Introduction
Discrete choice experiments (DCE) are increasingly being conducted using online panels. However, the comparability of such DCE-based preferences to traditional modes of data collection ...(e.g., in-person) is not well established. In this study, supervised, face-to-face DCE was compared with its unsupervised, online facsimile on face validity, respondent behavior, and modeled preferences.
Methods
Data from face-to-face and online EQ-5D-5L health state valuation studies were compared, in which each used the same experimental design and quota sampling procedure. Respondents completed 7 binary DCE tasks comparing 2 EQ-5D-5L health states presented side by side (health states A and B). Data face validity was assessed by comparing preference patterns as a function of the severity difference between 2 health states within a task. The prevalence of potentially suspicious choice patterns (i.e., all As, all Bs, and alternating As/Bs) was compared between studies. Preference data were modeled using multinomial logit regression and compared based on dimensional contribution to overall scale and importance ranking of dimension-levels.
Results
One thousand five Online respondents and 1,099 face-to-face screened (F2FS) respondents were included in the main comparison of DCE tasks. Online respondents reported more problems on all EQ-5D dimensions except for Mobility. The face validity of the data was similar between comparators. Online respondents had a greater prevalence of potentially suspicious DCE choice patterns (Online: 5.3% F2FS 2.9%, P = 0.005). When modeled, the relative contribution of each EQ-5D dimension differed between modes of administration. Online respondents weighed Mobility more importantly and Anxiety/Depression less importantly.
Discussion
Although assessments of face validity were similar between Online and F2FS, modeled preferences differed. Future analyses are needed to clarify whether differences are attributable to preference or data quality variation between modes of data collection.
Objective The objective of this study was to identify, document, and weight attributes of a pain medication that are relevant from the perspective of patients with chronic pain. Within the ...sub-population of patients suffering from "chronic neuropathic pain", three groups were analyzed in depth: patients with neuropathic back pain, patients with painful diabetic polyneuropathy, and patients suffering from pain due to post-herpetic neuralgia. The central question was: "On which features do patients base their assessment of pain medications and which features are most useful in the process of evaluating and selecting possible therapies?" Methods A detailed literature review, focus groups with patients, and face-to-face interviews with widely recognized experts for pain treatment were conducted to identify relevant treatment attributes of a pain medication. A pre-test was conducted to verify the structure of relevant and dominant attributes using factor analyses by evaluating the most frequently mentioned representatives of each factor. The Discrete-Choice Experiment (DCE) used a survey based on self-reported patient data including sociodemographics and specific parameters concerning pain treatment. Furthermore, the neuropathic pain component was determined in all patients based on their scoring in the painDETECT® questionnaire. For statistical data analysis of the DCE, a random effect logit model was used and coefficients were presented. Results A total of 1,324 German patients participated in the survey, of whom 44 % suffered from neuropathic back pain (including mixed pain syndrome), 10 % complained about diabetic polyneuropathy, and 4 % reported pain due to post-herpetic neuralgia. A total of 36 single quality aspects of pain treatment, detected in the qualitative survey, were grouped in 7 dimensions by factor analysis. These 7 dimensions were used as attributes for the DCE. The DCE model resulted in the following ranking of relevant attributes for treatment decision: "no character change", "less nausea and vomiting", "pain reduction" (coefficient: >0.9 for all attributes, "high impact"), "rapid effect", "low risk of addiction" (coefficient ~0.5, "middle impact"), "applicability with comorbidity" (coefficient ~0.3), and "improvement of quality of sleep" (coefficient ~0.25). All attributes were highly significant (p < 0.001). Conclusions The results were intended to enable early selection of an individualized pain medication. The results of the study showed that DCE is an appropriate means for the identification of patient preferences when being treated with specific pain medications. Due to the fact that pain perception is subjective in nature, the identification of patients preferences will enable therapists to better develop and implement patient-oriented treatment of chronic pain. It is therefore essential to improve the therapists understanding of patient preferences in order to make decisions concerning pain treatment. DCE and direct assessment should become valid instruments to elicit treatment preferences in chronic pain.
Stroke is a common, serious, and disabling healthcare problem with increasing incidence and prevalence. Following a stroke, identifying the factors associated with decisions about rehabilitation ...interventions is important to assess rehabilitation after stroke. The aim is to guide clinical staff to make patient-centered decisions. Fundamentally, decision makers cannot draw on evidence to consider the relevance of distinct functions and activities from the patient's perspective. Until now, outcomes of rehabilitation are generally categorized using the International Classification of Functioning, Disability and Health (ICF). This can be seen as a conceptual basis for the assessment of health and disability. Since the ICF does not distinguish importance between these aspects there is a need to value the most important clinical factors as well as related activities from a patients and public perspective to help guide therapists in effectively designing post-acute rehabilitation care for individuals following stroke. The research question is which ICF body functions and activities are of value to stroke patients? Which trade-offs are patients willing to make within the core elements? Health preference research (HPR) answers the need to develop additional preference weights for certain ICF dimensions. Patient preference information (PPI) values health conditions based on the ICF from a patient perspective.
