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Journal articles on the topic 'Elicitation of expert belief'

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Published: 10 March 2023

Last updated: 11 March 2023

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1

RENOOIJ, SILJA. "Probability elicitation for belief networks: issues to consider." Knowledge Engineering Review 16, no. 3 (September 2001): 255–69. http://dx.doi.org/10.1017/s0269888901000145.

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Upon assessing probabilities for Bayesian belief networks, the knowledge and practical experience of experts is often the only available source of probabilistic information. It is important to realise that issues concerning human capabilities with respect to making judgements come into play when relying on experts for probability elicitation. A number of methods for the elicitation of probabilities are known from the field of decision analysis. These methods try, to some extent, to deal with those issues. I present here an overview of the issues to consider when relying on expert judgements and describe the methods that are available for expert elicitation, along with their benefits and drawbacks.

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Phillipson, Frank, Peter Langenkamp, and Reinder Wolthuis. "Alternative Initial Probability Tables for Elicitation of Bayesian Belief Networks." Mathematical and Computational Applications 26, no. 3 (July 28, 2021): 54. http://dx.doi.org/10.3390/mca26030054.

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Bayesian Belief Networks are used in many fields of application. Defining the conditional dependencies via conditional probability tables requires the elicitation of expert belief to fill these tables, which grow very large quickly. In this work, we propose two methods to prepare these tables based on a low number of input parameters using specific structures and one method to generate the table using probability tables of each relation of a child node with a certain parent. These tables can be used further as a starting point for elicitation.

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COUPÉ, VEERLE M. H., LINDA C. VAN DER GAAG, and J. DIK F. HABBEMA. "Sensitivity analysis: an aid for belief-network quantification." Knowledge Engineering Review 15, no. 3 (September 2000): 215–32. http://dx.doi.org/10.1017/s0269888900003027.

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When building a Bayesian belief network, usually a large number of probabilities have to be assessed by experts in the domain of application. Experience shows that experts are often reluctant to assess all probabilities required, feeling that they are unable to give assessments with a high level of accuracy. We argue that the elicitation of probabilities from experts can be supported to a large extent by iteratively performing sensitivity analyses of the belief network in the making, starting with rough, initial assessments. Since it gives insight into which probabilities require a high level of accuracy and which do not, performing a sensitivity analysis allows for focusing further elicitation efforts. We propose an elicitation procedure in which, alternately, sensitivity analyses are performed and probability assessments refined, until satisfactory behaviour of the belief network is obtained, until the costs of further elicitation outweigh the benefits of higher accuracy or until higher accuracy can no longer be attained due to lack of knowledge.

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Bojke, Laura, Marta Soares, Karl Claxton, Abigail Colson, Aimée Fox, Christopher Jackson, Dina Jankovic, Alec Morton, Linda Sharples, and Andrea Taylor. "Developing a reference protocol for structured expert elicitation in health-care decision-making: a mixed-methods study." Health Technology Assessment 25, no. 37 (June 2021): 1–124. http://dx.doi.org/10.3310/hta25370.

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Background Many decisions in health care aim to maximise health, requiring judgements about interventions that may have higher health effects but potentially incur additional costs (cost-effectiveness framework). The evidence used to establish cost-effectiveness is typically uncertain and it is important that this uncertainty is characterised. In situations in which evidence is uncertain, the experience of experts is essential. The process by which the beliefs of experts can be formally collected in a quantitative manner is structured expert elicitation. There is heterogeneity in the existing methodology used in health-care decision-making. A number of guidelines are available for structured expert elicitation; however, it is not clear if any of these are appropriate for health-care decision-making. Objectives The overall aim was to establish a protocol for structured expert elicitation to inform health-care decision-making. The objectives are to (1) provide clarity on methods for collecting and using experts’ judgements, (2) consider when alternative methodology may be required in particular contexts, (3) establish preferred approaches for elicitation on a range of parameters, (4) determine which elicitation methods allow experts to express uncertainty and (5) determine the usefulness of the reference protocol developed. Methods A mixed-methods approach was used: systemic review, targeted searches, experimental work and narrative synthesis. A review of the existing guidelines for structured expert elicitation was conducted. This identified the approaches used in existing guidelines (the ‘choices’) and determined if dominant approaches exist. Targeted review searches were conducted for selection of experts, level of elicitation, fitting and aggregation, assessing accuracy of judgements and heuristics and biases. To sift through the available choices, a set of principles that underpin the use of structured expert elicitation in health-care decision-making was defined using evidence generated from the targeted searches, quantities to elicit experimental evidence and consideration of constraints in health-care decision-making. These principles, including fitness for purpose and reflecting individual expert uncertainty, were applied to the set of choices to establish a reference protocol. An applied evaluation of the developed reference protocol was also undertaken. Results For many elements of structured expert elicitation, there was a lack of consistency across the existing guidelines. In almost all choices, there was a lack of empirical evidence supporting recommendations, and in some circumstances the principles are unable to provide sufficient justification for discounting particular choices. It is possible to define reference methods for health technology assessment. These include a focus on gathering experts with substantive skills, eliciting observable quantities and individual elicitation of beliefs. Additional considerations are required for decision-makers outside health technology assessment, for example at a local level, or for early technologies. Access to experts may be limited and in some circumstances group discussion may be needed to generate a distribution. Limitations The major limitation of the work conducted here lies not in the methods employed in the current work but in the evidence available from the wider literature relating to how appropriate particular methodological choices are. Conclusions The reference protocol is flexible in many choices. This may be a useful characteristic, as it is possible to apply this reference protocol across different settings. Further applied studies, which use the choices specified in this reference protocol, are required. Funding This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 37. See the NIHR Journals Library website for further project information. This work was also funded by the Medical Research Council (reference MR/N028511/1).

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Kreinovich, Vladik. "INTERVAL METHODS IN KNOWLEDGE REPRESENTATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 04, no. 05 (October 1996): 467–90. http://dx.doi.org/10.1142/s0218488596000433.

