About Us
DCRI's Preference Evaluation Research Group (PrefER) faculty and staff are recognized thought leaders in applying cutting-edge quantitative stated-preference methods, like discrete-choice experiments, best-worse scaling, and prioritization exercises. We have extensive experience in patient engagement and collaborate closely with Duke clinicians and partners from industry, regulatory authorities, and nonprofit organizations to design preference research studies that promote patient-centric care, bringing the patient voice into healthcare decision-making.
Our research informs regulatory decision-making, device and medical product development, clinical trial design, payment and coverage policies, and shared decision-making between patients and their clinicians.
We are highly experienced experts in:
- designing and conducting regulatory benefit-risk evaluations across a broad range of therapeutic areas
- understanding and quantifying risk tolerance, time-equivalent value, willingness-to-pay, and how preferences relate to treatment adherence
- developing and validating decision-support tools (i.e. decision aids) that enable patients, caregivers and physicians to make informed healthcare decisions that align with patient preferences
Jui-Chen Yang, Director of Analytics and Creative Solutions
Jessie Sutphin, Director of Research
Upcoming Events
International Society for Pharmacoeconomics and Outcomes Research
Annual Meeting
Montreal, Quebec, Canada
May 13-16, 2024
How Much Do We Already Know? Building a Stated-Preference Evidence Base for Regulatory, Product Development, and Clinical Decision Applications |
Reed Johnson and Semra Ozdemir |
Using Artificial Intelligence to Predict Patient's Preferences |
Tina Cheng |
Patient Optimism and the Trade-Off Between Kidney Quality and Waiting Time |
Tina Cheng |
Alternative Recruitment Options for Patient Preference Studies: Thinking Outside the Market Research Panel Box |
Semra Ozdemir |
From MAR to SMART: Advanced Methods for Integrating Patient Preferences in Regulatory Science |
Juan Marcos Gonzalez |
Methodological Contributions
The PrefER team continues to develop, evaluate and apply new methods in preference research.
Gonzalez, J. M., Johnson, F. R., & Finkelstein, E. (2024). To pool or not to pool: Accounting for task non-attendance in subgroup analysis. Journal of Choice Modelling, 51. https://doi.org/10.1016/j.jocm.2024.100487
Pooling data from different subgroups offers advantages of shrinking standard errors and simplifying characterization of the data structure. The ability to pool data also facilitates meta-analysis to evaluate consensus among multiple studies and to inform benefit transfer to new choice settings. Testing for poolability requires accounting for differences in response variance or scale among subgroups. This is commonly done by assuming a single scale factor within each subgroup of interest. This assumption may not hold for many subgroups, especially when task non-attendance is present. We use data from a prior DCE study to show that task non-attendance, and by extension the assumption of a single scale factor across subgroups, can lead to inaccurate conclusions when determining poolability. To address this concern, we propose a latent-class/random-parameters Logit (LCRP) model specification that accommodates task non-attendance or other causes of scale differences within subgroups and directly tests for poolability.
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Gonzalez Sepulveda, J. M., Van Houtven, G., Reed, S. D., Webster, S., & Johnson, F. R. (2024). The impact of violations of expected utility theory on choices in the face of multiple risks. Journal of Choice Modelling, 53. https://doi.org/10.1016/j.jocm.2024.100511
Use of preference information to infer risk tolerance has increased in recent years as a way to inform benefit-risk evaluations in regulatory and medical decision making. However, a framework for the measurement of tolerance for multiple uncertain outcomes has not been formalized when choices do not comply with expected utility theory (EUT). We developed a formal analytic framework for the measurement of preferences through choices under uncertainty with multiple risks. Based on the analytic framework, we find that violations of EUT can lead to interaction effects between uncertain outcomes, not just nonlinearities in the disutility of risks. Our framework also implies that measures of risk tolerance derived from utility, such as maximum-acceptable risk, must consider all relevant risks jointly if their effect on choices is expected to violate EUT. Somewhat reassuringly, however, we find that cross-outcome effects are expected to be negligible when the probabilities of other outcomes approach certainty. Finally, we identify a simple test that can help evaluate whether preferences for one uncertain outcome are affected by other uncertain outcomes.
