Divergent preferences for enhanced HIV testing options among high-risk populations in northern Tanzania: a short report.

To achieve the UNAIDS target of diagnosing 95% of all persons living with HIV, enhanced HIV testing services with greater attractional value need to be developed and implemented. We conducted a discrete choice experiment (DCE) to quantify preferences for enhanced HIV testing features across two high-risk populations in the Kilimanjaro Region in northern Tanzania. We designed and fielded a survey with 12 choice tasks to systematically recruited female barworkers and male mountain porters. Key enhanced features included: testing availability on every day of the week, an oral test, integration of a general health check or an examination for sexually transmitted infections (STI) with HIV testing, and provider-assisted confidential partner notification in the event of a positive HIV test result. Across 300 barworkers and 440 porters surveyed, mixed logit analyses of 17,760 choices indicated strong preferences for everyday testing availability, health checks, and STI examinations. Most participants were averse to oral testing and confidential partner notification by providers. Substantial preference heterogeneity was observed within each risk group. Enhancing HIV testing services to include options for everyday testing, general health checks, and STI examinations may increase the appeal of HIV testing offers to high-risk populations.Trial registration: ClinicalTrials.gov identifier: NCT02714140.

Patient Preferences in the Treatment of Hemophilia A: A Best-Worst Scaling Case 3 Analysis.

OBJECTIVE: To assess patient preferences for benefits and risks in hemophilia A treatment. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.

Public preferences for the allocation of donor organs for transplantation: Focus group discussions.

BACKGROUND: Deceased donor organs are scarce resources because of a large supply-and-demand mismatch. This scarcity leads to an ethical dilemma, forcing priority-setting of how these organs should be allocated and whom to leave behind. OBJECTIVE: To explore public preferences for the allocation of donor organs in regard to ethical aspects of distributive justice. METHODS: Focus groups were facilitated between November and December 2018 at Hannover Medical School. Participants were recruited locally. Transcripts were assessed with content analysis using the deductive framework method. All identified and discussed criteria were grouped according to the principles of distributive justice and reported following the COREQ statement. RESULTS: Six focus groups with 31 participants were conducted. Overall, no group made a final decision of how to allocate donor organ; however, we observed that not only a single criterion/principle but rather a combination of criteria/principles is relevant. Therefore, the public wants to allocate organs to save as many lives as possible by both maximizing success for and also giving priority to urgent patients considering the best compatibility. Age, waiting time, reciprocity and healthy lifestyles should be used as additional criteria, while sex, financial status and family responsibility should not, based on aspects of equality. CONCLUSIONS: All participants recognized the dilemma that prioritizing one patient might cause another one to die. They discussed mainly the unclear trade-offs between effectiveness/benefit and medical urgency and did not establish an agreement about their importance. The results suggest a need of preference studies to elucidate public preferences in organ allocation.

Patient Preferences Concerning Alternative Treatments for Neuroendocrine Tumors: Results of the "PIANO-Study".

OBJECTIVES: Neuroendocrine tumors (NETs) are rare, slow-growing malignant tumors. So far, there are no data on patient preferences regarding its therapy. This empirical study aimed to elicit patient preferences in the drug treatment of NET. METHODS: Based on qualitative patient interviews and an analytic hierarchy process, six patient-relevant attributes were analyzed and weighted using a discrete-choice experiment. Patients were recruited with the help of a NET support group. An experimental 3*3 + 6*3 -MNL design was created using NGene. The design consisted of eighty-four choices, divided into seven blocks. Participants were randomly assigned to these blocks. The analysis included random parameter logit and latent class models. RESULTS: A total of 275 participants (51.6 percent female; mean age, 58.4 years) were included. The preference analysis within the random parameter logit model, taking into account the 95 percent confidence interval, showed predominance for the attribute "overall survival." The attributes "response to treatment" and "stabilization of tumor growth" followed. The side effects "nausea/vomiting" and "diarrhea" were considered of relatively equal importance. Latent class analysis of possible subgroup differences revealed three preference patterns. CONCLUSIONS: Preferences can influence therapeutic decisions. Preference analyses indicated that "overall survival" had the strongest influence, with participants clearly weighing outcome attributes higher than side effect attributes. In conclusion, mono-criterial decisions would not fully reflect patient perspectives.