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In this issues, we continue to publish abstracts and reviews of recents papers on interval methods in knowledge representation. In knowledge representation, intervals are used for two main purposes: • to describe durations of events; and • to describe uncertainty of measurement results and expert estimates of different quantities; often, we do not know the exact value of a quantity, but we know its lower and upper bounds (e.g., we may not know the exact value of someone's weight, but we may know that this weight is in between 140 and 160 pounds). An important case of this uncertainty occurs in knowledge elicitation, when we ask experts to numerically estimate their degrees of belief in their own statements; in this case, it is often difficult for an expert to estimate this degree of belief precisely, but an expert can often provide us with an interval of possible values. The reviews are collected by Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA, email vladik@cs.utep.edu

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Anuar, Nadia, Ahmad Mazli Muhammad, and Zainudin Awang. "An Exploratory Factor Analysis of Elicited Students’ Salient Beliefs Toward Critical Reading." International Journal of Modern Languages And Applied Linguistics 4, no. 4 (December 18, 2020): 101. http://dx.doi.org/10.24191/ijmal.v4i4.11288.

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Critical reading was named a key aspiration in the Malaysian Education Blueprint (2015-2025) and is an essential skill students must acquire. However, an increased number of students was reported to demonstrate poor critical reading performance at the workplace. Thus, Theory of Planned Behaviour (TPB) was utilised to examine students’ beliefs critical reading which encompass behavioural belief (advantages and disadvantages of critical reading), normative belief (identification of people who approve participating in critical reading), and control belief (difficulties in critical reading). A three-stage research design was employed. The first stage, elicitation study, was conducted as this stage has received minimum scholarly attention in the TPB literature and to ensure a more comprehensive analysis. The beliefs were analysed qualitatively, which was proceeded by expert panel review. Subsequently, exploratory factor analysis was conducted to determine the validity of the salient beliefs. Findings from the exploratory factor analysis and reliability analysis revealed that beliefs that were elicited from students in this first stage are appropriate and possess sufficient reliability and construct validity. Hence, the results of this study not only contributed to the critical reading and Theory of Planned Behaviour’s literature but have also identified more relevant factors that influence students’ perception toward critical reading.

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Carvalho, Arthur. "A Note on Sandroni-Shmaya Belief Elicitation Mechanism." Journal of Prediction Markets 10, no. 2 (January 27, 2017): 14–21. http://dx.doi.org/10.5750/jpm.v10i2.1225.

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Incentive-compatible methods for eliciting beliefs, such as proper scoring rules, often rely on strong assumptions about how humans behave when making decisions under risk and uncertainty. For example, standard proper scoring rules assume that humans are risk neutral, an assumption that is often violated in practice. Under such an assumption, proper scoring rules induce honest reporting of beliefs, in a sense that experts maximize their expected scores from a proper scoring rule by honestly reporting their beliefs.Sandroni and Shmaya [Economic Theory Bulletin, volume 1, issue 1, 2013] suggested a remarkable mechanism based on proper scoring rules that induces honest reporting of beliefs without any assumptions on experts’ risk attitudes. In particular, the authors claimed that the mechanism relies only on the natural assumptions of probabilistic sophistication and dominance. We suggest in this paper that the reduction of compound lotteries axiom is another assumption required for Sandroni and Shmaya’s mechanism to induce honest reporting of beliefs. We further elaborate on the implications of such an extra assumption in light of recent findings regarding the reduction of compound lotteries axiom.

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Bryant, Andrew, Michael Grayling, Shaun Hiu, Ketankumar Gajjar, Eugenie Johnson, Ahmed Elattar, Luke Vale, Dawn Craig, and Raj Naik. "Residual disease after primary surgery for advanced epithelial ovarian cancer: expert elicitation exercise to explore opinions about potential impact of publication bias in a planned systematic review and meta-analysis." BMJ Open 12, no. 8 (August 2022): e060183. http://dx.doi.org/10.1136/bmjopen-2021-060183.

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ObjectivesWe consider expert opinion and its incorporation into a planned meta-analysis as a way of adjusting for anticipated publication bias. We conduct an elicitation exercise among eligible British Gynaecological Cancer Society (BGCS) members with expertise in gynaecology.DesignExpert elicitation exercise.SettingBGCS.ParticipantsMembers of the BGCS with expertise in gynaecology.MethodsExperts were presented with details of a planned prospective systematic review and meta-analysis, assessing overall survival for the extent of excision of residual disease (RD) after primary surgery for advanced epithelial ovarian cancer. Participants were asked views on the likelihood of different studies (varied in the size of the study population and the RD thresholds being compared) not being published. Descriptive statistics were produced and opinions on total number of missing studies by sample size and magnitude of effect size estimated.ResultsEighteen expert respondents were included. Responders perceived publication bias to be a possibility for comparisons of RD <1 cm versus RD=0 cm, but more so for comparisons involving higher volume suboptimal RD thresholds. However, experts’ perceived publication bias in comparisons of RD=0 cm versus suboptimal RD thresholds did not translate into many elicited missing studies in Part B of the elicitation exercise. The median number of missing studies estimated by responders for the main comparison of RD<1 cm versus RD=0 cm was 10 (IQR: 5–20), with the number of missing studies influenced by whether the effect size was equivocal. The median number of missing studies estimated for suboptimal RD versus RD=0 cm was lower.ConclusionsThe results may raise awareness that a degree of scepticism is needed when reviewing studies comparing RD <1 cm versus RD=0 cm. There is also a belief among respondents that comparisons involving RD=0 cm and suboptimal thresholds (>1 cm) are likely to be impacted by publication bias, but this is unlikely to attenuate effect estimates in meta-analyses.

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Vijayan, Vimal, Sanjay K. Chaturvedi, and Ritesh Chandra. "A failure interaction model for multicomponent repairable systems." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 3 (February 3, 2020): 470–86. http://dx.doi.org/10.1177/1748006x19897828.