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Gonzalez Sepulveda JM, Townsend M, Waters HC, Brubaker M, Wallace M, Johnson R. Self-stigmatization and treatment preferences: Measuring the impact of treatment labels on choices for depression medications. PLoS One. 2024;19(9) https://doi.org/10.1371/journal.pone.0309562
We developed a discrete-choice experiment (DCE) survey instrument that asked respondents to make choices between hypothetical treatments for major depressive disorder (MDD). The choice questions mimicked the information presented in product inserts and required systematic tradeoffs between treatment efficacy, treatment type, and indication. We calculated how many patients were willing to forgo efficacy to avoid treatments with information associated with self-stigmatization, and how much efficacy they were willing to forgo. We also evaluated the impact of contextualizing the treatment information to reduce self-stigmatization by randomizing respondents who received additional context. Product-label treatment indication can potentially lead to patient self-stigmatization as shown by patients’ avoidance of treatments that are also used to treat schizophrenia. While the effect appears to be relatively small, results indicate that label effects could be pervasive.
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Johnson FR, Adamowicz W, Groothuis-Oudshoorn C. What Can Discrete-Choice Experiments Tell Us about Patient Preferences? An Introduction to Quantitative Analysis of Choice Data. Patient. 2024/07/24 2024;doi:10.1007/s40271-024-00705-7
This paper is an introduction to statistical analysis of choice data using example data from a simple DCE. It describes the layout of the analysis dataset, types of variables contained in the dataset, and how to identify response patterns in the data indicating data quality. Model-specification options include linear models with continuous attribute levels and non-linear continuous and categorical attribute levels. Advantages and disadvantages of conditional logit, mixed logit, and latent-class analysis are discussed and illustrated using the example DCE data. Readers are provided with links to various software programs for analyzing choice data. References are provided on topics for which there currently is limited consensus and on more advanced techniques to guide readers interested in exploring choice-modeling challenges in greater depth. Supplementary materials include the simulated example data used to illustrate modeling approaches, together with R and Matlab code to reproduce the estimates shown.
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Ozdemir, S., Gonzalez, J. M., Bansal, P., Huynh, V. A., Sng, B. L., & Finkelstein, E. (2024). Getting it right with discrete choice experiments: Are we hot or cold? Soc Sci Med, 348, 116850. https://doi.org/10.1016/j.socscimed.2024.116850 |
In 2019, the US Food and Drug Administration issued a warning that symptomatic relief from claudication using paclitaxel-coated devices might be associated with an increase in mortality over 5 years. We designed a discrete-choice experiment (DCE) to quantify tradeoffs that patients would accept between a decreased risk of clinically driven target-vessel revascularization (CDTVR) and increased mortality risk. Our work is a model for future collaborative efforts including patients, clinicians, researchers, regulatory partners, and industry to further define patient preferences and goals of care.
Wallace MJ, Weissler EH, Yang J-C, Brotzman L, Corriere MA, Secemsky EA, Sutphin J, Johnson FR, Gonzalez JM, Tarver ME, Saha A, Chen AL, Gebben DJ, Malone M, Farb A, Babalola O, Rorer EM, Zikmund-Fisher BJ, Reed SD. Using separate single-outcome risk presentations instead of integrated multioutcome formats improves comprehension in discrete choice experiments. Med Decis Making. 2024 [cited 2024 Jul 23]; Available from: https://pubmed.ncbi.nlm.nih.gov/38903012/ |