[The Probabilistic Efficiency Frontier: A Value Assessment of Treatment Options in Hepatitis C]. / Die probabilistische Effizienzgrenze: eine Wirtschaftlichkeitsanalyse von Therapieoptionen im Indikationsgebiet Hepatitis C.

BACKGROUND: The German Institute for Quality and Efficiency in Health Care (IQWiG) recommends the concept of the efficiency frontier to assess health care interventions. The efficiency frontier supports regulatory decisions on reimbursement prices for the appropriate allocation of health care resources. Until today this cost-benefit assessment framework has only been applied on the basis of individual patient-relevant endpoints. This contradicts the reality of a multi-dimensional patient benefit. OBJECTIVE: The objective of this study was to illustrate the operationalization of multi-dimensional benefit considering the uncertainty in clinical effects and preference data in order to calculate the efficiency of different treatment options for hepatitis C (HCV). This case study shows how methodological challenges could be overcome in order to use the efficiency frontier for economic analysis and health care decision-making. METHOD: The operationalization of patient benefit was carried out on several patient-relevant endpoints. Preference data from a discrete choice experiment (DCE) study and clinical data based on clinical trials, which reflected the patient and the clinical perspective, respectively, were used for the aggregation of an overall benefit score. A probabilistic efficiency frontier was constructed in a Monte Carlo simulation with 10000 random draws. Patient-relevant endpoints were modeled with a beta distribution and preference data with a normal distribution. The assessment of overall benefit and costs provided information about the adequacy of the treatment prices. The parameter uncertainty was illustrated by the price-acceptability-curve and the net monetary benefit. RESULTS: Based on the clinical and preference data in Germany, the interferon-free treatment options proved to be efficient for the current price level. The interferon-free therapies of the latest generation achieved a positive net cost-benefit. Within the decision model, these therapies showed a maximum overall benefit. Due to their high additional benefit and approved prices, the therapies lie above of the extrapolated efficiency frontier, which suggests that these options have efficient reimbursement prices. Considering uncertainty, even a higher price would have resulted in a positive cost-benefit ratio. CONCLUSION: IQWiG's efficiency frontier was used to assess the value of different treatment options in HCV. This study demonstrates that the probabilistic efficiency frontier, price-acceptability-curve and the net monetary benefit can contribute essential information to reimbursement decisions and price negotiations.

PATIENT PRIORITIES FOR TREATMENT ATTRIBUTES IN ADJUNCTIVE DRUG THERAPY OF SEVERE HYPERCHOLESTEROLEMIA IN GERMANY: AN ANALYTIC HIERARCHY PROCESS.

OBJECTIVES: Severe hypercholesterolemia is a major cause of death in coronary heart disease. New adjunctive drug therapies have passed authorization processes and been launched recently. So far it is not known which properties of the new treatment options generate the highest benefit for patients. The aim was to evaluate patient priorities in the field of adjunctive drug therapy with apheresis. Therapy characteristics were examined as to their relevance to hypercholesterolemia patients. METHODS: To identify all potential patient-relevant treatment characteristics, a systematic literature review and ten interviews with patients were conducted. Seven key characteristics were identified from the patient perspective. Patients' priorities were elicited using an analytic hierarchy process (AHP). RESULTS: In total, N = 61 patients diagnosed with severe hypercholesterolemia and undergoing apheresis participated in the study. The analysis showed predominance for the attribute "reduction of LDL-C level in blood" (Wglobal:0.362). The "risk of myopathy" (Wglobal:0.164), "risk of neurocognitive impairment" (Wglobal:0.161) and "frequency of apheresis" (Wglobal:0.119) were ranked second, third and fourth. Subgroup analyses revealed that "frequency of apheresis" is of greater importance to younger patients, men and/or patients who indicated a reduction in quality of life due to apheresis. CONCLUSIONS: The essential decision criteria for optimal therapy from the patients' perspective were obtained. "Reduction of lipoprotein in blood" was ranked highest compared with the "mode of administration" and "side effects" characteristics. The study offers a transparent approach for the identification of patient priorities for adjunctive PCSK9-inhibitor therapy in apheresis-treated hypercholesterolemia. The project can be used by healthcare decision makers to understand the importance of each patient-relevant endpoint.

