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24 results for “DISCRET CHOICE EXPERIMENT”
CUPID: Parental preferences for unscheduled paediatric healthcare: A Discrete Choice Experiment
<p>Unscheduled healthcare is a key component of healthcare delivery and makes up a significant proportion of healthcare access with children being particularly high users of unscheduled healthcare. Understanding the relative importance of factors that influence this behaviour and decision making is fundamental to ensuring the system is best designed to meet the needs of users, and the appropriate cost-effective usage of health system resources.</p> <p>A discrete choice experiment (DCE) was developed to identify the preferences of parents accessing unscheduled healthcare for their children. Data was collected from parents in Ireland (N = 458) to elicit preferences across five attributes: timeliness, appointment type, healthcare professional attended, telephone guidance prior to attending, and cost. Using mixed logit models, all attributes were statistically significant, with same-day or next-day access, coupled with care by their own GP, identified as the strongest preferences of parents accessing unscheduled healthcare for their children. The results have implications for policy development and implementation initiatives that seek to improve unscheduled health services as understanding how parents use these services can maximise their effectiveness. </p> <p>Files explained:</p> <p>1. Parent_Decision_Making_DCE_Survey.docx details the survey.</p> <p>2. Final Parent DCE design.ngd: Ngene™ file used to set-up the DCE. A Bayesian efficient design, based on minimising the Bayesian D-error criterion, was used to develop the choice sets and the alternatives using Ngene<sup>TM </sup>software. In total, 24 choice sets were created, and a blocked design split the choice sets into two blocks of twelve to minimise the burden on respondents.</p> <p>3. Data from Survey 23 Feb 2021.xlsx is the source file detailing responses to the survey.</p> <p>4. Stata Data Formatted for DCE.dta is the file formatted for the DCE for analysis.</p>
EVIDENT H2020 – Discrete Choice Experiment Dataset
<p>The EVIDENT Discrete Choice Experiment seeks to explore the impact of energy related financial literacy, consumer motivation, point-of-sale information and demographic factors on discount rate and willingness to pay for efficient household appliances. Across a series of choice experiments, the impact of factors such as financial information (purchase price, operating cost, salience of financial information), risk reduction (i.e. extended warranty), and financial capacity (i.e. low cost loans) on implicit discount rates for home appliances is examined. Further, the impact of direct rebound rates on efficient appliance selection is examined.</p> <p>The experiment consists of the following sections: 1) demographic information; 2) current home appliance purchasing behaviour; 3) financial literacy; 4) environmental literacy; 5) stated preference experiment consisting of four choice points; 6) discount rates; 7) discrete choice experiment consisting of ten choice points; and 8) questions examining direct rebound rates associated with the novel appliance selected.</p> <p>As noted above, two choice experiments are included within the current use case. The first of these is a stated preference experiment which examines the impact of financial and energy framing on willingness-to-pay for energy efficient appliances. Four choice points are presented within this experiment. Choice 1 presents five identical versions of an appliance which differ only by key feature, and seeks to reduce hypothetical bias across the choice experiment. For example, for a washing machine the key features are cost, capacity, spin speed, quick wash time and pause wash functionality. Choice 2 consists of the participants initial choice (at choice 1) alongside alternatives which differ only in purchase price and energy rating, with purchase price greater for more efficient appliances (I.e. A rated appliances are most expensive; D rated appliances are least expensive). Choice 3 is similar to choice 2, however in this instance operational costs per month are also presented. Again, operational costs are lower for more efficient appliances. Choice 3 is similar to choice 3 however in this instance operational costs per year are presented.</p> <p>The second choice experiment is the DCE which explores the relative impacts of risk reduction (extended warranty), and financial supports (low cost loan, loan term) on willingness to invest in more efficient energy appliances. Attributes were selected based on literature review, focus group analyses, cognitive walk-through and usability analyses. Once final attributes were determined, choice cards were developed using a fractional factorial design. A statistically efficient main-effects design with 10 choice sets was created in R studio using the idefix package. As such, participants are presented with a series of ten choice points, each consisting of two appliances and a ‘no preference’ option.</p> <p>More information on the EVIDENT Discrete Choice Experiment can be found on the public deliverables of the EVIDENT project <a href="https://evident-h2020.eu/deliverables/">https://evident-h2020.eu/deliverables/</a>. More specifically, the experiment's theoretical framework and motivation are described in deliverable D1.2 <a href="https://evident-h2020.eu/wp-content/uploads/2021/12/EVIDENT_D1.2_Assessing_behavioural_biases_and_financial_literacy.pdf">Assessing behavioural biases and financial literacy</a>, in section 5 while the final design is reported in D2.2 <a href="https://space.uowm.gr/confluence/display/evidenth2020/D2.2+Optimized+protocols+design">Optimised Protocols Design</a></p>
Data from: How to use discrete choice experiments to capture stakeholder preferences in social work research