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Modeling of stochastic dependency among components in a repairable system is still a challenging task when dealing with the maintenance of multicomponent systems. With the help of stochastic dependency information, failure of a component brings attention to the components having strong interactions with the failed component. With this information, one can plan the maintenance of components in a better way. Since a change in failure probability of a component (due to deterioration or failure of a component in a given time interval) influences the failure probabilities of other components in the system, therefore, in this article, we consider probability of failure to represent the state of the component to model the stochastic dependency among components. We apply the Bayesian belief network to model such scenario of dependency among the components and present two case studies to compute various probabilities. In the first study, expert elicitation is being used, whereas the time between failure of the components is used in the second case to calculate failure probabilities. To illustrate the applicability of the proposed approach, one case study for each is presented. The first case study takes the case of an army truck through expert elicitation approach whereas the second case study deals with a rolling mill gearbox whose time between failure of components was available.

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Schlag, Karl H., and Joël J. van der Weele. "A method to elicit beliefs as most likely intervals." Judgment and Decision Making 10, no. 5 (September 2015): 456–68. http://dx.doi.org/10.1017/s1930297500005593.

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AbstractWe show how to elicit the beliefs of an expert in the form of a “most likely interval”, a set of future outcomes that are deemed more likely than any other outcome. Our method, called the Most Likely Interval elicitation rule (MLI), asks the expert for an interval and pays according to how well the answer compares to the actual outcome. We show that the MLI performs well in economic experiments, and satisfies a number of desirable theoretical properties such as robustness to the risk preferences of the expert.

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Mearns, Linda O., Melissa S. Bukovsky, and Vanessa J. Schweizer. "Potential Value of Expert Elicitation for Determining Differential Credibility of Regional Climate Change Simulations: An Exercise with the NARCCAP co-PIs for the Southwest Monsoon Region of North America." Bulletin of the American Meteorological Society 98, no. 1 (January 1, 2017): 29–35. http://dx.doi.org/10.1175/bams-d-15-00019.1.

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Abstract In this brief article, we report the initial results of an expert elicitation with the co-PIs (regional climate modelers) of the North American Regional Climate Change Assessment Program regarding their evaluation of the relative quality of regional climate model simulations focusing on the subregion dominated by the North American monsoon (NAM). We assumed that an expert elicitation framework might reveal interesting beliefs and understanding that would be different from what would be obtained from calculating quantitative metrics associated with model quality. The simulations considered were of six regional climate models (RCMs) that used NCEP Reanalysis 2 as boundary conditions for the years 1980–2004. The domain covers most of North America and adjacent oceans. The seven participating regional modelers were asked to complete surveys on their background beliefs about model credibility and their judgments regarding the quality of the six models based on a series of plots of variables related to the NAM (e.g., temperature, winds, humidity, moisture flux, precipitation). The specific RCMs were not identified. We also compared the results of the expert elicitation with those obtained from using a series of metrics developed to evaluate a European collection of climate model simulations. The results proved to be quite different in the two cases. The results of this exercise proved very enlightening regarding regional modelers’ perceptions of model quality and their beliefs about how this information should or should not be used. Based on these pilot study results, we believe a more complete study is warranted.

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Zainuddin, Ahmad Nur Fikry, Nurul Ain Syuhadah Mohammad Khorri, Nurul Sa’aadah Sulaiman, and Fares Ahmed Alaw. "Elicitation of Conditional Probability Table (CPT) For Risk Analysis of Biomass Boiler in Energy Plant." Pertanika Journal of Science and Technology 30, no. 2 (March 28, 2022): 1327–42. http://dx.doi.org/10.47836/pjst.30.2.26.

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The utilization of Empty fruit bunch (EFB) in energy production has increased in Malaysia over the last two decades. The EFB can be used as a solid fuel in a boiler system for heat and power generation. However, numerous safety and technical issues lead to a lower energy production rate. A holistic probabilistic risk analysis is developed using the Bayesian Belief Network (BBN) to reduce the risk in the boiler system. The Conditional Probability Table (CPT) indicates the influence strength between the parent node and child node in BBN. Due to scarcely available information on EFB boiler, elicitation from the expert’s opinion is vital. The formulation for boiler failures likelihood prediction that relies on experts’ perceptions was developed using the Weighted Sum Algorithm (WSA). A case study from BioPower Plant in Pahang was applied in this project. The model illustrates the relationship between the cause and the effect of the biomass boiler efficiency in a systematic way. Two types of analyses, prediction and diagnostic analysis, were performed. The results facilitated the decision-maker to predict and identify the influential underlying factors of the boiler efficiency, respectively. The result shows that the most important boiler failure factor is combustion stability. It agrees with experts’ experience that most biomass boiler failure was caused by EFB, which contains high moisture content that affects flame stability. The proposed formulation for expert opinions and perceptions conversion can be utilized for risk analysis to benefit the boiler and other infrastructure that relies on experts’ experience.

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Wang, Meng, Eric E. Smith, Nils Daniel Forkert, Thierry Chekouo, Zahinoor Ismail, Aravind Ganesh, and Tolulope Sajobi. "Integrating expert knowledge for dementia risk prediction in individuals with mild cognitive impairment (MCI): a study protocol." BMJ Open 11, no. 11 (November 2021): e051185. http://dx.doi.org/10.1136/bmjopen-2021-051185.

