Dataset Information

The Role of Qualitative Research Methods in Discrete Choice Experiments.

ABSTRACT:

Background

The use of qualitative research (QR) methods is recommended as good practice in discrete choice experiments (DCEs). This study investigated the use and reporting of QR to inform the design and/or interpretation of healthcare-related DCEs and explored the perceived usefulness of such methods.

Methods

DCEs were identified from a systematic search of the MEDLINE database. Studies were classified by the quantity of QR reported (none, basic, or extensive). Authors ( n = 91) of papers reporting the use of QR were invited to complete an online survey eliciting their views about using the methods.

Results

A total of 254 healthcare DCEs were included in the review; of these, 111 (44%) did not report using any qualitative methods; 114 (45%) reported "basic" information; and 29 (11%) reported or cited "extensive" use of qualitative methods. Studies reporting the use of qualitative methods used them to select attributes and/or levels ( n = 95; 66%) and/or pilot the DCE survey ( n = 26; 18%). Popular qualitative methods included focus groups ( n = 63; 44%) and interviews ( n = 109; 76%). Forty-four studies (31%) reported the analytical approach, with content ( n = 10; 7%) and framework analysis ( n = 5; 4%) most commonly reported. The survey identified that all responding authors ( n = 50; 100%) found that qualitative methods added value to their DCE study, but many ( n = 22; 44%) reported that journals were uninterested in the reporting of QR results.

Conclusions

Despite recommendations that QR methods be used alongside DCEs, the use of QR methods is not consistently reported. The lack of reporting risks the inference that QR methods are of little use in DCE research, contradicting practitioners' assessments. Explicit guidelines would enable more clarity and consistency in reporting, and journals should facilitate such reporting via online supplementary materials.

SUBMITTER: Vass C

PROVIDER: S-EPMC5367554 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Publications

The Role of Qualitative Research Methods in Discrete Choice Experiments.

Vass Caroline C Rigby Dan D Payne Katherine K

Medical decision making : an international journal of the Society for Medical Decision Making 20170106 3

<h4>Background</h4>The use of qualitative research (QR) methods is recommended as good practice in discrete choice experiments (DCEs). This study investigated the use and reporting of QR to inform the design and/or interpretation of healthcare-related DCEs and explored the perceived usefulness of such methods.<h4>Methods</h4>DCEs were identified from a systematic search of the MEDLINE database. Studies were classified by the quantity of QR reported (none, basic, or extensive). Authors ( n = 91) ...[more]

PMID: 28061040

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Project description:IntroductionDespite the recognised importance of participant understanding for valid and reliable discrete choice experiment (DCE) results, there has been limited assessment of whether, and how, people understand DCEs, and how 'understanding' is conceptualised in DCEs applied to a health context.ObjectivesOur aim was to identify how participant understanding is conceptualised in the DCE literature in a health context. Our research questions addressed how participant understanding is defined, measured, and used.MethodsSearches were conducted (June 2019) in the MEDLINE, EMBASE, PsychINFO and Econlit databases, as well as hand searching. Search terms were based on previous DCE systematic reviews, with additional understanding keywords used in a proximity-based search strategy. Eligible studies were peer-reviewed journal articles in the field of health, related to DCE or best-worst scaling type 3 (BWS3) studies, and reporting some consideration or assessment of participant understanding. A descriptive analytical approach was used to chart relevant data from each study, including publication year, country, clinical area, subject group, sample size, study design, numbers of attributes, levels and choice sets, definition of understanding, how understanding was tested, results of the understanding tests, and how the information about understanding was used. Each study was categorised based on how understanding was conceptualised and used within the study.ResultsOf 306 potentially eligible articles identified, 31 were excluded based on titles and abstracts, and 200 were excluded on full-text review, resulting in 75 included studies. Three categories of study were identified: applied DCEs (n = 52), pretesting studies (n = 7) and studies of understanding (n = 16). Typically, understanding was defined in relation to either the choice context, such as attribute terminology, or the concept of choosing. Very few studies considered respondents' engagement as a component of understanding. Understanding was measured primarily through qualitative pretesting, rationality or validity tests included in the survey, and participant self-report, however reporting and use of the results of these methods was inconsistent.ConclusionsThose conducting or using health DCEs should carefully select, justify, and report the measurement and potential impact of participant understanding in their specific choice context. There remains scope for research into the different components of participant understanding, particularly related to engagement, the impact of participant understanding on DCE validity and reliability, the best measures of understanding, and methods to maximise participant understanding.