<p>The primary article (cited below under "Related works") introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. The article includes an online supplement with a worked example demonstrating DCE design and analysis with realistic simulated data. The worked example focuses on caregivers' priorities in choosing treatment for children with attention deficit hyperactivity disorder. This dataset includes the scripts (and, in some cases, Excel files) that we used to identify appropriate experimental designs, simulate population and sample data, estimate sample size requirements for the multinomial logit (MNL, also known as conditional logit) and random parameter logit (RPL) models, estimate parameters using the MNL and RPL models, and analyze attribute importance, willingness to pay, and predicted uptake. It also includes the associated data files (experimental designs, data generation parameters, simulated population data and parameters, simulated choice data, MNL and RPL results, RPL sample size simulation results, and willingness-to-pay results) and images. The data could easily be analyzed using other software, and the code could easily be adapted to analyze other data. Because this dataset contains only simulated data, we are not aware of any legal or ethical considerations.</p>
Data from: How to use discrete choice experiments to capture stakeholder preferences in social work research
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Results and analysis script from a discrete choice experiment assessing public preferences for rewilding in the Oder Delta
<p>1. Rewilding is an emerging paradigm in restoration science, and is increasingly gaining popularity as a cost-effective ecosystem restoration option. A rewilding framework was recently proposed that contains three integral components: restoring trophic complexity, allowing for stochastic disturbances, and enhancing species' potential to disperse. However, as of yet, there has been limited quantitative analysis looking at public preference for rewilding and each of its elements.</p> <p>2. We used a discrete choice experiment approach to determine public preference for rewilding in the Oder Delta. The unique geographical context of the Oder Delta, spreading evenly across two countries, allowed us to analyze differences between the German (n = 1,005) and Polish (n = 1,066) samples.</p> <p>3. In both countries, we found respondents were willing to pay for rewilding interventions when compared against a status quo option. Notably, preferences were strongest for restoring trophic complexity through promoting the comeback of large mammals.</p> <p>4. In addition, we found respondents living locally to the study region had significantly different preferences than the nationwide samples, exhibiting negative willingness to pay for the restoration of natural flooding regimes and the presence of large predator species.</p>
The role of contextual factors in decision-making by General Practitioners on paediatric referral to the Emergency Department: A Discrete Choice Experiment
<p>A General Practitioner’s (GP) decision to refer a patient to the emergency department (ED) requires consideration of a multitude of factors, and significant variation in GP referral patterns to secondary care has been recorded. This study examines the contextual factors that influence GPs when referring a paediatric patient with potentially self-limiting clinical symptoms to the ED.</p> <p>Utilizing a discrete choice experiment, survey data was collected from GPs in Ireland (n = 142) to elicit factors influencing this decision across five attributes: time/day of visit, repeat presentation, parents’ capacity to cope, parent requesting a referral, and access to a paediatric outpatient clinic/day unit.</p> <p>Using mixed logit models, all attributes were statistically significant, with repeat presentation and parents lacking the capacity to cope with a sick child identified as the strongest contextual factors leading to the decision to refer to the ED.</p> <p> </p> <p>Files explained:</p> <p>1. Survey Questions.docx details the survey.</p> <p>2. Ngene DCE design.ngd: Ngene™ file used to set-up the DCE.</p> <p>3. Survey Data File.xlsx is the source file detailing responses to the survey.</p> <p>4. Stata Data File Formatted for DCE.dta is the file formatted for the DCE for analysis</p> <p> </p> <p>Note:</p> <p><em>Data collection for a second survey for a patient with intellectual disability and limited communication skills was carried out at the same time as this survey. Data from this second study (Q4.1 – Q4.7 & Q6.1 – Q6.7) are not included in these files. </em></p>
Patient preferences for HIV service delivery models: A discrete choice experiment in Kisumu, Kenya
<p><strong>Background</strong>: Novel "differentiated service delivery" models for HIV treatment that reduce clinic visit frequency, minimize waiting time, and deliver treatment in the community promise retention improvement for HIV treatment in Sub-Saharan Africa. Quantitative assessments of differentiated service delivery (DSD) feature most preferred by patient populations do not widely exist but could inform the selection and prioritization of different types of DSD models.</p> <p><strong>Methods</strong>: We used a discrete choice experiment (DCE) to elicit patient preferences for HIV treatment services and how they differ across DSD models. We surveyed adults aged >18 years, enrolled in HIV care for >6 months between February and March 2019 at four facilities in Kisumu County, Kenya. DCE offered patients a series of comparisons between three treatment models, each of which varied in seven attributes: ART refill location, the quantity of ART dispensed at each refill, medication pick-up hours, type of adherence support, clinical visit frequency, staff attitude, and professional cadre of the person providing ART refills. We used a hierarchical Bayesian model to estimate attribute importance and the relative desirability of care characteristics, latent class analysis (LCA) for groups of preferences, and mixed logit model for willingness to trade analysis.