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IntroductionTo date, there is no broadly accepted dementia risk score for use in individuals with mild cognitive impairment (MCI), partly because there are few large datasets available for model development. When evidence is limited, the knowledge and experience of experts becomes more crucial for risk stratification and providing MCI patients with prognosis. Structured expert elicitation (SEE) includes formal methods to quantify experts’ beliefs and help experts to express their beliefs in a quantitative form, reducing biases in the process. This study proposes to (1) assess experts’ beliefs about important predictors for 3-year dementia risk in persons with MCI through SEE methodology and (2) to integrate expert knowledge and patient data to derive dementia risk scores in persons with MCI using a Bayesian approach.Methods and analysisThis study will use a combination of SEE methodology, prospectively collected clinical data, and statistical modelling to derive a dementia risk score in persons with MCI . Clinical expert knowledge will be quantified using SEE methodology that involves the selection and training of the experts, administration of questionnaire for eliciting expert knowledge, discussion meetings and results aggregation. Patient data from the Prospective Registry for Persons with Memory Symptoms of the Cognitive Neurosciences Clinic at the University of Calgary; the Alzheimer’s Disease Neuroimaging Initiative; and the National Alzheimer’s Coordinating Center’s Uniform Data Set will be used for model training and validation. Bayesian Cox models will be used to incorporate patient data and elicited data to predict 3-year dementia risk.DiscussionThis study will develop a robust dementia risk score that incorporates clinician expert knowledge with patient data for accurate risk stratification, prognosis and management of dementia.

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Pibouleau, Leslie, and Sylvie Chevret. "AN INTERNET-BASED METHOD TO ELICIT EXPERTS’ BELIEFS FOR BAYESIAN PRIORS: A CASE STUDY IN INTRACRANIAL STENT EVALUATION." International Journal of Technology Assessment in Health Care 30, no. 4 (October 2014): 446–53. http://dx.doi.org/10.1017/s0266462314000403.

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Rationale: Bayesian methods provide an interesting approach to assessing an implantable medical device (IMD) that has evolved through successive versions because they allow for explicit incorporation of prior knowledge into the analysis. However, the literature is sparse on the feasibility and reliability of elicitation in cases where expert beliefs are used to form priors.Objectives: To develop an Internet-based method for eliciting experts’ beliefs about the success rate of an intracranial stenting procedure and to assess their impact on the estimated benefit of the latest version.Study Design and Setting: The elicitation questionnaire was administered to a group of nineteen experts. Elicited experts’ beliefs were used to inform the prior distributions of a Bayesian hierarchical meta-analysis model, allowing for the estimation of the success rate of each version. RESULTS: Experts believed that the success rate of the latest version was slightly higher than that of the previous one (median: 80.8 percent versus 75.9 percent). When using noninformative priors in the model, the latest version was found to have a lower success rate (median: 83.1 percent versus 86.0 percent), while no difference between the two versions was detected with informative priors (median: 85.3 percent versus 85.6 percent).Conclusions: We proposed a practical method to elicit experts’ beliefs on the success rates of successive IMD versions and to explicitly combine all available evidence in the evaluation of the latest one. Our results suggest that the experts were overoptimistic about this last version. Nevertheless, the proposed method should be simplified and assessed in larger, representative samples.

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Neyman, Eric, and Tim Roughgarden. "Strictly Proper Contract Functions Can Be Arbitrage-Free." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 5150–55. http://dx.doi.org/10.1609/aaai.v36i5.20449.

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We consider mechanisms for truthfully eliciting probabilistic predictions from a group of experts. The standard approach --- using a proper scoring rule to separately reward each expert --- is not robust to collusion: experts may collude to misreport their beliefs in a way that guarantees them a larger total reward no matter the eventual outcome. It is a long-standing open question whether there is a truthful elicitation mechanism that makes any such collusion (also called "arbitrage") impossible. We resolve this question positively, exhibiting a class of strictly proper arbitrage-free contract functions. These contract functions have two parts: one ensures that the total reward of a coalition of experts depends only on the average of their reports; the other ensures that changing this average report hurts the experts under at least one outcome.

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Carey, Niamh, Conor Hickey, Laura Mc Cullagh, and Michael Barry. "OP206 Expert Elicitation Of Probabilistic Distributions to Inform Survival Modelling of CD19 Chimeric Antigen Receptor T-Cell Therapies." International Journal of Technology Assessment in Health Care 36, S1 (December 2020): 3. http://dx.doi.org/10.1017/s0266462320000951.

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IntroductionIn 2018, the National Centre for Pharmacoeconomics (NCPE) was commissioned to conduct a health technology assessment (HTA) of one of the first commercially available chimeric antigen receptor (CAR) T-cell therapies, tisagenlecleucel. CAR T-cells are a major advance in personalized cancer treatment, demonstrating promising outcomes in relapsed/refractory pediatric acute lymphoblastic leukemia (pALL). However, the results are based on short-term follow up, limiting their value in predicting long-term survival and leading to uncertainty about the most appropriate survival modeling method to employ. This study aimed to address these limitations by means of expert elicitation.MethodsAn expert elicitation method, the histogram technique, was employed. A predefined discrete numerical scale was presented in Microsoft Excel® and the expert was asked to place twenty crosses on a frequency chart. These crosses represented the expert's beliefs about the distribution of particular quantities. Each cross represented five percent of the probabilistic distribution. Individual distributions were then aggregated across experts using linear pooling.ResultsA total of seventeen experts were invited to take part; eight agreed to participate and five completed the exercise. Three experts did not consider tisagenlecleucel to be a “curative” therapy because patients had a higher risk of death, compared with the age- and sex-matched general population. The aggregated distributions indicated the five-year overall survival rate to be thirty-three percent (95% CI 8.65–56.88) in patients who do not receive a subsequent stem cell transplant and twenty percent (95% CI 2.38 -52.04) in those who do.ConclusionsThe results of this study will be used to calibrate CD19 CAR T-cell therapy survival estimates presented in HTA submissions to the NCPE to ensure more robust assessments. They will also be used to inform the construction of a de novo cost-utility model for examining the cost effectiveness of CD19 CAR T-cell therapies for relapsed/refractory pALL in the Irish healthcare setting.

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Barrera-Causil, Carlos, Juan Correa, Andrew Zamecnik, Francisco Torres-Avilés, and Fernando Marmolejo-Ramos. "An FDA-Based Approach for Clustering Elicited Expert Knowledge." Stats 4, no. 1 (March 4, 2021): 184–204. http://dx.doi.org/10.3390/stats4010014.