[Benefit Assessment for Examination and Treatment Methods with Medical Devices of High Hisk: Trade-off between Patient Benefit, Evidence and Access]. / Nutzenbewertung für Untersuchungs- und Behandlungsmethoden mit Medizinprodukten hoher Klassen: Die Abwägung von Patientennutzen, Evidenz und Zugang.

The lately inured § 137 h SGB V aims to assess the benefit of new examination and treatment methods with medical devices of class IIb and III. In the future, the decision on the reimbursement eligibility will be marked by high requirements in terms of documentation of benefits and harm. The objective of benefit assessment is the operationalization of benefit to patients. A comparative determination of benefit enables rational decision by regulatory authorities. The process of benefit assessment should guarantee transparent decision-making, and the underlying medical and health economic fundamentals should be documented. In principle, benefit assessment can be divided into 3 phases: measuring causal effects of an intervention, assessment of the measured effects and decision on reimbursement based on the aggregation of an overall benefit. To address the peculiarities of medical devices in this process, adaptive study designs, MCDA and adaptive benefit assessment provide a sustainable concept for rapid access by patients to innovative treatments of high quality and safety.

The Probabilistic Efficiency Frontier: A Framework for Cost-Effectiveness Analysis in Germany Put into Practice for Hepatitis C Treatment Options.

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.

Patient-Focused Benefit-Risk Analysis to Inform Regulatory Decisions: The European Union Perspective.

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.

TREATMENT AFTER ACUTE CORONARY SYNDROME: ANALYSIS OF PATIENT'S PRIORITIES WITH ANALYTIC HIERARCHY PROCESS.

BACKGROUND: Cardiovascular disease is one of the most common causes of death worldwide, with many individuals having experienced acute coronary syndrome (ACS). How patients with a history of ACS value aspects of their medical treatment have been evaluated rarely. The aim of this study was to determine patient priorities for long-term drug therapy after experiencing ACS. METHODS: To identify patient-relevant treatment characteristics, a systematic literature review and qualitative patient interviews were conducted. A questionnaire was developed to elicit patient's priorities for different characteristics of ACS treatment using Analytic Hierarchy Process (AHP). To evaluate the patient-relevant outcomes, the eigenvector method was applied. RESULTS: Six-hundred twenty-three patients participated in the computer-assisted personal interviews and were included in the final analysis. Patients showed a clear priority for the attribute "reduction of mortality risk" (weight: 0.402). The second most preferred attribute was the "prevention of a new myocardial infarction" (weight: 0.272), followed by "side effect: dyspnea" (weight: 0.165) and "side effect: bleeding" (weight: 0.117). The "frequency of intake" was the least important attribute (weight: 0.044). CONCLUSION: In conclusion, this study shows that patients strongly value a reduction of the mortality risk in post-ACS treatment. Formal consideration of patient preferences and priorities can help to inform a patient-centered approach, clinical practice, development of future effective therapies, and health policy for decision makers that best represents the needs and goals of the patient.

[Patients' preferences in the medicamentous treatment of diabetes mellitus type 2: A systematic classification and meta-comparison of patient preference studies]. / Patientenpräferenzen in der medikamentösen Therapie von Diabetes Mellitus Typ 2 : Systematische Klassifizierung und Metavergleich der Patientenpräferenzstudien.

