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.
Objective Lung cancer is a major cause of cancer-related deaths and thus represents a global health problem. According to World Health Organization (WHO) estimates, approximately 1.37 million people ...die each year from lung cancer. Different therapeutic approaches as well as several treatment options exist. To date decisions on which therapies to use have largely been made by clinical experts. Comparative preference studies show that underlying weighting of treatment goals by experts is not necessarily congruent with the preferences of affected patients. Aim and methods The aim of this empirical study was to ascertain patient preferences in relation to treatment of nonsmall-cell lung cancer (NSCLC). After identification of patient-relevant treatment attributes via literature review and qualitative interviews(ten) a discrete-choice experiment including seven patient-relevant attributes was conducted using a fractional factorial NGene-design. Statistical data analysis was performed using latent class models. Results The qualitative part of this study identified outcome measures related to efficacy, side effects and mode of administration. A total of 211 NSCLC patients (N = 211) participated in the computer-assisted personal interview. A clear preference for an increase in "progression-free survival" (coef.: 1.087) and a reduction of "tumor-associated symptoms"(cough, shortness of breath and pain); coef.: 1.090) was demonstrated, followed by the reduction of side effects: "nausea and vomiting" (coef.: 0.605); "rash" (coef.: 0.432); "diarrhea" (coef.: 0.427); and, "tiredness and fatigue" (coef.: 0.423). The "mode of administration" was less important for participants (coef.: 0.141). Conclusion Preference measurement showed "progression-free survival" and "tumor-associated symptoms" had a significant influence on the treatment decision. Subgroup analysis revealed that the importance of "progression-free survival" increases with increased therapy experience. Based on the presented results therapies can be designed, assessed and chosen on the basis of patient-oriented findings. As such, more effective and efficient care of patients can be achieved and benefits increased.
Healthcare decision making is usually characterized by a low degree of transparency. The demand for transparent decision processes can be fulfilled only when assessment, appraisal and decisions about ...health technologies are performed under a systematic construct of benefit assessment. The benefit of an intervention is often multidimensional and, thus, must be represented by several decision criteria. Complex decision problems require an assessment and appraisal of various criteria; therefore, a decision process that systematically identifies the best available alternative and enables an optimal and transparent decision is needed. For that reason, decision criteria must be weighted and goal achievement must be scored for all alternatives. Methods of multi-criteria decision analysis (MCDA) are available to analyse and appraise multiple clinical endpoints and structure complex decision problems in healthcare decision making. By means of MCDA, value judgments, priorities and preferences of patients, insurees and experts can be integrated systematically and transparently into the decision-making process. This article describes the MCDA framework and identifies potential areas where MCDA can be of use (e.g. approval, guidelines and reimbursement/pricing of health technologies). A literature search was performed to identify current research in healthcare. The results showed that healthcare decision making is addressing the problem of multiple decision criteria and is focusing on the future development and use of techniques to weight and score different decision criteria. This article emphasizes the use and future benefit of MCDA.
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.
Clinicians and public health experts make evidence-based decisions for individual patients, patient groups and even whole populations. In addition to the principles of internal and external validity ...(evidence), patient preferences must also influence decision making. Great Britain, Australia and Germany are currently discussing methods and procedures for valuing patient preferences in regulatory (authorization and pricing) and in health policy decision making. However, many questions remain on how to best balance patient and public preferences with physicians’ judgement in healthcare and health policy decision making. For example, how to define evaluation criteria regarding the perceived value from a patient’s perspective? How do physicians’ fact-based opinions also reflect patients’ preferences based on personal values? Can empirically grounded theories explain differences between patients and experts—and, if so, how? This article aims to identify and compare studies that used different preference elicitation methods and to highlight differences between patient and physician preferences. Therefore, studies comparing patient preferences and physician judgements were analysed in a review. This review shows a limited amount of literature analysing and comparing patient and physician preferences for healthcare interventions and outcomes. Moreover, it shows that methodology used to compare preferences is diverse. A total of 46 studies used the following methods—discrete-choice experiments, conjoint analyses, standard gamble, time trade-offs and paired comparisons—to compare patient preferences with doctor judgements. All studies were published between 1985 and 2011. Most studies reveal a disparity between the preferences of actual patients and those of physicians. For most conditions, physicians underestimated the impact of intervention characteristics on patients’ decision making. Differentiated perceptions may reflect ineffective communication between the provider and the patient. This in turn may keep physicians from fully appreciating the impact of certain medical conditions on patient preferences. Because differences exist between physicians’ judgement and patient preferences, it is important to incorporate the needs and wants of the patient into treatment decisions.
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.