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Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian inference is then to find ways to reconcile discrepant elicited prior distributions. This paper proposes the parallel analysis of clusters of prior distributions through a hierarchical method for clustering distributions and that can be readily extended to functional data. The proposed method consists of (i) transforming the infinite-dimensional problem into a finite-dimensional one, (ii) using the Hellinger distance to compute the distances between curves and thus (iii) obtaining a hierarchical clustering structure. In a simulation study the proposed method was compared to k-means and agglomerative nesting algorithms and the results showed that the proposed method outperformed those algorithms. Finally, the proposed method is illustrated through an EKE experiment and other functional data sets.

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JOHNSON, SINDHU R., JOHN T. GRANTON, GEORGE A. TOMLINSON, HADDAS A. GROSBEIN, GILLIAN A. HAWKER, and BRIAN M. FELDMAN. "Effect of Warfarin on Survival in Scleroderma-associated Pulmonary Arterial Hypertension (SSc-PAH) and Idiopathic PAH. Belief Elicitation for Bayesian Priors." Journal of Rheumatology 38, no. 3 (December 15, 2010): 462–69. http://dx.doi.org/10.3899/jrheum.100632.

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Objective.Warfarin use in scleroderma (SSc)-associated pulmonary arterial hypertension (PAH) and idiopathic PAH (IPAH) is controversial. A prerequisite for a trial is the demonstration of community uncertainty. We evaluated experts’ beliefs about the effect of warfarin on 3-year survival in SSc-PAH and IPAH, and factors that influence warfarin use.Methods.PAH experts attending the 2008 American College of Rheumatology or American Thoracic Society meetings expressed the probability of 3-year survival without and with warfarin and their degree of uncertainty by applying adhesive dots, each representing a 5% weight of probability, in “bins” on a line, creating a prior probability distribution or prior. Using a numeric rating scale, participants rated factors that influence their use of warfarin.Results.Forty-five experts (44% pulmonologists, 38% rheumatologists, 16% cardiologists, 2% internists) underwent the belief elicitation interview. In SSc-PAH, the mean probabilities of 3-year survival without and with warfarin were 54% and 56%, respectively. Pessimistic experts believe that warfarin worsens survival by 7%. Optimistic experts believe that warfarin improves survival by 13%. In IPAH, the mean probabilities of 3-year survival without and with warfarin were 68% and 76%. Factors (mean rating out of 10, 0 = not at all important, 10 = extremely important) that influence experts’ use of warfarin were functional class (5.4), age (5.4), pulmonary artery pressure (5.2), peripheral vascular disease (3.6), disease duration (2.8), and sex (1.7).Conclusion.Bayesian priors effectively quantify and illustrate experts’ beliefs about the effect of warfarin on survival in SSc-PAH and IPAH. This study demonstrates the presence of uncertainty about the effect of warfarin, and provides justification for a clinical trial.

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Grimm, Sabine E., Simon Dixon, and John W. Stevens. "Assessing the Expected Value of Research Studies in Reducing Uncertainty and Improving Implementation Dynamics." Medical Decision Making 37, no. 5 (January 6, 2017): 523–33. http://dx.doi.org/10.1177/0272989x16686766.

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Background. With low implementation of cost-effective health technologies being a problem in many health systems, it is worth considering the potential effects of research on implementation at the time of health technology assessment. Meaningful and realistic implementation estimates must be of dynamic nature. Objective. To extend existing methods for assessing the value of research studies in terms of both reduction of uncertainty and improvement in implementation by considering diffusion based on expert beliefs with and without further research conditional on the strength of evidence. Methods. We use expected value of sample information and expected value of specific implementation measure concepts accounting for the effects of specific research studies on implementation and the reduction of uncertainty. Diffusion theory and elicitation of expert beliefs about the shape of diffusion curves inform implementation dynamics. We illustrate use of the resulting dynamic expected value of research in a preterm birth screening technology and results are compared with those from a static analysis. Results. Allowing for diffusion based on expert beliefs had a significant impact on the expected value of research in the case study, suggesting that mistakes are made where static implementation levels are assumed. Incorporating the effects of research on implementation resulted in an increase in the expected value of research compared to the expected value of sample information alone. Conclusions. Assessing the expected value of research in reducing uncertainty and improving implementation dynamics has the potential to complement currently used analyses in health technology assessments, especially in recommendations for further research. The combination of expected value of research, diffusion theory, and elicitation described in this article is an important addition to the existing methods of health technology assessment.

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Kinnersley, Nelson, and Simon Day. "Structured approach to the elicitation of expert beliefs for a Bayesian-designed clinical trial: a case study." Pharmaceutical Statistics 12, no. 2 (January 21, 2013): 104–13. http://dx.doi.org/10.1002/pst.1552.

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Stefanini, Federico M. "Chain Graph Models to Elicit the Structure of a Bayesian Network." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/749150.

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Bayesian networks are possibly the most successful graphical models to build decision support systems. Building the structure of large networks is still a challenging task, but Bayesian methods are particularly suited to exploit experts’ degree of belief in a quantitative way while learning the network structure from data. In this paper details are provided about how to build a prior distribution on the space of network structures by eliciting a chain graph model on structural reference features. Several structural features expected to be often useful during the elicitation are described. The statistical background needed to effectively use this approach is summarized, and some potential pitfalls are illustrated. Finally, a few seminal contributions from the literature are reformulated in terms of structural features.

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Mario, Moroo, and Anindita Das. "Development of an Instrument Based on Salient Behavioral Beliefs to Measure Attitude towards Physical Education." Physical Education Theory and Methodology 22, no. 3s (November 30, 2022): S102—S109. http://dx.doi.org/10.17309/tmfv.2022.3s.14.