OBJECTIVE: Diabetes Mellitus is one of the most expensive common diseases. Because of the great socio-economic importance of this indication, it seems necessary to consider the expectations and needs of all patients with regard to treatment characteristics. To evaluate patient preferences stated-preference methods are increasingly used. The aim was to analyze and compare the available evidence from patient preference studies regarding the treatment characteristics and to display which target criteria in the medicinal treatment of diabetes mellitus type 2 are most important from the patients view. METHODS: A literature review in PubMed was conducted to identify stated preference studies in the indication of diabetes and show which properties of a treatment are relevant to the patient's benefit. By means of level difference calculation and a subsequent normalization uniform weights for the attributes of the studies were determined. Based on a final ranking the key criteria according to their value proposition from the perspective of affected populations are demonstrated. RESULTS: N = 13 studies could be included in the analysis. By calculating the level difference and performing a normalization it was possible to obtain a uniform representation of all attributes of N = 7 studies. The preference studies show that the control of blood sugar, side effects (such as hypoglycemia, nausea and stomach discomfort or weight changes), long-term complications and the mode of administration represent the most common patient-relevant outcomes in diabetes therapy. In five of the 13 DCE studies the blood glucose control achieved the highest ranking within the relative importance score in the treatment of diabetes mellitus. In two studies, the side effects hypoglycemia and weight changes occupied the first rank. Furthermore, in two other studies, the side effects of nausea/upset stomach and in one study the type of application determined as the primary outcome measure. CONCLUSIONS: The use of stated preference methods in health economics aims to reflect the benefit assessments of therapy features from patient's or expert perspective. So far it is unclear how this evidence is documented in benefit assessment of medical interventions and how the findings will be made available to decision-makers. Based on the findings of this review the evidence of patient preferences in diabetes treatment is synthesized. The results show the relative importance based on a derived ranking score of the used patient-relevant endpoints. The ranking allows the comparison of the results of the identified preference studies.

Preferences for Monitoring Comprehensive Heart Failure Care: A Latent Class Analysis.

OBJECTIVE: To measure preference heterogeneity for monitoring systems among patients with a chronic heart failure. METHODS: A best-worst scaling experiment (BWS case 3) was conducted among patients with chronic heart failure to assess preferences for hypothetical monitoring care scenarios. These were characterized by the attributes mobility, risk of death, risk of hospitalization, type and frequency of monitoring, risk of medical device, and system-relevant complications. A latent class analysis (LCA) was used to analyze and interpret the data. In addition, a market simulator was used to examine which treatment configurations participants in the latent classes preferred. RESULTS: Data from 278 respondents were analyzed. The LCA identified four heterogeneous classes. For class 1, the most decisive factor was mobility with a longer distance covered being most important. Class 2 respondents directed their attention toward attribute "monitoring," with a preferred monitoring frequency of nine times per year. The attribute risk of hospitalization was most important for respondents of class 3, closely followed by risk of death. For class 4, however, risk of death was most important. A market simulation showed that, even with high frequency of monitoring, most classes preferred therapy with high improvement in mobility, mortality, and hospitalization. CONCLUSION: Using LCA, variations in preferences among different groups of patients with chronic heart failure were examined. This allows treatment alternatives to be adapted to individual needs of patients and patient groups. The findings of the study enhance clinical and allocative decision-making while elevating the quality of clinical data interpretation.

How to Present a Decision Object in Health Preference Research: Attributes and Levels, the Decision Model, and the Descriptive Framework.

In health preference research (HPR) studies, data are generated by participants'/subjects' decisions. When developing an HPR study, it is therefore important to have a clear understanding of the components of a decision and how those components stimulate participant behavior. To obtain valid and reliable results, study designers must sufficiently describe the decision model and its components. HPR studies require a detailed examination of the decision criteria, detailed documentation of the descriptive framework, and specification of hypotheses. The objects that stimulate subjects' decisions in HPR studies are defined by attributes and attribute levels. Any limitations in the identification and presentation of attributes and levels can negatively affect preference elicitation, the quality of the HPR data, and study results. This practical guide shows how to link the HPR question to an underlying decision model. It covers how to (1) construct a descriptive framework that presents relevant characteristics of a decision object and (2) specify the research hypotheses. The paper outlines steps and available methods to achieve all this, including the methods' advantages and limitations.

Patient and Public Acceptance of Digital Technologies in Health Care: Protocol for a Discrete Choice Experiment.

