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.
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.
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.
Objective
The aim of this study was to compare online, unsupervised and face-to-face (F2F), supervised valuation of EQ-5D-5L health states using composite time trade-off (cTTO) tasks.
Methods
The ...official EuroQol experimental design and valuation protocol for the EQ-5D-5L of 86 health states were implemented in interviewer-assisted, F2F and unsupervised, online studies. Validity of preferences was assessed using prevalence of inconsistent valuations and expected patterns of TTO values. Respondent task engagement was measured using number of trade-offs and time per task. Trading patterns such as better-than-dead only was compared between modes. Value sets were generated using linear regression with a random intercept (RILR). Value set characteristics such as range of scale and dimension ranking were evaluated between modes.
Results
Five hundred one online and 1,134 F2F respondents completed the surveys. Mean elicited TTO values were higher online than F2F when compared by health state severity. Compared to F2F, a larger proportion of online respondents did not assign the poorest EQ-5D-5L health state (i.e., 55555) the lowest TTO value (Online 41.3% F2F 12.2%) (
p
< 0.001). A higher percentage of online cTTO tasks were completed in 3 trade-offs or fewer (Online 15.8% F2F 3.7%), (
p
< 0.001). When modeled using the RILR, the F2F range of scale was larger than online (Online 0.600 F2F 1.307) and the respective dimension rankings differed.
Conclusions
Compared to F2F data, TTO tasks conducted online had more inconsistencies and decreased engagement, which contributed to compromised data quality. This study illustrates the challenges of conducting online valuation studies using the TTO approach.
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.
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).
Abstract Regulatory decisions are often based on multiple clinical end points, but the perspectives used to judge the relative importance of those end points are predominantly those of expert ...decision makers rather than of the patient. However, there is a growing awareness that active patient and public participation can improve decision making, increase acceptance of decisions, and improve adherence to treatments. The assessment of risk versus benefit requires not only information on clinical outcomes but also value judgments about which outcomes are important and whether the potential benefits outweigh the harms. There are a number of mechanisms for capturing the input of patients, and regulatory bodies within the European Union are participating in several initiatives. These can include patients directly participating in the regulatory decision-making process or using information derived from patients in empirical studies as part of the evidence considered. One promising method that is being explored is the elicitation of “patient preferences.” Preferences , in this context, refer to the individual’s evaluation of health outcomes and can be understood as statements regarding the relative desirability of a range of treatment options, treatment characteristics, and health states. Several methods for preference measurement have been proposed, and pilot studies have been undertaken to use patient preference information in regulatory decision making. This article describes how preferences are currently being considered in the benefit-risk assessment context, and shows how different methods of preference elicitation are used to support decision making within the European context.
This study aimed to assess public preferences for the allocation of donor organs in Germany with the focus on ethical principles of distributive justice. We performed a discrete choice experiment ...(DCE) using a self-completed online questionnaire. Based on a systematic review and focus group discussions, six attributes, each with two–four levels, were selected (corresponding principle of distributive justice in brackets), including (1) life years gained after transplantation (principle of distributive justice: effectiveness/benefit – utilitarianism), (2) quality of life after transplantation (effectiveness/benefit – utilitarianism), (3) chance for a further donor organ offer (principle of distributive justice: medical urgency – favouring the worst-off), (4) age (medical and social risk factors: sociodemographic status), (5) registered donor (principle of distributive justice: value for society), and (6) individual role in causing organ failure (principle of distributive justice: own fault). Each respondent was presented with eight choice sets and asked to choose between two hypothetical patients without an opt-out. Data were analysed using conditional logit, mixed logit and latent class models. The final sample comprised 1028 respondents. Choice decisions were significantly influenced by all attributes except chance for a further donor organ offer. The attributes of good quality of life after transplantation, younger age, and no individual role in causing organ failure had the greatest impact on choice decisions. Life years gained after transplantation and being a registered donor were less important for the public. The latent class model identified four classes with preference heterogeneities. Respondents preferred to allocate deceased donor organs by criteria related to effectiveness/benefit, whereas medical urgency was of minor importance. Therefore, a public propensity for a rational, utilitarian, ethical model of allocation could be identified. Public preferences can help to inform policy to warrant socially responsible allocation systems and thus improve organ donation rates.
•Good quality of life after transplantation and younger age were the most important attributes.•Lower chance for a further donor organ offer was the least important attribute.•Trade-offs between medical urgency and effectiveness were not observable in this study.•Our DCE study linked these preferences with principles of distributive justice.•Understanding of public preferences can help to increase the willingness to donate.
To assess patient preferences for benefits and risks in hemophilia A treatment.
A systematic literature search and pretest interviews were conducted to determine the most patient-relevant endpoints ...in terms of effects, risks, and administration of hemophilia A treatments. A Best–Worst Scaling (BWS; Case 3 or multiprofile case) approach was applied in a structured questionnaire. Patients were surveyed by interviewers in a computer-assisted personal interview. Treatments in the choice scenarios comprised bleeding frequency per year, application type, risk of thromboembolic event risk, and inhibitor development. Each respondent answered 13 choice tasks, including 1 dominant task, comparing 3 treatment profiles. Data were analyzed using a mixed logit model (random-parameters logit).
Data from 57 patients were used. The attributes “bleeding frequency per year” and “inhibitor development” had the greatest impact on respondents’ choice decisions. Patients disliked being at risk of inhibitor development more than being at risk of thromboembolic events. The type of application, whether intravenous or subcutaneous, was of less importance for patients. There was a significant preference variation for all attributes.
Patients value low frequency of bleeding per year and low risk of development of inhibitors the most. An increase of risk and frequency would significantly decrease the impact on choice decisions. The type of application does not seem to influence the choice decision very much compared with the other attributes. Regarding preference heterogeneity, further analysis is needed to identify subgroups among patients and their characteristics. This may help to adapt individually patient-tailored treatment alternatives for hemophilia A patients.
•Among the four attributes defining hemophilia A treatments, “bleeding frequency per year” and “development of inhibitors” were most important and therefore had the highest impact on patients’ choices.•The attributes “type of application” and “risk of thromboembolic events” were less important for hemophilia A patients.•The mixed logit model and the standard deviations showed significant preference heterogeneity for all attributes in the sample.
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