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Study purpose. The study purpose was to develop an instrument to measure students’ attitude towards physical education through salient behavioral beliefs. Materials and methods. The participants of the study were both boys and girls of grades 7 to 10 from the state of Manipur (India) in the first, second and fourth phase. In the first phase, based on the recommendation of the theory of reasoned action and its extension, the theory of planned behavior, an elicitation study was conducted using open-ended questions, where a total of 308 student responses were collected. The student responses were used to develop items through content analysis. In the second phase, i.e. content validity study, a total of 5 experts were recruited to evaluate the developed items from the elicitation study. In the third phase i.e. pilot study, out of the total of 123 student responses, 104 student responses were found to be usable for analysis. In the fourth phase, i.e. main study, out of the total of 433 student responses, 377 student responses were found to be usable for analysis. Results. Due to lack of good model fit from the pilot study, a second exploratory factor analysis was conducted with the first half of the main study data which produced a three-factor model, and this model was tested with confirmatory factor analysis with the other half of the main study data. The model fit indices were found to be appropriate, and also the composite reliability and construct validity were found to be appropriate. Conclusions. This belief-based attitude measuring instrument was found to be valid and reliable for grades 7 to 10 in the Indian physical education context, i.e. schools which incorporate both yoga and physical education in the general physical education curriculum.

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Jousselme, Anne-Laure, Giuliana Pallotta, and John Locke. "Risk Game: Capturing impact of information quality on human belief assessment and decision making." International Journal of Serious Games 5, no. 4 (December 18, 2018): 23–44. http://dx.doi.org/10.17083/ijsg.v5i4.258.

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This paper presents the Risk Game, a general methodology to elicit experts’ knowledge and know-how, in their ability to deal with information provided by different types of sources (sensors or humans) of variable quality, to take into account the information quality and to reason about concurrent events. It is a contrived technique capturing data expressing human reasoning features during a specific task of situation assessment. The information is abstracted by cards and its quality, which varies along the three dimensions of uncertainty, imprecision and falseness, is randomly selected by dice roll. The game has been played by experts of maritime surveillance, mostly marine officers from several nations. The Risk Game is domain-independent and can be designed for any specific application involving reasoning with multi-sources. The preliminary results obtained are promising and allow validating the efficiency of the elicitation method in capturing the link between information quality and human belief assessment. Besides the positive feedback collected from the players and their perceived effectiveness of the method, the data effectively capture the impact some specific information quality dimensions on belief assessment. We highlight, for instance, that the relevance of information perceived by the players may differ from the effective information relevance, that a high ratio of false information increases the uncertainty of the player before decision and may lead to wrong decisions, or that the context has a high impact on the decision made. Future extensions of the Risk Game are finally sketched.

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Schapira, Marilyn M., Keri L. Rodriguez, Sumedha Chhatre, Liana Fraenkel, Lori A. Bastian, Jeffrey D. Kravetz, Onur Asan, et al. "When Is a Harm a Harm? Discordance between Patient and Medical Experts’ Evaluation of Lung Cancer Screening Attributes." Medical Decision Making 41, no. 3 (February 6, 2021): 317–28. http://dx.doi.org/10.1177/0272989x20987221.

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Background A shared decision-making (SDM) process for lung cancer screening (LCS) includes a discussion between clinicians and patients about benefits and potential harms. Expert-driven taxonomies consider mortality reduction a benefit and consider false-positives, incidental findings, overdiagnosis, overtreatment, radiation exposure, and direct and indirect costs of LCS as potential harms. Objective To explore whether patients conceptualize the attributes of LCS differently from expert-driven taxonomies. Design Cross-sectional study with semistructured interviews and a card-sort activity. Participants Twenty-three Veterans receiving primary care at a Veterans Affairs Medical Center, 55 to 73 y of age with 30 or more pack-years of smoking. Sixty-one percent were non-Hispanic African American or Black, 35% were non-Hispanic White, 4% were Hispanic, and 9% were female. Approach Semistructured interviews with thematic coding. Main Measures The proportion of participants categorizing each attribute as a benefit or harm and emergent themes that informed this categorization. Key Results In addition to categorizing reduced lung cancer deaths as a benefit (22/23), most also categorized the following as benefits: routine annual screening (8/9), significant incidental findings (20/23), follow-up in a nodule clinic (20/23), and invasive procedures (16/23). Four attributes were classified by most participants as a harm: false-positive (13/22), overdiagnosis (13/23), overtreatment (6/9), and radiation exposure (20/22). Themes regarding the evaluation of LCS outcomes were 1) the value of knowledge about body and health, 2) anticipated positive and negative emotions, 3) lack of clarity in terminology, 4) underlying beliefs about cancer, and 5) risk assessment and tolerance for uncertainty. Conclusions Anticipating discordance between patient- and expert-driven taxonomies of the benefits and harms of LCS can inform the development and interpretation of value elicitation and SDM discussions.

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Chambers, Christopher P., and Nicolas S. Lambert. "Dynamic Belief Elicitation." Econometrica 89, no. 1 (2021): 375–414. http://dx.doi.org/10.3982/ecta15293.

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At an initial time, an individual forms a belief about a future random outcome. As time passes, the individual may obtain, privately or subjectively, further information, until the outcome is eventually revealed. How can a protocol be devised that induces the individual, as a strict best response, to reveal at the outset his prior assessment of both the final outcome and the information flows he anticipates and, subsequently, what information he privately receives? The protocol can provide the individual with payoffs that depend only on the outcome realization and his reports. We develop a framework to design such protocols, and apply it to construct simple elicitation mechanisms for common dynamic environments. The framework is general: we show that strategyproof protocols exist for any number of periods and large outcome sets. For these more general settings, we build a family of strategyproof protocols based on a hierarchy of choice menus, and show that any strategyproof protocol can be approximated by a protocol of this family.

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Tsakas, Elias. "Obvious belief elicitation." Games and Economic Behavior 118 (November 2019): 374–81. http://dx.doi.org/10.1016/j.geb.2019.07.002.