Stated-preference methods increasingly are used to quantify preferences in health economics, health technology assessment, benefit-risk analysis and health services research. The objective of ...stated-preference studies is to acquire information about trade-off preferences among treatment outcomes, prioritization of clinical decision criteria, likely uptake or adherence to healthcare products and acceptability of healthcare services or policies. A widely accepted approach to eliciting preferences is discrete-choice experiments. Patient, physician, insurant or general-public respondents choose among constructed, experimentally controlled alternatives described by decision-relevant features or attributes. Attributes can represent complete health states, sets of treatment outcomes or characteristics of a healthcare system. The observed pattern of choice reveals how different respondents or groups of respondents implicitly weigh, value and assess different characteristics of treatments, products or services. An important advantage of choice experiments is their foundation in microeconomic utility theory. This conceptual framework provides tests of internal validity, guidance for statistical analysis of latent preference structures, and testable behavioural hypotheses. Choice experiments require expertise in survey-research methods, random-utility theory, experimental design and advanced statistical analysis. This paper should be understood as an introduction to setting up a basic experiment rather than an exhaustive critique of the latest findings and procedures. Where appropriate, we have identified topics of active research where a broad consensus has not yet been established.
Aims The aim of this empirical study is to evaluate patient preferences for different characteristics of oral type 2 diabetes mellitus (T2DM) treatment. As T2DM treatment requires strict adherence, ...patient needs and preferences should be taken into consideration. Methods Based on a qualitative and quantitative analysis, a discrete choice experiment (DCE) was applied to identify patient preferences. Apart from six identical attributes (adjustment of glycated hemoglobin HbA1c, prevention of hypoglycemia, risk of genital infection, risk of gastrointestinal problems, risk of urinary tract infection, and weight change), one continuous variable of either "additional healthy life years" (AHY) or "additional costs" attribute (AC) was included. The DCE was conducted using a fractional factorial design, and the statistical data analysis used random effect logit models. Results In total, N = 626 (N = 318 AHY + N = 308 AC) T2DM patients participated in the survey. The estimation revealed a clear dominance for prevention of hypoglycemia (coefficient 0.937) and adjustment of HbA1c (coefficient 0.541). The attributes, "additional healthy life years" (coefficient 0.458) or "additional costs" (coefficient 0.420), were in the middle rank and both of significant impact. The side effects, risk of genital infection (coefficient 0.301), risk of gastrointestinal problems (coefficient 0.296), and risk of urinary tract infection (coefficient 0.241) followed in this respective order. Possible weight change (coefficient 0.047) was of less importance (last rank) to the patients in this evaluation. Conclusions These survey results demonstrate how much a (hypothetical) T2DM oral treatment characteristic affects the treatment decision. The preference data can be used for riskbenefit assessment, cost-benefit assessment, and the establishment of patient-oriented evidence. Understanding how patients perceive and value different aspects of diabetes oral treatment is vital to the optimal design and evaluation of treatment options. The present results can be an additional source of information for design, assessment, and decision in T2DM treatment regimes. As such, more effective and efficient care of patients can be achieved, thereby increasing adherence.
Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of ...methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences.
An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends.
Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model.
Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance.
•There is an increasing interest in accounting for preference heterogeneity in discrete choice experiments, matched with a growing portfolio of analytical methods.•Accounting for heterogeneity allows researchers to go beyond understanding the “average” preference and reduces bias in the estimated parameters.•Most current studies estimate mixed logit models with either continuous (eg, normal, lognormal) or discrete (ie, latent classes) parameter distributions. Some studies attempt to separate heterogeneity in scale and preferences by using more complex models, despite both forms of heterogeneity being statistically confounded.•Health preference researchers are using increasingly complex methods to analyze preference data; nevertheless, our survey suggests there is disagreement among experts and applied researchers on the role, capabilities, and suitability of alternative approaches, indicating a need for discourse, alignment, and guidance.
Objective
To estimate the relative importance of organizational‐, procedural‐, and interpersonal‐level features of health care delivery systems from the patient perspective.
Data Sources/Study ...Setting
We designed four discrete choice experiments (DCEs) to measure patient preferences for 21 health system attributes. Participants were recruited through the online patient portal of a large health system. We analyzed the DCE data using random effects logit models.
Data Collection/Extraction Methods
DCEs were performed in which respondents were provided with descriptions of alternative scenarios and asked to indicate which scenario they prefer. Respondents were randomly assigned to one of the three possible health scenarios (current health, new lung cancer diagnosis, or diabetes) and asked to complete 15 choice tasks. Each choice task included an annual out‐of‐pocket cost attribute.
Principal Findings
A total of 3,900 respondents completed the survey. The out‐of‐pocket cost attribute was considered the most important across the four different DCEs. Following the cost attribute, trust and respect, multidisciplinary care, and shared decision making were judged as most important. The relative importance of out‐of‐pocket cost was consistently lower in the hypothetical context of a new lung cancer diagnosis compared with diabetes or the patient's current health.
Conclusions
This study demonstrates the complexity of patient decision making processes regarding features of health care delivery systems. Our findings suggest the importance of these features may change as a function of an individual's medical conditions.
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