In this study we conduct three best-worst scaling (BWS) experiments to value body function and activities from patients' and public perspective. Out of all ICF dimensions this research covers health conditions relevant to stroke patients in terms of body function, perception, and activities of daily living. Stroke patients as well as members of the general population will be recruited to participate in the online BWS surveys. Fractional, efficient designs are applied regarding the survey design. Conditional and multinominal logit analyses will be used as the main analysis method, with the best-worst count analysis as a secondary analysis. The survey is being piloted prior to commencing the process of data collection. Results are expected by the autumn of 2023.
The research will add to the current literature on clinical decision-making in stroke rehabilitation and the value of certain body functions as well as related activities in neurorehabilitation. Moreover, the study will show whether body functions and activities that are currently equally weighted in international guidelines are also equally important from the point of view of those affected, or whether there are disconcordances in terms of differences between public judgements and patients' preferences.
To examine subgroup-specific treatment preferences and characteristics of patients with hemophilia A.
Best-Worst Scaling (BWS) Case 3 (four attributes: application type; bleeding frequencies/year; ...inhibitor development risk; thromboembolic events of hemophilia A treatment risk) conducted via online survey. Respondents chose the best and the worst option of three treatment alternatives. Data were analyzed via latent class model (LCM), allowing capture of heterogeneity in the sample. Respondents were grouped into a predefined number of classes with distinct preferences.
The final dataset contained 57 respondents. LCM analysis segmented the sample into two classes with heterogeneous preferences. Preferences within each were homogeneous. For class 1, the most decisive factor was bleeding frequency/year. Respondents seemed to focus mainly on this in their choice decisions. With some distance, inhibitor development was the second most important. The remaining attributes were of far less importance for respondents in this class. Respondents in class 2 based their choice decisions primarily on inhibitor development, also followed, by some distance, the second most important attribute bleeding frequency/year. There was statistical significance (P < 0.05) between the number of annual bleedings and the probability of class membership.
The LCM analysis addresses heterogeneity in respondents' choice decisions, which helps to tailor treatment alternatives to individual needs. Study results support clinical and allocative decision-making and improve the quality of interpretation of clinical data.
Objective. To examine whether personality traits, particularly conscientiousness and agreeableness, were associated with systematic differences in health outcome preferences in cancer treatment ...scenarios among second-year Doctor of Pharmacy students.
Methods. An online survey that quantified outcome preferences using profile best-worst scaling tasks was administered to pharmacy students (n=185). The Big Five personality inventory was used to categorize respondents into tertile-based levels of each trait. Treatment-related health outcomes were described using the EQ-5D-Y system and framed with hypothetical cancer treatment scenarios. Preferences were obtained using count analysis for each treatment-related outcome, and differences based on the level of trait were tested using analysis of variance. Logistic regression was used to test for significant associations between higher levels of a trait and choosing dead over a severe health state.
Results. Higher conscientiousness was associated with students who had an approximately 20% more positive preference for “no problems” in the Usual Activities and Pain/Discomfort attributes, as well as a 19% more negative preference for “a lot of problems” in the Pain/Discomfort attribute. No differences in treatment preferences were observed across agreeableness tertiles. Higher levels of personality traits were not significantly associated with choosing death over being in moderate health.
Conclusion. Conscientiousness appears to be a factor in treatment-related outcome preferences among pharmacy students. Individuals with higher levels of conscientiousness may be more likely to recommend treatments that are less likely to cause pain or discomfort and negatively impact a patient’s usual activities.
Abstract Stated-preference methods are a class of evaluation techniques for studying the preferences of patients and other stakeholders. While these methods span a variety of techniques, ...conjoint-analysis methods—and particularly discrete-choice experiments (DCEs)—have become the most frequently applied approach in health care in recent years. Experimental design is an important stage in the development of such methods, but establishing a consensus on standards is hampered by lack of understanding of available techniques and software. This report builds on the previous ISPOR Conjoint Analysis Task Force Report: Conjoint Analysis Applications in Health—A Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. This report aims to assist researchers specifically in evaluating alternative approaches to experimental design, a difficult and important element of successful DCEs. While this report does not endorse any specific approach, it does provide a guide for choosing an approach that is appropriate for a particular study. In particular, it provides an overview of the role of experimental designs for the successful implementation of the DCE approach in health care studies, and it provides researchers with an introduction to constructing experimental designs on the basis of study objectives and the statistical model researchers have selected for the study. The report outlines the theoretical requirements for designs that identify choice-model preference parameters and summarizes and compares a number of available approaches for constructing experimental designs. The task-force leadership group met via bimonthly teleconferences and in person at ISPOR meetings in the United States and Europe. An international group of experimental-design experts was consulted during this process to discuss existing approaches for experimental design and to review the task force’s draft reports. In addition, ISPOR members contributed to developing a consensus report by submitting written comments during the review process and oral comments during two forum presentations at the ISPOR 16th and 17th Annual International Meetings held in Baltimore (2011) and Washington, DC (2012).