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Knippen, Kerri Lynn, Natalie Walkup, Cameron Burmeister, Joseph Dake, and Kent Bishop. "Factors Associated with Breastfeeding Duration and Satisfaction after Gestational Diabetes among Women Living in Northwest Ohio." Ohio Journal of Public Health 3, no. 1 (June 1, 2020): 9–18. http://dx.doi.org/10.18061/ojph.v3i1.9016.

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Background: Given the potential for type 2 diabetes and the protective benefits of breastfeeding after gestational diabetes mellitus (GDM), there is a need to promote and support breastfeeding; however, delayed lactogenesis and postpartum experiences may challenge breastfeeding success. We aimed to describe factors that influence breastfeeding duration and satisfaction after GDM.Methods: A cross-sectional survey, informed by an elicitation phase and subject matter expert review, was conducted to evaluate factors associated with breastfeeding satisfaction and duration after GDM. The study included women (n = 50) from Northwest Ohio who delivered a living child from a singleton pregnancy at greater than or equal to 34 weeks gestation, who intended to breastfeed after GDM. Spearman correlation and Mann-Whitney U test were calculated to evaluate factors associated with breastfeeding duration and satisfaction.Results: Women described a lack of breastfeeding support, and there appeared to be a lack of awareness on the benefits of breastfeeding after GDM. Attitudes were associated with breastfeeding duration and satisfaction. Negative experiences in the child’s first week of life were associated with shorter duration and lower level of satisfaction. Delayed lactogenesis, barriers after delivery, and negative normative influences were significantly associated with a lower level of breastfeeding satisfaction.Conclusion: More work is needed to deliver breastfeeding education and support after GDM. Interventions tailored for GDM are recommended to promote positive breastfeeding beliefs and realistic breastfeeding expectations. Ongoing support to address early experiences and barriers after GDM is recommended. Further work should examine these factors in a larger, more diverse sample.

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O'Hagan, A. "Eliciting expert beliefs in substantial practical applications [Read before The Royal Statistical Society at ameeting on 'Elicitation' on Wednesday, april 16th, 1997, the President, Professor A. F. M. Smithin the Chair]." Journal of the Royal Statistical Society: Series D (The Statistician) 47, no. 1 (March 1998): 21–35. http://dx.doi.org/10.1111/1467-9884.00114.

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Burdea, Valeria, and Jonathan Woon. "Online belief elicitation methods." Journal of Economic Psychology 90 (June 2022): 102496. http://dx.doi.org/10.1016/j.joep.2022.102496.

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Falahee, Marie, Gwenda Simons, Rachael L. DiSantostefano, Larissa Valor Méndez, Christine Radawski, Matthias Englbrecht, Karin Schölin Bywall, et al. "Treatment preferences for preventive interventions for rheumatoid arthritis: protocol of a mixed methods case study for the Innovative Medicines Initiative PREFER project." BMJ Open 11, no. 4 (April 2021): e045851. http://dx.doi.org/10.1136/bmjopen-2020-045851.

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IntroductionAmidst growing consensus that stakeholder decision-making during drug development should be informed by an understanding of patient preferences, the Innovative Medicines Initiative project ‘Patient Preferences in Benefit-Risk Assessments during the Drug Life Cycle’ (PREFER) is developing evidence-based recommendations about how and when patient preferences should be integrated into the drug life cycle. This protocol describes a PREFER clinical case study which compares two preference elicitation methodologies across several populations and provides information about benefit–risk trade-offs by those at risk of rheumatoid arthritis (RA) for preventive interventions.Methods and analysisThis mixed methods study will be conducted in three countries (UK, Germany, Romania) to assess preferences of (1) first-degree relatives (FDRs) of patients with RA and (2) members of the public. Focus groups using nominal group techniques (UK) and ranking surveys (Germany and Romania) will identify and rank key treatment attributes. Focus group transcripts will be analysed thematically using the framework method and average rank orders calculated. These results will inform the treatment attributes to be assessed in a survey including a discrete choice experiment (DCE) and a probabilistic threshold technique (PTT). The survey will also include measures of sociodemographic variables, health literacy, numeracy, illness perceptions and beliefs about medicines. The survey will be administered to (1) 400 FDRs of patients with RA (UK); (2) 100 FDRs of patients with RA (Germany); and (3) 1000 members of the public in each of UK, Germany and Romania. Logit-based approaches will be used to analyse the DCE and imputation and interval regression for the PTT.Ethics and disseminationThis study has been approved by the London-Hampstead Research Ethics Committee (19/LO/0407) and the Ethics Committee of the Friedrich-Alexander-Universität Erlangen-Nürnberg (92_17 B). The protocol has been approved by the PREFER expert review board. The results will be disseminated widely and will inform the PREFER recommendations.

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Schotter, Andrew, and Isabel Trevino. "Belief Elicitation in the Laboratory." Annual Review of Economics 6, no. 1 (August 2014): 103–28. http://dx.doi.org/10.1146/annurev-economics-080213-040927.

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Chambers, Christopher P., and Nicolas S. Lambert. "Introduction to dynamic belief elicitation." ACM SIGecom Exchanges 14, no. 1 (November 12, 2015): 80–83. http://dx.doi.org/10.1145/2845926.2845930.

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Danz, David, Lise Vesterlund, and Alistair J. Wilson. "Belief Elicitation and Behavioral Incentive Compatibility." American Economic Review 112, no. 9 (September 1, 2022): 2851–83. http://dx.doi.org/10.1257/aer.20201248.