Evaluating poolability by considering subgroup means, as is usually done, can provide an incomplete assessment of differences in preferences across several groups or samples. Comparing means only can overlook the fact that subgroups can share the same set of preference types, even if distributed differently. We developed a novel approach to control for distributional differences in preferences across subgroups. In a study of family caregivers across five countries we found that our approach afforded us greater flexibility to accommodate respondents from multiple countries within the same model of preferences. This, in turn, helped us shrink standard errors of the preference estimates without increasing the study sample size. Gonzalez JM, Johnson FR, Finkelstein E. To pool or not to pool: Accounting for task non-attendance in subgroup analysis. J Choice Model. 2024;51(100487):100487. Available from: http://dx.doi.org/10.1016/j.jocm.2024.100487 |
Conventional estimates of maximum-acceptable risks (MARs) evaluate only one adverse-event risk at a time, but patients are often exposed to multiple treatment-related adverse-event risks. To account for this, we developed Simultaneous Maximum Acceptable Risk Thresholds (SMART) to represent combinations of maximum risk levels that would be jointly acceptable for specific treatment benefits. Fairchild AO, Reed SD, Gonzalez JM. Method for calculating the Simultaneous Maximum Acceptable Risk Threshold (SMART) from discrete-choice experiment benefit-risk studies. Med Decis Making. 2023;43(2):227–38. Available from: http://dx.doi.org/10.1177/0272989x221132266
We extended methods for SMARTs to produce SMART contours which account for uncertainty associated with acceptable tradeoffs between benefits and two adverse-event risks. In this paper, we apply this approach to three previously published patient-preference studies. Montano-Campos JF, Gonzalez JM, Rickert T, Fairchild AO, Levitan B, Reed SD. Use of patient preferences data regarding multiple risks to inform regulatory decisions. MDM Policy Pract. 2023;8(1):238146832211487. Available from: http://dx.doi.org/10.1177/23814683221148715 |
Medication non-adherence remains a significant challenge in healthcare, impacting treatment outcomes and the overall effectiveness of medical interventions. This article introduces a novel approach to understanding and predicting medication non-adherence by integrating patient preferences with patients’ beliefs, efficacy expectations, and perceived costs. Existing theoretical models often fall short in quantifying the impact of barrier removal on medication adherence and struggle to address cases where patients consciously choose not to follow prescribed medication regimens. In response to these limitations, this study presents an empirical framework that seeks to provide a quantifiable model for both individual and population-level prediction of non-adherence under different scenarios. Muiruri C, van den Broek-Altenburg E, Bosworth H, Cené C, Gonzalez J. A quantitative framework for medication non-adherence: Integrating patient treatment expectations and preferences. Patient Prefer Adherence [Internet]. 2023;17:3135–45. Available from: http://dx.doi.org/10.2147/ppa.s434640 |
Approaches that combine patient preferences and clinical evidence can facilitate effective patient-provider communication and more patient-centric healthcare decisions. However, diagnosing individual-level preferences is challenging. We proposed an approach to efficiently diagnose the preferences of patients for outcomes of treatment alternatives by leveraging prior information on patient preferences. Our approach allows the generation of adaptive choice questions to identify a patient's proximity to known preference phenotypes with as little as two questions. The diagnosis of preferences can help physicians discuss relevant aspects of the treatment options and proactively address patient concerns during the clinical encounter. Gonzalez Sepulveda JM, Johnson FR, Reed SD, Muiruri C, Hutyra CA, Mather RC III. Patient-preference diagnostics: Adapting stated-preference methods to inform effective shared decision making. Med Decis Making [Internet]. 2023;43(2):214–26. Available from: http://dx.doi.org/10.1177/0272989x221115058 |
Gonzalez Sepulveda, J. M., Reed, S. D., & Telen, M. (2024). The critical role of symptom experience on risk tolerance for gene therapy. Blood Adv, 8(21), 5721–5722. https://doi.org/10.1182/bloodadvances.2024013811 |
Gonzalez Sepulveda, J. M., Van Houtven, G., Reed, S. D., Webster, S., & Johnson, F. R. (2024). The impact of violations of expected utility theory on choices in the face of multiple risks. Journal of Choice Modelling, 53. https://doi.org/10.1016/j.jocm.2024.10051 |