Project description:Several disciplines, among them health, sociology, and economics, provide strong evidence that social context is important to individual choices. It is therefore surprising that relatively little research has been focused on integrating the effect of social influence into choice models, especially given the importance of such choices in healthcare. This study developed and empirically tested a choice model that accounts for social network influences in a discrete choice experiment (DCE). We focused on maternal choices for childhood vaccination in Australia, and used an econometric choice model that explicitly 1) incorporated vaccine schedule characteristics, benefits and costs, and 2) represented up to ten different identifiable key influencer types (e.g., partner, parents, friends, healthcare professionals, inter alia), allowing for the attribution of directional importance of each influencer on the gravid woman's decision to adhere to or reject childhood vaccination. Pregnant women (N = 604) aged 18 years and older recruited from an online panel completed a survey, including a DCE and questions about key influencers. A two-class ordered latent class model was conducted to analyse the DCE data, which assumes that the underlying latent driver (in our case the WHO vaccine hesitancy scale) is ordered, to give a practical interpretation of the meaning of the classes. When the choice model considered both childhood vaccination attributes and key influencers, a very high model fit was reached. The impact of key influencers on maternal choice for childhood vaccination was massive compared to the impact of childhood vaccination attributes. The marginal impact differed between key influencers. Our DCE study showed that the maternal decision for childhood vaccination was essentially almost completely socially driven, suggesting that the potential impact of social network influences can and should be considered in health-related DCEs, particular those where there are likely to be strong underlying social norms dictating decision maker behaviour.

Project description:BackgroundVaccination can be viewed as comprising the most important defensive barriers to protect susceptible groups from infection. However, vaccine hesitancy for COVID-19 is widespread worldwide.ObjectiveWe aimed to systematically review studies eliciting the COVID-19 vaccine preference using discrete choice experiments.MethodsA literature search was conducted in PubMed, Embase, Web of Science, Scopus, and CINAHL Plus platforms in April 2023. Search terms included discrete choice experiments, COVID-19, and vaccines and related synonyms. Descriptive statistics were used to summarize the study characteristics. Subgroup analyses were performed by factors such as high-income countries and low- and middle-income countries and study period (before, during, and after the pandemic wave). Quality appraisal was performed using the 5-item Purpose, Respondents, Explanation, Findings, and Significance checklist.ResultsThe search yield a total of 623 records, and 47 studies with 53 data points were finally included. Attributes were grouped into 4 categories: outcome, process, cost, and others. The vaccine effectiveness (21/53, 40%) and safety (7/53, 13%) were the most frequently reported and important attributes. Subgroup analyses showed that vaccine effectiveness was the most important attribute, although the preference varied by subgroups. Compared to high-income countries (3/29, 10%), a higher proportion of low- and middle-income countries (4/24, 17%) prioritized safety. As the pandemic progressed, the duration of protection (2/24, 8%) during the pandemic wave and COVID-19 mortality risk (5/25, 20%) after the pandemic wave emerged as 2 of the most important attributes.ConclusionsOur review revealed the critical role of vaccine effectiveness and safety in COVID-19 vaccine preference. However, it should be noticed that preference heterogeneity was observed across subpopulations and may change over time.Trial registrationPROSPERO CRD42023422720; https://tinyurl.com/2etf7ny7.

Dataset Information

The Role of Qualitative Research Methods in Discrete Choice Experiments.

Background

Methods

Results

Conclusions

Publications

The Role of Qualitative Research Methods in Discrete Choice Experiments.

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