Dataset Information

The Fold-in, Fold-out Design for DCE Choice Tasks: Application to Burden of Disease.

ABSTRACT: Background In discrete-choice experiments (DCEs), choice alternatives are described by attributes. The importance of each attribute can be quantified by analyzing respondents' choices. Estimates are valid only if alternatives are defined comprehensively, but choice tasks can become too difficult for respondents if too many attributes are included. Several solutions for this dilemma have been proposed, but these have practical or theoretical drawbacks and cannot be applied in all settings. The objective of the current article is to demonstrate an alternative solution, the fold-in, fold-out approach (FiFo). We use a motivating example, the ABC Index for burden of disease in chronic obstructive pulmonary disease (COPD). Methods Under FiFo, all attributes are part of all choice sets, but they are grouped into domains. These are either folded in (all attributes have the same level) or folded out (levels may differ). FiFo was applied to the valuation of the ABC Index, which included 15 attributes. The data were analyzed in Bayesian mixed logit regression, with additional parameters to account for increased complexity in folded-out questionnaires and potential differences in weight due to the folding status of domains. As a comparison, a model without the additional parameters was estimated. Results Folding out domains led to increased choice complexity for respondents. It also gave domains more weight than when it was folded in. The more complex regression model had a better fit to the data than the simpler model. Not accounting for choice complexity in the models resulted in a substantially different ABC Index. Conclusion Using a combination of folded-in and folded-out attributes is a feasible approach for conducting DCEs with many attributes.

SUBMITTER: Goossens LMA

PROVIDER: S-EPMC6613173 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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The Fold-in, Fold-out Design for DCE Choice Tasks: Application to Burden of Disease.

Goossens Lucas M A LMA Jonker Marcel F MF Rutten-van Mölken Maureen P M H MPMH Boland Melinde R S MRS Slok Annerika H M AHM Salomé Philippe L PL van Schayck Onno C P OCP In 't Veen Johannes C C M JCCM Stolk Elly A EA Donkers Bas B

Medical decision making : an international journal of the Society for Medical Decision Making 20190529 4

<b>Background</b> In discrete-choice experiments (DCEs), choice alternatives are described by attributes. The importance of each attribute can be quantified by analyzing respondents' choices. Estimates are valid only if alternatives are defined comprehensively, but choice tasks can become too difficult for respondents if too many attributes are included. Several solutions for this dilemma have been proposed, but these have practical or theoretical drawbacks and cannot be applied in all settings. ...[more]

PMID: 31142198

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Project description:ObjectiveOur objective was to develop and test a discrete-choice experiment (DCE) survey to elicit adolescent and parent preferences for dental care for hypodontia (a developmental condition where one or more teeth fail to develop).MethodsThis was a mixed-methods study. Participants were adolescents (aged 12-16 years) with hypodontia and their parents and the dentists providing hypodontia care. Stage one entailed attribute development, as follows. (1) Attribute identification: systematic review of hypodontia literature; interviews with adolescents with hypodontia (n = 8) and parents (n = 8); observation of hypodontia clinical consultations (n = 5); environmental scan of hypodontia patient information resources (n = 30); and systematic analysis of social media posts (n = 176). (2) Attribute selection: stakeholder consultation to develop items for a questionnaire; rating and ranking questionnaire for adolescents with hypodontia and parents (n = 18); further stakeholder consultation. Stage two involved the development of the DCE survey, and stage three included the pre-testing using cognitive interviews with adolescents (n = 12) and parents (n = 8) to assess face and content validity.ResultsThe attribute long list included 27 attributes focusing on service delivery and treatment outcome, from which seven 'important' attributes were selected for pre-testing. Cognitive interviewing suggested adolescents found the DCE choice tasks challenging to understand; the survey was modified to enhance its acceptability. One attribute was excluded as it showed poor validity with adolescents. Pre-testing suggested DCE choice tasks encouraged thinking and discussion about preferences for treatment.ConclusionsIncluding the target respondent group in all stages of DCE development ensured the final DCE survey was valid and acceptable. DCE methods appear to be a useful tool for exploring joint decision making alongside conventional preference elicitation.

Project description:BackgroundDysphonia influences the quality of life by interfering with communication. However, a laryngoscopic examination is expensive and not readily accessible in primary care units. Experienced laryngologists are required to achieve an accurate diagnosis.ObjectiveThis study sought to detect various vocal fold diseases through pathological voice recognition using artificial intelligence.MethodsWe collected 189 normal voice samples and 552 samples of individuals with voice disorders, including vocal atrophy (n=224), unilateral vocal paralysis (n=50), organic vocal fold lesions (n=248), and adductor spasmodic dysphonia (n=30). The 741 samples were divided into 2 sets: 593 samples as the training set and 148 samples as the testing set. A convolutional neural network approach was applied to train the model, and findings were compared with those of human specialists.ResultsThe convolutional neural network model achieved a sensitivity of 0.66, a specificity of 0.91, and an overall accuracy of 66.9% for distinguishing normal voice, vocal atrophy, unilateral vocal paralysis, organic vocal fold lesions, and adductor spasmodic dysphonia. Compared with the accuracy of human specialists, the overall accuracy rates were 60.1% and 56.1% for the 2 laryngologists and 51.4% and 43.2% for the 2 general ear, nose, and throat doctors.ConclusionsVoice alone could be used for common vocal fold disease recognition through a deep learning approach after training with our Mandarin pathological voice database. This approach involving artificial intelligence could be clinically useful for screening general vocal fold disease using the voice. The approach includes a quick survey and a general health examination. It can be applied during telemedicine in areas with primary care units lacking laryngoscopic abilities. It could support physicians when prescreening cases by allowing for invasive examinations to be performed only for cases involving problems with automatic recognition or listening and for professional analyses of other clinical examination results that reveal doubts about the presence of pathologies.

Dataset Information

The Fold-in, Fold-out Design for DCE Choice Tasks: Application to Burden of Disease.

Publications

The Fold-in, Fold-out Design for DCE Choice Tasks: Application to Burden of Disease.

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