Reed, S. D., Yang, J.-C., Wallace, M. J., Sutphin, J., Johnson, F. R., Ozdemir, S., Delgado S., Goates S., Harbert , N., Lo M., Rajagopalan B., Ip, J.E., Al-Khatib, S. M. (2024). Patient Preferences for Features Associated With Leadless Versus Conventional Transvenous Cardiac Pacemakers. Circ Cardiovasc Qual Outcomes, 17(12), e011168. https://doi.org/10.1101/2024.04.19.24306110 |
Reed, S. D., Harrington, J. L., Morin, D. P., Saba, S. F., Montgomery, J. A., Harrison, R. W., Frisch D, Viethen T, Tamm M, Xiao J, Mundl H, Coppolecchia R, Yang J-C, Wallace M.J., Gonzalez J.M., Patel, M. R. (2024). Participant Engagement and Preference Study for Clinical Outcomes Associated With Atrial Fibrillation: The PEARL-AF Study. JACC Adv, 3(12), 101370. https://doi.org/10.1016/j.jacadv.2024.101370 |
Reed, S. D., Sutphin, J., Wallace, M. J., Gonzalez, J. M., Yang, J.-C., Reed Johnson, F., … Corriere, M. A. (2024). Quantifying patients' preferences on tradeoffs between mortality risk and reduced need for target vessel revascularization for claudication. Vasc Med, 29(6), 675–683. https://doi.org/10.1177/1358863X241290233 |
Johnson FR, Sheehan JJ, Ozdemir S, Wallace M, Yang JC. How Much Better is Faster? Empirical Tests of QALY Assumptions in Health-Outcome Sequences. PharmacoEconomics. 2024. https://doi.org/10.1007/s40273-024-01437-0 |
Gonzalez Sepulveda JM, Townsend M, Waters HC, Brubaker M, Wallace M, Johnson R. Self-stigmatization and treatment preferences: Measuring the impact of treatment labels on choices for depression medications. PLoS One. 2024;19(9) https://doi.org/10.1371/journal.pone.0309562 |
Swaminathan A, Yang J-C, Ding H, Grover K, Coles T, Schelfhout J, Johnson FR. Patient preferences for the treatment of chronic cough: a discrete choice experiment. BMJ Open Respiratory Research. 2024;11(1):e001888. doi:10.1136/bmjresp-2023-001888 |
Huang L, Srivastava A, Fairchild A, Whittington D, Johnson R. Young Adult and Parent Willingness to Pay for Meningococcal Serogroup B Vaccination. MDM Policy & Practice. 2024;9(2). doi:10.1177/23814683241264280 |
Johnson FR, Adamowicz W, Groothuis-Oudshoorn C. What Can Discrete-Choice Experiments Tell Us about Patient Preferences? An Introduction to Quantitative Analysis of Choice Data. Patient. 2024/07/24 2024;doi:10.1007/s40271-024-00705-7 |
Ozdemir, S., Gonzalez, J. M., Bansal, P., Huynh, V. A., Sng, B. L., & Finkelstein, E. (2024). Getting it right with discrete choice experiments: Are we hot or cold? Soc Sci Med, 348, 116850. https://doi.org/10.1016/j.socscimed.2024.116850 |
Larson, A. N., Marks, M. C., Gonzalez Sepulveda, J. M., Newton, P. O., Devlin, V. J., Peat, R., … the Harms Study Group. (2024). Non-Fusion Versus Fusion Surgery in Pediatric Idiopathic Scoliosis: What Trade-Offs in Outcomes Are Acceptable for the Patient and Family? J Bone Joint Surg Am, 106(1), 2–9. https://doi.org/10.2106/JBJS.23.00503 |
Coles T, McFatrich M, Ding H, Lucas N, Daniell E, Swaminathan A, Schelfhout J, Johnson FR. Quality of Life in Adults with Chronic Cough: A Mixed Methods Study Informing the Development of a Quantitative Patient Preference Study. Patient. 2024;17(3):253-262. doi:10.1007/s40271-023-00654-7 |
Ozdemir S, Quaife M, Mohamed A, Normal R. An Overview of Data Collection in Health Preference Research. The Patient 2024; https://doi.org/10.1007/s40271-024-00695-6. |
Finkelstein E, Ozdemir S, Huynh VA, Chay J, Muhlbacher A, Tan HK. Navigating Public Policy Responses to a Pandemic: The Balancing Act Between Physical Health, Mental Health, and Household Income. Value in Health 2024; 27(8): 1121-1129. https://doi.org/10.1016/j.jval.2024.04.019. |
Bansal P, Kim EJ, Ozdemir S. Discrete Choice Experiments with Eye-tracking: How Far We Have Come and Ways Forward. Journal of Choice Modelling 2024; 51: 100478. https://doi.org/10.1016/j.jocm.2024.100478. |
van Til J, Pearce A, Ozdemir S, Hollin IL, Peay HL, Wu AW, Ostermann J, Deal K, Craig BM. Role Preferences in Medical Decision Making: Relevance and Implications for Health Preference Research. The Patient 2024; 17: 3-12. https://doi.org/10.1007/s40271-023-00649-4. |
Johnson FR, Sheehan JJ, Ozdemir S, Wallace M, Yang JC. How Much Better is Faster? Empirical Tests of QALY Assumptions in Health-Outcome Sequences. PharmacoEconomics 2024; 43(1): 45-52. https://doi.org/10.1007/s40273-024-01437-0.