BACKGROUND: Strokes pose a particular challenge to the health care system. Although stroke-related mortality has declined in recent decades, the absolute number of new strokes (incidence), stroke deaths, and survivors of stroke has increased. With the increasing need of neurorehabilitation and the decreasing number of professionals, innovations are needed to ensure adequate care. Digital technologies are increasingly used to meet patients' unfilled needs during their patient journey. Patients must adhere to unfamiliar digital technologies to engage in health interventions. Therefore, the acceptance of the benefits and burdens of digital technologies in health interventions is a key factor in implementing these innovations. OBJECTIVE: This study aims to describe the development of a discrete choice experiment (DCE) to weigh criteria that impact patient and public acceptance. Secondary study objectives are a benefit-burden assessment (estimation of the maximum acceptable burden of technical features and therapy-related characteristics for the patient or individual, eg, no human contact), overall comparison (assessment of the relative importance of attributes for comparing digital technologies), and adherence (identification of key attributes that influence patient adherence). The exploratory objectives include heterogeneity assessment and subgroup analysis. The methodological aims are to investigate the use of DCE. METHODS: To obtain information on the criteria impacting acceptance, a DCE will be conducted including 7 attributes based on formative qualitative research. Patients with stroke (experimental group) and the general population (control group) are surveyed. The final instrument includes 6 best-best choice tasks in partial design. The experimental design is a fractional-factorial efficient Bayesian design (D-error). A conditional logit regression model and mixed logistic regression models will be used for analysis. To consider the heterogeneity of subgroups, a latent class analysis and an analysis of heteroscedasticity will be performed. RESULTS: The literature review, qualitative preliminary study, survey development, and pretesting were completed. Data collection and analysis will be completed in the last quarter of 2023. CONCLUSIONS: Our results will inform decision makers about patients' and publics' acceptance of digital technologies used in innovative interventions. The patient preference information will improve decisions regarding the development, adoption, and pricing of innovative interventions. The behavioral changes in the choice of digital intervention alternatives are observable and can therefore be statistically analyzed. They can be translated into preferences, which define the value. This study will investigate the influences on the acceptance of digital interventions and thus support decisions and future research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46056.

Population preferences for non-pharmaceutical interventions to control the SARS-CoV-2 pandemic: trade-offs among public health, individual rights, and economics.

PROBLEM: Policymakers must decide on interventions to control the pandemic. These decisions are driven by weighing the risks and benefits of various non-pharmaceutical intervention alternatives. Due to the nature of the pandemic, these decisions are not based on sufficient evidence regarding the effects, nor are decision-makers informed about the willingness of populations to accept the economic and health risks associated with different policy options. This empirical study seeks to reduce uncertainty by measuring population preferences for non-pharmaceutical interventions. METHODS: An online-based discrete choice experiment (DCE) was conducted to elicit population preferences. Respondents were asked to choose between three pandemic scenarios with different interventions and impacts of the Corona pandemic. In addition, Best-worst scaling (BWS) was used to analyze the impact of the duration of individual interventions on people's acceptance. The marginal rate of substitution was applied to estimate willingness-to-accept (WTA) for each intervention and effect by risk of infection. RESULTS: Data from 3006 respondents were included in the analysis. The DCE showed, economic effect of non-pharmaceutical measures had a large impact on choice decisions for or against specific lockdown scenarios. Individual income decreases had the most impact. Excess mortality and individual risk of infection were also important factors influencing choice decisions. Curfews, contact restrictions, facility closures, personal data transmissions, and mandatory masking in public had a lesser impact. However, significant standard deviations in the random parameter logit model (RPL) indicated heterogeneities in the study population. The BWS results showed that short-term restrictions were more likely to be accepted than long-term restrictions. According to WTA estimates, people would be willing to accept a greater risk of infection to avoid loss of income. DISCUSSION: The results can be used to determine which consequences of pandemic measures would be more severe for the population. For example, the results show that citizens want to limit the decline in individual income during pandemic measures. Participation in preference studies can also inform citizens about potential tradeoffs that decision-makers face in current and future decisions during a pandemic. Knowledge of the population's preferences will help inform decisions that consider people's perspectives and expectations for the future. Survey results can inform decision-makers about the extent to which the population is willing to accept certain lockdown measures, such as curfews, contact restrictions, lockdowns, or mandatory masks.

Personalized diabetes management: what do patients with diabetes mellitus prefer? A discrete choice experiment.

