XY, Groothuis-Oudshoorn CG, Tan CS, van Til JA, Hartman M, Chong KJ, IJzerman MJ, Wee H-L. Women’s preferences, willingness-to-pay, and predicted uptake for single-nucleotide polymorphism gene testing to guide personalized breast cancer screening strategies: a discrete choice experiment. Patient Prefer Adherence. 2018;12:1837.

    PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Fanos JH, Johnson JP. Barriers to carrier testing for adult cystic fibrosis sibs: the importance of not knowing. Am J Med Genet. 1995;59(1):85–91.

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    National Cancer Institute (2019) Does someone who inherits a cancer susceptibility variant always get cancer?. https://www.cancer.gov/about-cancer/causes-prevention/genetics/genetic-testing-fact-sheet#does-someone-who-inherits-a-cancer-susceptibility-variant-always-get-cancer. Accessed 8 May 2020

  24. 24.

    National Cancer Institute (2019) What do the results of genetic testing mean?. https://www.cancer.gov/about-cancer/causes-prevention/genetics/genetic-testing-fact-sheet#what-do-the-results-of-genetic-testing-mean. Accessed 8 May 2020

  25. 25.

    Cirino AL, Harris S, Lakdawala NK, Michels M, Olivotto I, Day SM, Abrams DJ, Charron P, Caleshu C, Semsarian C. Role of genetic testing in inherited cardiovascular disease: a review. JAMA Cardiol. 2017;2(10):1153–60.

    PubMed  Article  Google Scholar 

  26. 26.

    Genetics Home Reference (2020) What is direct-to-consumer genetic testing?. https://ghr.nlm.nih.gov/primer/dtcgenetictesting/directtoconsumer. Accessed 23 Apr 2020

  27. 27.

    Marshall D, McGregor E, Currie G. Measuring preferences for colorectal cancer screening. Patient Patient Cent Outcomes Res. 2010;3(2):79–89.

    Article  Google Scholar 

  28. 28.

    Hollin IL, Craig BM, Coast J, Beusterien K, Vass C, DiSantostefano R, Peay H. Reporting formative qualitative research to support the development of quantitative preference study protocols and corresponding survey instruments: guidelines for authors and reviewers. Patient Patient Cent Outcomes Res. 2020;13(1):121–36.

    Article  Google Scholar 

  29. 29.

    Marshall D, Bridges JF, Hauber B, Cameron R, Donnalley L, Fyie K, Johnson FR. Conjoint analysis applications in health—how are studies being designed and reported? Patient Patient Cent Outcomes Res. 2010;3(4):249–56.

    Article  Google Scholar 

  30. 30.

    Bridges JF, Hauber AB, Marshall D, 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(4):403–13.

    PubMed  Article  Google Scholar 

  31. 31.

    Bless H, Bohner G, Hild T, Schwarz N. Asking difficult questions: task complexity increases the impact of response alternatives. Eur J Soc Psychol. 1992;22(3):309–12.

    Article  Google Scholar 

  32. 32.

    Luce MF, Payne JW, Bettman JR. Emotional trade-off difficulty and choice. J Mark Res. 1999;36(2):143–59.

    Article  Google Scholar 

  33. 33.

    Rezaei A, Patterson Z. Detecting, non-transitive, inconsistent responses in discrete choice experiments. Montreal: CIRRELT; 2015.

    Google Scholar 

  34. 34.

    Scott A. Identifying and analysing dominant preferences in discrete choice experiments: an application in health care. J Econ Psychol. 2002;23(3):383–98.

    Article  Google Scholar 

  35. 35.

    Egleston BL, Miller SM, Meropol NJ. The impact of misclassification due to survey response fatigue on estimation and identifiability of treatment effects. Stat Med. 2011;30(30):3560–72.

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Davidson BA, Ehrisman J, Reed SD, Yang J-C, Buchanan A, Havrilesky LJ. Preferences of women with epithelial ovarian cancer for aspects of genetic testing. Gynecol Oncol Res Pract. 2019;6(1):1–8.

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Dhanda DS, Veenstra DL, Regier DA, Basu A, Carlson JJ. Payer preferences and willingness to pay for genomic precision medicine: a discrete choice experiment. J Manag Care Spec Pharm. 2020;26(4):529–37.

    PubMed  Google Scholar 

  38. 38.

    Hendrix N, Regier DA, Chatterjee J, Dhanda DS, Basu A, Veenstra DL, Carlson JJ. Provider preferences for resolving uncertainty and avoiding harms in precision medicine: a discrete choice experiment. Pers Med. 2020;17(05):389–98.

