Preference Evaluation Research Group (PrefER)

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.

Reed SD, Sutphin J, Wallace MJ, et al. Quantifying patients’ preferences on tradeoffs between mortality risk and reduced need for target vessel revascularization for claudication. Vascular Medicine. 2024;29(6):675-683. Available from: doi:10.1177/1358863X241290233

Patients making benefit-risk tradeoffs have to consider the magnitude of risks which requires consideration of probabilities or percentages. Good risk-communication practices are essential to ensure patients’ understanding, but prior research had not evaluated risk-communications approaches in the context of discrete-choice experiment questions in which multiple risks are shown for alternative treatment profiles.  We designed a six-arm randomized study to compare different approaches to presenting probabilistic treatment risks and evaluated their impact on internal validity across DCE questions.

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