BACKGROUND: There are unresolved procedural and medical problems in the care of diabetes, which cause high costs for health systems. These include the inadequate glycemic adjustment, care gaps, therapeutic inertia, and a lack of motivation. Personalized diabetes management can be seen as a kind of "standard process" that provides both physicians and patients with a framework. The aim of this empirical survey is the evaluation of patient preferences regarding personalized diabetes management. The purpose of this experiment is to demonstrate the properties of the programs that are relevant for the choice of insulin-based therapy regimens for patients with type II diabetes mellitus. METHODS: A discrete choice experiment (DCE) was applied to identify preferences for a personalized diabetes management in patients with type II diabetes mellitus. Six attributes were included. The DCE was conducted in June 2017 using a fractional factorial design, and the statistical data analysis used random effect logit models. RESULTS: N = 227 patients (66.1% male) were included. The preference analysis showed dominance for the attribute "occurrence of severe hypoglycemias per year" [level difference (LD) 2765]. Preference analysis also showed that participants weight the "risk of myocardial infarction (over 10 years)" (LD 1.854) highest among the side effects. Within the effectiveness criterion of "change in the long-term blood glucose level (HbA1c)" a change at an initial value of 9.5% (LD 1.146) is weighted slightly higher than changes at 7.5% (LD 1.141). Within the random parameter logit estimation, all coefficients proved to be significantly different from zero at the level p ≤ 0.01. The latent class analysis shows three heterogeneous classes, each showing clearly different weights of the therapeutic properties. This results in a clear three-folding: for 1/3 of the respondents the change of the long-term blood sugar (HbA1c value) is the top objective. Another third is solely interested in the short-term effectiveness of the therapy in the sense of the occurrence of severe hypoglycemias per year. The last third of the interviewees finally focuses on the follow-up regarding cardiovascular events. Overall, there were five structural and personality traits which have an influence on the respective probability of the class membership. DISCUSSION/CONCLUSION: This study identifies and weights the key decision-making criteria for optimal management of diabetes from the perspective of patients. It was shown that the effectiveness of a care program is the most important from the perspective of the patient and avoiding severe a hypoglycemia has the greatest influence on the choice. The risk of myocardial infarction as a follow-up disease and the long-term adjustment of the blood glucose follow the importance. In the analysis of possible subgroup differences by means of latent class analysis, it was found that three preference patterns exist within the sample. The generated preference data can be used for the design of personalized management approaches. It remains open to the extent to which expert opinions and patient preferences diverge.

Patient preferences in the treatment of hemophilia A: A latent class analysis.

OBJECTIVE: To examine subgroup-specific treatment preferences and characteristics of patients with hemophilia A. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Public preferences for the allocation of donor organs for transplantation: A discrete choice experiment.

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.

What factors influence HIV testing? Modeling preference heterogeneity using latent classes and class-independent random effects.

Efforts to eliminate the HIV epidemic will require increased HIV testing rates among high-risk populations. To inform the design of HIV testing interventions, a discrete choice experiment (DCE) with six policy-relevant attributes of HIV testing options elicited the testing preferences of 300 female barworkers and 440 male Kilimanjaro mountain porters in northern Tanzania. Surveys were administered between September 2017 and July 2018. Participants were asked to complete 12 choice tasks, each involving first- and second-best choices from 3 testing options. DCE responses were analyzed using a random effects latent class logit (RELCL) model, in which the latent classes summarize common participant preference profiles, and the random effects capture additional individual-level preference heterogeneity with respect to three attribute domains: (a) privacy and confidentiality (testing venue, pre-test counseling, partner notification); (b) invasiveness and perceived accuracy (method for obtaining the sample for the HIV test); and (c) accessibility and value (testing availability, additional services provided). The Bayesian Information Criterion indicated the best model fit for a model with 8 preference classes, with class sizes ranging from 6% to 19% of participants. Substantial preference heterogeneity was observed, both between and within latent classes, with 12 of 16 attribute levels having positive and negative coefficients across classes, and all three random effects contributing significantly to participants' choices. The findings may help identify combinations of testing options that match the distribution of HIV testing preferences among high-risk populations; the methods may be used to systematically design heterogeneity-focused interventions using stated preference methods.