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Subjective beliefs are crucial for economic inference, yet behavior can challenge the elicitation. We propose that belief elicitation should be incentive compatible not only theoretically but also in a de facto behavioral sense. To demonstrate, we show that the binarized scoring rule, a state-of-the-art elicitation, violates two weak conditions for behavioral incentive compatibility: (i) within the elicitation, information on the incentives increases deviations from truthful reporting; and (ii) in a pure choice over the set of incentives, most deviate from the theorized maximizer. Moreover, we document that deviations are systematic and center-biased, and that the elicited beliefs substantially distort inference. (JEL D83, D91)

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Eyting, Markus, and Patrick Schmidt. "Belief elicitation with multiple point predictions." European Economic Review 135 (June 2021): 103700. http://dx.doi.org/10.1016/j.euroecorev.2021.103700.

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Schlag, Karl, and James Tremewan. "Simple belief elicitation: An experimental evaluation." Journal of Risk and Uncertainty 62, no. 2 (April 2021): 137–55. http://dx.doi.org/10.1007/s11166-021-09349-6.

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Crosetto, Paolo, Antonio Filippin, Peter Katuščák, and John Smith. "Central tendency bias in belief elicitation." Journal of Economic Psychology 78 (June 2020): 102273. http://dx.doi.org/10.1016/j.joep.2020.102273.

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Middlestadt, Susan E. "Beliefs Underlying Eating Better and Moving More." ANNALS of the American Academy of Political and Social Science 640, no. 1 (February 10, 2012): 81–100. http://dx.doi.org/10.1177/0002716211425015.

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The reasoned action approach has been used to identify the determinants of a behavior to be modified by social and behavioral interventions. Information on the specific beliefs underlying behavioral decisions is vital to intervention design. More attention is needed on the salient belief elicitation—a critical step in a theory-based formative research process. This article considers the methodological issues involved in conducting a salient belief elicitation with special attention to an elicitation that allows the comparison of results across several behaviors and priority groups. The author uses the behaviors underlying overweight and obesity as her central example. To support the development of interventions to improve cardiovascular health and to explore methods for a comparative analysis of salient belief elicitations, semistructured interviews were conducted with 243 adults from two rural and two urban worksites in Indiana and 344 students from three middle schools in rural Indiana. Content and frequency analyses identified the modal salient beliefs underlying several eating and physical activity behaviors. Illustrative results are presented, and recommendations for salient belief elicitation are discussed.

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O’Hagan, Anthony. "Expert Knowledge Elicitation: Subjective but Scientific." American Statistician 73, sup1 (March 20, 2019): 69–81. http://dx.doi.org/10.1080/00031305.2018.1518265.

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van Wingerden, J. W., P. A. Fleming, T. Göçmen, I. Eguinoa, B. M. Doekemeijer, K. Dykes, M. Lawson, et al. "Expert Elicitation on Wind Farm Control." Journal of Physics: Conference Series 1618 (September 2020): 022025. http://dx.doi.org/10.1088/1742-6596/1618/2/022025.

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Tregoning, Robert Lee, Lee Richard Abramson, Paul Michael Scott, and Nilesh Chokshi. "LOCA frequency evaluation using expert elicitation." Nuclear Engineering and Design 237, no. 12-13 (July 2007): 1429–36. http://dx.doi.org/10.1016/j.nucengdes.2006.09.036.

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Astfalck, L. C., E. J. Cripps, J. P. Gosling, M. R. Hodkiewicz, and I. A. Milne. "Expert elicitation of directional metocean parameters." Ocean Engineering 161 (August 2018): 268–76. http://dx.doi.org/10.1016/j.oceaneng.2018.04.047.

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Bedford, Tim, John Quigley, and Lesley Walls. "Expert Elicitation for Reliable System Design." Statistical Science 21, no. 4 (November 2006): 428–50. http://dx.doi.org/10.1214/088342306000000510.

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Albert, Isabelle, Sophie Donnet, Chantal Guihenneuc-Jouyaux, Samantha Low-Choy, Kerrie Mengersen, and Judith Rousseau. "Combining Expert Opinions in Prior Elicitation." Bayesian Analysis 7, no. 3 (September 2012): 503–32. http://dx.doi.org/10.1214/12-ba717.

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Bosetti, Valentina, Francis Dennig, Ning Liu, Massimo Tavoni, and Elke U. Weber. "Forward-Looking Belief Elicitation Enhances Intergenerational Beneficence." Environmental and Resource Economics 81, no. 4 (January 25, 2022): 743–61. http://dx.doi.org/10.1007/s10640-022-00648-3.

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Papp, Kim A., Robert Gniadecki, Jennifer Beecker, Jan Dutz, Melinda J. Gooderham, Chih-Ho Hong, Mark G. Kirchhof, et al. "Psoriasis Prevalence and Severity by Expert Elicitation." Dermatology and Therapy 11, no. 3 (April 22, 2021): 1053–64. http://dx.doi.org/10.1007/s13555-021-00518-8.

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Forester, John, Dennis Bley, Susan Cooper, Erasmia Lois, Nathan Siu, Alan Kolaczkowski, and John Wreathall. "Expert elicitation approach for performing ATHEANA quantification." Reliability Engineering & System Safety 83, no. 2 (February 2004): 207–20. http://dx.doi.org/10.1016/j.ress.2003.09.011.

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O’Brien, Sinead, William G. Proud, and Dr Margaret A. Wilson. "Elicitation of knowledge from a defence expert." Journal of Physics: Conference Series 1507 (March 2020): 102027. http://dx.doi.org/10.1088/1742-6596/1507/10/102027.

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Fenton, Norman, and Martin Neil. "Comment: Expert Elicitation for Reliable System Design." Statistical Science 21, no. 4 (November 2006): 451–53. http://dx.doi.org/10.1214/088342306000000529.

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Koehler, Andrew. "Comment: Expert Elicitation for Reliable System Design." Statistical Science 21, no. 4 (November 2006): 454–55. http://dx.doi.org/10.1214/088342306000000538.

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Wang, Wenbin. "Comment: Expert Elicitation for Reliable System Design." Statistical Science 21, no. 4 (November 2006): 456–59. http://dx.doi.org/10.1214/088342306000000547.

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