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Marshall DA, Veldwijk J, Janssen EM, Reed SD. Stated-preference survey design and testing in health applications. Patient 2024 [cited 2024 Jul 23]; Available from: https://pubmed.ncbi.nlm.nih.gov/38294720/ |
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Hung A, Yang J-C, Wallace M, Zwischenberger BA, Vemulapalli S, Mentz RJ, et al. Patient risk–benefit preferences for Transcatheter versus surgical mitral valve repair. J Am Heart Assoc 2024;13(6). Available from: http://dx.doi.org/10.1161/jaha.123.032807 |
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Finkelstein EA, Ozdemir S, Huynh VA, Chay J, Mühlbacher A, Tan HK. Navigating public policy responses to a pandemic: The balancing act between physical health, mental health, and household income. Value Health 2024; Available from: http://dx.doi.org/10.1016/j.jval.2024.04.019 |
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Ozdemir S, Quaife M, Mohamed AF, Norman R. An overview of data collection in health preference research. Patient 2024; Available from: http://dx.doi.org/10.1007/s40271-024-00695-6 |
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Bansal P, Kim E-J, Ozdemir S. Discrete choice experiments with eye-tracking: How far we have come and ways forward. J Choice Model 2024;51(100478):100478. Available from: http://dx.doi.org/10.1016/j.jocm.2024.100478 |
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Ozdemir S, Chaudhry I, Malhotra C, Teo I, Finkelstein EA, Singh R, et al. Goals of care among patients with advanced cancer and their family caregivers in the last years of life. JAMA Netw Open. 2024 [cited 2024 Jul 23];7(4):e245866. Available from: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2817443 |
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Ramakrishnan C, Widjaja N, Malhotra C, Finkelstein E, Khan BA, Ozdemir S, et al. Unravelling complex choices: multi-stakeholder perceptions on dialysis withdrawal and end-of-life care in kidney disease. BMC Nephrol. 2024;25(1). Available from: http://dx.doi.org/10.1186/s12882-023-03434-5 |
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van Til JA, Pearce A, Ozdemir S, Hollin IL, Peay HL, Wu AW, et al. Role preferences in medical decision making: Relevance and implications for health preference research. Patient. 2024;17(1):3–12. Available from: http://dx.doi.org/10.1007/s40271-023-00649-4 |
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Ozdemir S, Ng S, Huynh VA, Mühlbacher A, Tan HK, Finkelstein EA. Trade-offs between vaccine effectiveness and vaccine safety: Personal versus policy decisions. Pharmacoeconom Open. 2023 [cited 2024 Jul 23];7(6):915–26. Available from: https://pubmed.ncbi.nlm.nih.gov/37819585/ |
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Gonzalez Sepulveda JM, Yang J-C, Reed SD, Lee T-H, Ng X, Stothers S, et al. Preferences for potential benefits and risks for gene therapy in the treatment of sickle cell disease. Blood Adv. 2023;7(23):7371–81. Available from: http://dx.doi.org/10.1182/bloodadvances.2023009680
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Gonzalez JM, Grover K, Leblanc TW, Reeve BB. Did a bot eat your homework? An assessment of the potential impact of bad actors in online administration of preference surveys. PLoS One. 2023;18(10):e0287766. Available from: http://dx.doi.org/10.1371/journal.pone.0287766
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Johnson FR, Gonzalez JM, Sheehan JJ, Reed SD. How much better is faster? Value adjustments for health-improvement sequences. Pharmacoeconomics. 2023;41(8):845–56. Available from: http://dx.doi.org/10.1007/s40273-023-01266-7 |
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Montano-Campos JF, Gonzalez JM, Rickert T, Fairchild AO, Levitan B, Reed SD. Use of patient preferences data regarding multiple risks to inform regulatory decisions. MDM Policy Pract. 2023;8(1):238146832211487. Available from: http://dx.doi.org/10.1177/23814683221148715