    CAS  Article  Google Scholar 

  39. 39.

    Tong R. Ethical concerns about genetic testing and screening. N C Med J. 2013;74(6):522–5.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Genetics Home Reference (2020) How can consumers be sure a genetic test is valid and useful?. https://ghr.nlm.nih.gov/primer/testing/validtest. Accessed 8 May 2020

  41. 41.

    Dong D, Ozdemir S, Mong Bee Y, Toh SA, 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. https://doi.org/10.1016/j.jval.2016.03.1837.

    Article  PubMed  Google Scholar 

  42. 42.

    Andrews LB. Assessing genetic risks: implications for health and social policy, vol. 1. National Academies; 1994.

    Google Scholar 

  43. 43.

    Genetics Home Reference (2020) What is genetic discrimination? https://ghr.nlm.nih.gov/primer/testing/discrimination. Accessed 23 Apr 2020

  44. 44.

    Hall J, Fiebig DG, King MT, Hossain I, Louviere JJ. What influences participation in genetic carrier testing? Results from a discrete choice experiment. J Health Econ. 2006;25(3):520–37.

    PubMed  Article  Google Scholar 

  45. 45.

    Herbild L, Gyrd-Hansen D, Bech M. Patient preferences for pharmacogenetic screening in depression. Int J Technol Assess Health Care. 2008;24(1):96.

    PubMed  Article  Google Scholar 

  46. 46.

    Herbild L, Bech M, Gyrd-Hansen D. Estimating the Danish populations’ preferences for pharmacogenetic testing using a discrete choice experiment. The case of treating depression. Value Health. 2009;12(4):560–7.

    PubMed  Article  Google Scholar 

  47. 47.

    Regier D, Friedman J, Makela N, Ryan M, Marra C. Valuing the benefit of diagnostic testing for genetic causes of idiopathic developmental disability: willingness to pay from families of affected children. Clin Genet. 2009;75(6):514–21.

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Payne K, Fargher EA, Roberts SA, Tricker K, Elliott RA, Ratcliffe J, Newman WG. Valuing pharmacogenetic testing services: a comparison of patients’ and health care professionals’ preferences. Value Health. 2011;14(1):121–34.

    PubMed  Article  Google Scholar 

  49. 49.

    Chan SL, Wen Low JJ, Lim YW, Finkelstein E, Farooqui MA, Chia KS, Wee HL. Willingness-to-pay and preferences for warfarin pharmacogenetic testing in Chinese warfarin patients and the Chinese general public. Pers Med. 2013;10(2):127–37.

    CAS  Article  Google Scholar 

  50. 50.

    Issa AM, Tufail W, Atehortua N, McKeever J. A national study of breast and colorectal cancer patients’ decision-making for novel personalized medicine genomic diagnostics. Pers Med. 2013;10(3):245–56.

    CAS  Article  Google Scholar 

  51. 51.

    Severin F, Schmidtke J, Mühlbacher A, Rogowski WH. Eliciting preferences for priority setting in genetic testing: a pilot study comparing best-worst scaling and discrete-choice experiments. Eur J Hum Genet. 2013;21(11):1202–8.

    PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Kilambi V, Johnson FR, González JM, Mohamed AF. Valuations of genetic test information for treatable conditions: the case of colorectal cancer screening. Value Health. 2014;17(8):838–45.

    PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Knight SJ, Mohamed AF, Marshall DA, Ladabaum U, Phillips KA, Walsh JM. Value of genetic testing for hereditary colorectal cancer in a probability-based US online sample. Med Decis Mak. 2015;35(6):734–44.

    Article  Google Scholar 

  54. 54.

    Blumenschein P, Lilley M, Bakal J, Christian S. Evaluating stakeholder’s perspective on referred out genetic testing in Canada: a discrete choice experiment. Clin Genet. 2016;89(1):133–8.

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Buchanan J, Wordsworth S, Schuh A. Patients’ preferences for genomic diagnostic testing in chronic lymphocytic leukaemia: a discrete choice experiment. Patient Patient Cent Outcomes Res. 2016;9(6):525–36.

    Article  Google Scholar 

  56. 56.

    Marshall DA, Deal K, Bombard Y, Leighl N, MacDonald KV, Trudeau M. How do women trade-off benefits and risks in chemotherapy treatment decisions based on gene expression profiling for early-stage breast cancer? A discrete choice experiment. BMJ Open. 2016;6(6):1–11.

