Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide.
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SUBMITTER: de Bekker-Grob EW
PROVIDER: S-EPMC4575371 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Although sample size calculations play an essential role in health research, published research often fails to report sample size selection. This study aims to explain the importance of sample size calculation and to provide considerations for determining sample size in a simplified manner. Approaches to sample size calculation according to study design are presented with examples in health research. For sample size estimation, researchers need to (1) provide information regarding the statistical analysis to be applied, (2) determine acceptable precision levels, (3) decide on study power, (4) specify the confidence level, and (5) determine the magnitude of practical significance differences (effect size). Most importantly, research team members need to engage in an open and realistic dialog on the appropriateness of the calculated sample size for the research question(s), available data records, research timeline, and cost. This study aims to further inform researchers and health practitioners interested in quantitative research, so as to improve their knowledge of sample size calculation.
| S-EPMC10000262 | biostudies-literature
Project description:Discrete choice experiments (DCEs) are increasingly used for studying and quantifying subjects preferences in a wide variety of healthcare applications. They provide a rich source of data to assess real-life decision-making processes, which involve trade-offs between desirable characteristics pertaining to health and healthcare and identification of key attributes affecting healthcare. The choice of the design for a DCE is critical because it determines which attributes' effects and their interactions are identifiable. We apply blocked fractional factorial designs to construct DCEs and address some identification issues by utilizing the known structure of blocked fractional factorial designs. Our design techniques can be applied to several situations including DCEs where attributes have different number of levels. We demonstrate our design methodology using two healthcare studies to evaluate (i) asthma patients' preferences for symptom-based outcome measures and (ii) patient preference for breast screening services. Copyright © 2016 John Wiley & Sons, Ltd.
| S-EPMC4893005 | biostudies-literature
Project description:IntroductionDiscrete choice experiments (DCEs) have been used to measure patient and healthcare professionals preferences in a range of settings internationally. Using DCEs in primary care is valuable for determining how to improve rational shared decision making. The purpose of this systematic review is to assess the validity of the methods used for DCEs assessing the decision making of healthcare professionals in primary care.Main bodyA systematic search was conducted to identify articles with original data from a discrete choice experiment where the population was primary healthcare professionals. All publication dates from database inception to 29th February 2020 were included. A data extraction and validity assessment template based on guidelines was used. After screening, 34 studies met the eligibility criteria and were included in the systematic review. The sample sizes of the DCEs ranged from 10 to 3727. The published DCEs often provided insufficient detail about the process of determining the attributes and levels. The majority of the studies did not involve primary care healthcare professionals outside of the research team in attribute identification and selection. Less than 80% of the DCEs were piloted and few papers investigated internal or external validity.ConclusionsFor findings to translate into improvements in rational shared decision making in primary care DCEs need to be internally and externally valid and the findings need to be able to be communicated to stakeholders in a way that is understandable and relevant.
| S-EPMC7725112 | biostudies-literature
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.
| S-EPMC7794102 | biostudies-literature
Project description:ObjectivesDiscrete choice experiments (DCEs) are increasingly advocated as a way to quantify preferences for health. However, increasing support does not necessarily result in increasing quality. Although specific reviews have been conducted in certain contexts, there exists no recent description of the general state of the science of health-related DCEs. The aim of this paper was to update prior reviews (1990-2012), to identify all health-related DCEs and to provide a description of trends, current practice and future challenges.MethodsA systematic literature review was conducted to identify health-related empirical DCEs published between 2013 and 2017. The search strategy and data extraction replicated prior reviews to allow the reporting of trends, although additional extraction fields were incorporated.ResultsOf the 7877 abstracts generated, 301 studies met the inclusion criteria and underwent data extraction. In general, the total number of DCEs per year continued to increase, with broader areas of application and increased geographic scope. Studies reported using more sophisticated designs (e.g. D-efficient) with associated software (e.g. Ngene). The trend towards using more sophisticated econometric models also continued. However, many studies presented sophisticated methods with insufficient detail. Qualitative research methods continued to be a popular approach for identifying attributes and levels.ConclusionsThe use of empirical DCEs in health economics continues to grow. However, inadequate reporting of methodological details inhibits quality assessment. This may reduce decision-makers' confidence in results and their ability to act on the findings. How and when to integrate health-related DCE outcomes into decision-making remains an important area for future research.
| S-EPMC6386055 | biostudies-literature
Project description:BackgroundThe 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.MethodsDCEs 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.ResultsA 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.ConclusionsDespite 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.
| S-EPMC5367554 | biostudies-literature
Project description:BackgroundComplex health interventions must incorporate user preferences to maximize their potential effectiveness. Discrete choice experiments (DCEs) quantify the strength of user preferences and identify preference heterogeneity across users. We present the process of using a DCE to supplement conventional qualitative formative research in the design of a demand creation intervention for voluntary medical male circumcision (VMMC) to prevent HIV in Tanzania.MethodsThe VMMC intervention was designed within a 3-month formative phase. In-depth interviews (n = 30) and participatory group discussions (n = 20) sought to identify broad setting-specific barriers to and facilitators of VMMC among adult men. Qualitative results informed the DCE development, identifying the role of female partners, service providers' attitudes and social stigma. A DCE among 325 men in Njombe and Tabora, Tanzania, subsequently measured preferences for modifiable VMMC service characteristics. The final VMMC demand creation intervention design drew jointly on the qualitative and DCE findings.ResultsWhile the qualitative research informed the community mobilization intervention, the DCE guided the specific VMMC service configuration. The significant positive utilities (u) for availability of partner counselling (u = 0.43, p < 0.01) and age-separated waiting areas (u = 0.21, p < 0.05) led to the provision of community information booths for partners and provision of age-separated waiting areas. The strong disutility of female healthcare providers (u = - 0.24, p < 0.01) led to re-training all providers on client-friendliness.ConclusionThis is, to our knowledge, the first study documenting how user preferences from DCEs can directly inform the design of a complex intervention. The use of DCEs as formative research may help increase user uptake and adherence to complex interventions.
| S-EPMC6399844 | biostudies-literature
Project description:BACKGROUND:Published formulas for case-control designs provide sample sizes required to determine that a given disease-exposure odds ratio is significantly different from one, adjusting for a potential confounder and possible interaction. RESULTS:The formulas are extended from one control per case to F controls per case and adjusted for a potential multi-category confounder in unmatched or matched designs. Interactive FORTRAN programs are described which compute the formulas. The effect of potential disease-exposure-confounder interaction may be explored. CONCLUSIONS:Software is now available for computing adjusted sample sizes for case-control designs.
| S-EPMC61037 | biostudies-literature
Project description:Results of a comprehensive simulation study are reported investigating the effects of sample size, test length, number of attributes and base rate of mastery on item parameter recovery and classification accuracy of four DCMs (i.e., C-RUM, DINA, DINO, and LCDMREDUCED). Effects were evaluated using bias and RMSE computed between true (i.e., generating) parameters and estimated parameters. Effects of simulated factors on attribute assignment were also evaluated using the percentage of classification accuracy. More precise estimates of item parameters were obtained with larger sample size and longer test length. Recovery of item parameters decreased as the number of attributes increased from three to five but base rate of mastery had a varying effect on the item recovery. Item parameter and classification accuracy were higher for DINA and DINO models.
| S-EPMC7868330 | biostudies-literature
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.
| S-EPMC9581381 | biostudies-literature