</p> <p><strong>Results</strong>: Of 242 patients, 128 (53.8%) were females and 150 (62.8%) lived in rural areas. Patients placed the greatest importance on ART refill location [19.5% (95%CI 18.4, 20.6)] and adherence support [19.5% (95%CI 18.7, 20.3)], followed by staff attitude [16.1% (95%CI 15.1, 17.2)]. In the mixed logit, patients preferred the nice attitude of the staff (coefficient=1.60), refill ART health center (Coeff=1.58), and individual adherence support (Coeff=1.54), 3 or 6 months for ART refill (Coeff=0.95 & 0.80, respectively) and pharmacists (instead of lay health workers) providing ART refill (Coeff=0.64). No differences were observed by gender or urbanicity. LCA revealed two distinct groups (59.5% vs.40.5%).</p> <p><strong>Conclusions</strong>: Participants preferred 3 to 6-month refill intervals or clinic visit spacing, which DSD provides for stable patients. While DSD has also encouraged community ART group options, our results suggest strong patient preferences for ART refills from health centers or by pharmacists over lay caregivers or community members. These preferences held across gender and urban/rural subpopulations. </p>
Results and analysis script from a discrete choice experiment assessing public preferences for rewilding in the Oder Delta
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Patient preferences for HIV service delivery models: A discrete choice experiment in Kisumu, Kenya
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Understanding what women want: eliciting preference for delivery health facility in a rural sub-County in Kenya, a discrete choice experiment
<p>Objective: To identify what women want in a delivery health facility and how they rank the attributes that influence the choice of a place of delivery.</p> <p>Design: A Discrete Choice Experiment was conducted to elicit rural women's preferences for choice of delivery health facility. Data were analyzed using both a conditional logit model to evaluate relative importance of the selected attributes. A mixed multinomial model evaluated how interactions with sociodemographic variables influence the choice of the selected attributes.</p> <p>Setting: Six health facilities in a rural sub-County.</p> <p>Participants: Women aged 18-49 years who had delivered within six weeks.</p> <p>Primary outcome: The DCE required women to select from hypothetical health facility A or B or opt-out alternative.</p> <p>Results: A total of 474 participants were sampled, 466 participants completed the survey (response rate 98%).The attribute with the strongest association with health facility preference was having a kind and supportive healthcare worker (β=1.184, p&lt;0.001), second availability of medical equipment and drug supplies (β=1.073, p&lt;0.001) and third quality of clinical services (β=0.826, p&lt;0.001). Distance, availability of referral services and costs were ranked 4th, 5th and 6th respectively (β=0.457, p&lt;0.001),<br> (β=0.266 p&lt;0.001), and (β=0.000018, p&lt;0.001). The opt-out alternative ranked last suggesting a disutility for home delivery. (β=-0.849, p&lt;0.001).</p> <p>Conclusion: The most highly valued attribute was a process indicator of quality of care followed by technical indicators. Policy makers need to consider women's preferences to inform strategies that are person-centered and lead to improvements in quality of care during delivery.</p>
Quantifying mothers' preferences for providing information about non-invasive prenatal testing in Sweden: evidence from a discrete choice experiment
<p>Training materials for a discrete choice experiment survey designed to quantify mothers' preferences for information about non-invasive prenatal testing in Sweden.</p>
Preferences for Certainty Versus Access When Evaluating New Cancer Drugs. A Discrete Choice Experiment.
ClinicalTrials.gov study NCT05936632. IPD Sharing: NO. Countries: 1. Publications: 1.
Preferences in Pain Treatment: A Discrete Choice Experiment in Patients With Peripheral Neuropathic Pain (pNP)
ClinicalTrials.gov study NCT04184596. IPD Sharing: NO. Countries: 1. Publications: 1.
Patient and Physician Benefit/ Risk Preferences for Treatment of mPC in Hong Kong: a Discrete Choice Experiment
ClinicalTrials.gov study NCT05761093. IPD Sharing: NO. Countries: 1. Publications: 8.
Understanding what women want: eliciting preference for delivery health facility in a rural sub-County in Kenya, a discrete choice experiment
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Measuring the Preferences of Patients With Type II Diabetes Using Best-worst Scaling and Discrete Choice Experiment
ClinicalTrials.gov study NCT02637622. IPD Sharing: Not stated. Countries: 1. Publications: 0.
LAB4SUPPLY -LAB EXPERIMENT - DISCRET CHOICE EXPERIMENT- FIG JAM- SPAIN
<p>Data from the 300 participants of the laboratory study to assess the preferences of fig jam consumers, applying a discrete choice experiment.</p>
Dataset for a discrete choice experiment examining preferences and willingness to pay for health app assessments among healthcare stakeholders
<p>Data collected as part of a discrete choice experiment study which examined the preferences and willingness to pay for health app assessments with different value propositions among health app developers and health system representatives. The following documents are posted on Zenodo:</p> <ul> <li>Codebook for participant background information</li> <li>Participant background information</li> <li>Discrete choice experiment data</li> </ul>
Molecular Analysis for Gastro-Esophageal Cancer: Multicenter Discrete Choice Experiment
ClinicalTrials.gov study NCT06346080. IPD Sharing: YES. Countries: 1. Publications: 0.
PRecision biomArker-Guided MAnagement of TuberculosIs Contacts: a Discrete Choice Experiment
ClinicalTrials.gov study NCT07024836. IPD Sharing: YES. Countries: 1. Publications: 0.
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