About the Authors: Axel C. Mühlbacher Roles Conceptualization, Methodology, Project administration, Writing – original draft, Writing – review & editing * E-mail: muehlbacher@hs-nb.de Affiliations ...Health Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany, Gesellschaft für Empirische Beratung GmbH (GEB), Freiburg, Germany ORCID logo https://orcid.org/0000-0003-4402-9211 Andrew Sadler Roles Writing – review & editing Affiliation: Health Economics and Health Care Management, Hochschule Neubrandenburg, Neubrandenburg, Germany Introduction The author of the paper to be discussed claims to have reanalyzed a case study on the efficiency frontier (EF). ...there are several rather significant differences in the methodological approaches which are described and presented in detail in the papers 2, 3. ...we acknowledge that our reporting on the methods used for identification of included studies was incomplete (due to the limited number of studies available at the time of publication). In accordance to the Multi-Criteria Decision Analysis (MCDA) literature we applied and described the following steps in our VAF 9: 1. definition of the decision problem to identify alternatives and decision tasks; 2. identification of relevant indicators and specification of the decision model; 3. performance measurement of each indicator; 4. scoring of indicators (normalization); 5. weighting of normalized indicators; 6. aggregation of indicators; 7. interpretation and analysis of uncertainty.
Highlights • This study used the discrete choice experiments methodology to assess the value judgments that different stakeholders believe should guide resource allocation decision making in genetic ...testing. • Results suggests that respondents highly valued prioritising genetic tests with clear proven medical benefit, for patients that have a high risk of having a condition and with low costs of testing. • The findings also highlight important differences in preferences between clinical geneticists and other experts. Clinical geneticists attached significantly higher values to testing high risk groups and conducting low costs test but lower value to conducting tests where medical benefit is only likely rather than proven. • The findings from this study might serve as a point of reference in decision making and can inform the policy debate on prioritising genetic tests.
Best-worst scaling (BWS), also known as maximum-difference scaling, is a multiattribute approach to measuring preferences. BWS aims at the analysis of preferences regarding a set of attributes, their ...levels or alternatives. It is a stated-preference method based on the assumption that respondents are capable of making judgments regarding the best and the worst (or the most and least important, respectively) out of three or more elements of a choiceset. As is true of discrete choice experiments (DCE) generally, BWS avoids the known weaknesses of rating and ranking scales while holding the promise of generating additional information by making respondents choose twice, namely the best as well as the worst criteria. A systematic literature review found 53 BWS applications in health and healthcare. This article expounds possibilities of application, the underlying theoretical concepts and the implementation of BWS in its three variants: "object case", "profile case", "multiprofile case". This paper contains a survey of BWS methods and revolves around study design, experimental design, and data analysis. Moreover the article discusses the strengths and weaknesses of the three types of BWS distinguished and offered an outlook. A companion paper focuses on special issues of theory and statistical inference confronting BWS in preference measurement.
Abstract Background The German Institute for Quality and Efficiency in Health Care (Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen) adapted the efficiency frontier (EF) approach to ...conform to statutory provisions on cost-effectiveness analysis of health technologies. EF serves as a framework for evaluating cost-effectiveness and indirectly for pricing and reimbursement decisions. Objectives To calculate an EF on the basis of single multidimensional benefit by taking patient preferences and uncertainty into account; to evaluate whether EF is useful to inform decision makers about cost-effectiveness of new therapies; and to find whether a treatment is efficient at given prices demonstrated through a case study on chronic hepatitis C. Methods A single multidimensional benefit was calculated by linear additive aggregation of multiple patient-relevant end points. End points were identified and weighted by patients in a previous discrete-choice experiment (DCE). Aggregation of overall benefit was ascertained using preferences and clinical data. Monte-Carlo simulation was applied. Uncertainty was addressed by price acceptability curve (PAC) and net monetary benefit (NMB). Results The case study illustrates that progress in benefit and efficiency of hepatitis C virus treatments could be depicted very well with the EF. On the basis of cost, effect, and preference data, the latest generations of interferon-free treatments are shown to yield a positive NMB and be efficient at current prices. Conclusions EF was implemented taking uncertainty into account. For the first time, a DCE was used with the EF. The study shows how DCEs in combination with EF, PAC, and NMB can contribute important information in the course of reimbursement and pricing decisions.
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