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Shammas RL, Hung A, Mullikin A, Sergesketter AR, Lee CN, Reed SD, et al. Patient preferences for postmastectomy breast reconstruction. JAMA Surg. 2023;158(12):1285. Available from: http://dx.doi.org/10.1001/jamasurg.2023.4432 |
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Rincon-Gonzalez L, Selig WKD, Hauber B, Reed SD, Tarver ME, Chaudhuri SE, et al. Leveraging patient preference information in medical device clinical trial design. Ther Innov Regul Sci. 2023 [cited 2024 Jul 23];57(1):152–9. Available from: https://pubmed.ncbi.nlm.nih.gov/36030334/ |
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Johnson FR. Comment on: Taking the shortcut: Simplifying heuristics in discrete choice experiments. Patient. 2023;16(4):289–92. Available from: http://dx.doi.org/10.1007/s40271-023-00629-8 |
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Johnson FR, Fairchild A, Whittington D, Srivastava AK, Gonzalez JM, Huang L. How does the public evaluate vaccines for low-incidence, severe-outcome diseases? A general-population choice experiment. Patient. 2023;16(2):139–51. Available from: http://dx.doi.org/10.1007/s40271-022-00602-x |
Sutphin J, Okafor S, Reed SD, Deb A, Silver J, Wallace MJ, Yang JC, Abi Hachem R, Jang DW. Patient preferences for treatment of chronic rhinosinusitis with nasal polyps. Rhinology. 2025 Apr 1;63(2):245-247. doi: 10.4193/Rhin24.475. PMID: 39806809. |
Shammas, Ronnie L. MD; Hung, Anna; Ramkalawan, Janel; Mullikin, Alexandria; Moore, Angelo; Greenup, Rachel A.; Hollenbeck, Scott T.; Phillips, Brett T.; Matros, Evan; Reed, Shelby D.; Lee, Clara N., Preferences for Care Among African American Women Considering Postmastectomy Breast Reconstruction. Plastic and Reconstructive Surgery ():10.1097/PRS.0000000000012003, February 04, 2025. | DOI: 10.1097/PRS.0000000000012003 |
Marshall, D.A., Veldwijk, J., Janssen, E.M. et al. Stated-Preference Survey Design and Testing in Health Applications. Patient 18, 187–197 (2025). https://doi.org/10.1007/s40271-023-00671-6 |
Veldwijk, J., Ozdemir, S., Bui, M., Gonzalez, JM, Groothuis-Oudshoorn, CGM, Hauber B, Tervonen T. Transferability of Preferences; for Better or ….?. Patient 18, 97–100 (2025). https://doi.org/10.1007/s40271-025-00728-8 |
Devlin, V. J., Gonzalez Sepulveda, J. M., Gebben, D., Larson, A. N., Marks, M. C., Newton, P., ... & Lonner, B. (2025). Fusion versus Non-fusion Surgery for Pediatric Idiopathic Scoliosis: Preferences of Patients, Caregivers, and Patient-Caregiver Dyads. Global Spine Journal, 21925682251330217. |
Babalola, Olufemi, Richard Hass, John McAna, Manav Segal, Juan Marcos Gonzalez, and Olajumoke Fadugba. "Quantifying Patient Preferences for Treatments for Refractory Chronic Spontaneous Urticaria." Journal of Allergy and Clinical Immunology: Global (2025): 100468. |
Sepulveda, Juan Marcos Gonzalez, Jui-Chen Yang, Alicja Mastylak, Elaine M. Wells-Gray, Landon Grace, and Stephen Fransen. "Quantifying Patient Preferences and Expectations About Diabetic Retinopathy Monitoring." JAMA ophthalmology 143, no. 2 (2025): 91-98. |
Marsh, Kevin, Juan Marcos Gonzalez Sepulveda, Conny Berlin, Bennett Levitan, Marco Boeri, Catharina GM Groothuis-Oudshoorn, Norah L. Crossnohere et al. "What Next for the Science of Patient Preference? Interoperability, Standardization, and Transferability." The Patient-Patient-Centered Outcomes Research (2025): 1-8. |
Training Opportunities
Attendees will learn how to apply a well-defined conceptual framework to the design, implementation, and critical assessment of stated-preference surveys. Case studies will illustrate strengths and limitations of various preference-elicitation methods. These case studies also will demonstrate application of such quantitative preference methods to inform patient-centered health care, health-technology assessment, and regulatory decision making. Participants will complete and evaluate a stated-choice survey during the workshop. An additional half-day session is offered to allow attendees to ask specific questions about their research ideas and projects.
Learn More: https://populationhealth.duke.edu/education/summer-institute-2024