    Google Scholar 

  57. 57.

    Veldwijk J, Lambooij MS, Kallenberg FG, Van Kranen HJ, Bredenoord AL, Dekker E, Smit HA, De Wit GA. 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.

    PubMed  Article  Google Scholar 

  58. 58.

    Jeong G. Assessment of direct-to-consumer genetic testing policy in Korea based on consumer preference. Public Health Genom. 2017;20(3):166–73.

    Article  Google Scholar 

  59. 59.

    Marshall DA, Gonzalez JM, MacDonald KV, Johnson FR. Estimating preferences for complex health technologies: lessons learned and implications for personalized medicine. Value Health. 2017;20(1):32–9.

    PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Lewis MA, Stine A, Paquin RS, Mansfield C, Wood D, Rini C, Roche MI, Powell CM, Berg JS, Bailey DB. Parental preferences toward genomic sequencing for non-medically actionable conditions in children: a discrete-choice experiment. Genet Med. 2018;20(2):181–9.

    PubMed  Article  Google Scholar 

  61. 61.

    Peyron C, Pélissier A, Béjean S. Preference heterogeneity with respect to whole genome sequencing. A discrete choice experiment among parents of children with rare genetic diseases. Soc Sci Med. 2018;214:125–32.

    PubMed  Article  Google Scholar 

  62. 62.

    Plöthner M, Schmidt K, Schips C, Damm K. Which attributes of whole genome sequencing tests are most important to the general population? Results from a German preference study. Pharmacogenom Pers Med. 2018;11:7.

    Google Scholar 

  63. 63.

    Weymann D, Veenstra DL, Jarvik GP, Regier DA. Patient preferences for massively parallel sequencing genetic testing of colorectal cancer risk: a discrete choice experiment. Eur J Hum Genet. 2018;26(9):1257–65.

    PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Johansson JV, Langenskiöld S, Segerdahl P, Hansson MG, Hösterey UU, Gummesson A, Veldwijk J. Research participants’ preferences for receiving genetic risk information: a discrete choice experiment. Genet Med. 2019;21(10):2381–9.

    Article  Google Scholar 

  65. 65.

    Bereza BG, Coyle D, So DY, Kadziola Z, Wells G, Grootendorst P, Papadimitropoulos EA. Stated preferences for attributes of a CYP2C19 pharmacogenetic test among the general population presented with a hypothetical acute coronary syndrome scenario. ClinicoEcono Outcomes Res. 2020;12:167.

    Article  Google Scholar 

  66. 66.

    Goranitis I, Best S, Christodoulou J, Stark Z, Boughtwood T. The personal utility and uptake of genomic sequencing in pediatric and adult conditions: eliciting societal preferences with three discrete choice experiments. Genet Med. 2020;22(8):1311–9.

    PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Regier DA, Veenstra DL, Basu A, Carlson JJ. Demand for precision medicine: a discrete-choice experiment and external validation study. Pharmacoeconomics. 2020;38(1):57–68.

    PubMed  Article  Google Scholar 

  68. 68.

    Wee JW, Png WY, Wong XY, Kwan YH, Lin YY, Tan DS, Wee HL. Measuring preferences for CYP2C19 genotyping in patients with acute coronary syndrome—a discrete choice experiment. Future Cardiol. 2020;16(6):663–74.

    CAS  PubMed  Google Scholar 

  69. 69.

    Goranitis I, Best S, Stark Z, Boughtwood T, Christodoulou J. The value of genomic sequencing in complex pediatric neurological disorders: a discrete choice experiment. Genet Med. 2021;23(1):155–62.

    PubMed  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Semra Ozdemir.

Ethics declarations


This study was funded by the Lien Centre for Palliative Care, Duke-NUS Medical School, Singapore.

Conflict of interest/Competing interests

The authors declare that they have no competing interests.

Availability of data and material

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Code availability

The code used for this study is available from the corresponding author on reasonable request.

Authors’ contributions

SO participated in the conception of the study, study design, data extraction, data analysis and manuscript writing. All other authors participated in the data extraction, data analysis and manuscript writing. All authors read and approved the final manuscript.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 29 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ozdemir, S., Lee, J.J., Chaudhry, I. et al. A Systematic Review of Discrete Choice Experiments and Conjoint Analysis on Genetic Testing. Patient (2021). https://doi.org/10.1007/s40271-021-00531-1

Download citation