Using blocked fractional factorial designs to construct discrete choice experiments for healthcare studies.
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ABSTRACT:
SUBMITTER: Jaynes J
PROVIDER: S-EPMC4893005 | biostudies-literature | 2016 Jul
REPOSITORIES: biostudies-literature
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Using blocked fractional factorial designs to construct discrete choice experiments for healthcare studies.
Jaynes Jessica J Wong Weng-Kee WK Xu Hongquan H
Statistics in medicine 20160128 15
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 intera ...[more]
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Project description:Discrete-choice experiments (DCEs) have become a commonly used instrument in health economics and patient-preference analysis, addressing a wide range of policy questions. An important question when setting up a DCE is the size of the sample needed to answer the research question of interest. Although theory exists as to the calculation of sample size requirements for stated choice data, it does not address the issue of minimum sample size requirements in terms of the statistical power of hypothesis tests on the estimated coefficients. The purpose of this paper is threefold: (1) to provide insight into whether and how researchers have dealt with sample size calculations for healthcare-related DCE studies; (2) to introduce and explain the required sample size for parameter estimates in DCEs; and (3) to provide a step-by-step guide for the calculation of the minimum sample size requirements for DCEs in health care.
| S-EPMC4575371 | 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:BackgroundDiscrete choice experiments (DCEs) are increasingly used for health state valuations. However, the values derived from initial DCE studies vary widely. We hypothesize that these findings indicate the presence of unknown sources of bias that must be recognized and minimized. Against this background, we studied whether values derived from a DCE are sensitive to how well the DCE design spans the severity range.MethodsWe constructed an experiment involving three variants of DCE tasks for health state valuation: standard DCE, DCE-death, and DCE-duration. For each type of DCE, an experimental design was generated under two different conditions, enabling a comparison of health state values derived from current best practice Bayesian efficient DCE designs with values derived from 'severity-stratified' designs that control for coverage of the severity range in health state selection. About 3000 respondents participated in the study and were randomly assigned to one of the six study arms.ResultsImposing the severity-stratified restriction had a large effect on health states sampled for the DCE-duration approach. The unstratified efficient design returned a skewed distribution of selected health states, and this introduced bias. The choice probability of bad health states was underestimated, and time trade-offs to avoid bad states were overestimated, resulting in too low values. Imposing the same restriction had limited effect in the DCE-death approach and standard DCE.ConclusionVariation in DCE-derived values can be partially explained by differences in how well selected health states spanned the severity range. Imposing a 'severity stratification' on DCE-duration designs is a validity requirement.
| S-EPMC6182499 | 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:A clinical trial with a 2×2 factorial design involves randomization of subjects to treatment A or A‾ and, within each group, further randomization to treatment B or B‾. Under this design, one can assess the effects of treatments A and B on a clinical endpoint using all patients. One may additionally compare treatment A, treatment B, or combination therapy AB to A‾B‾. With multiple comparisons, however, it may be desirable to control the overall type I error, especially for regulatory purposes. Because the subjects overlap in the comparisons, the test statistics are generally correlated. By accounting for the correlations, one can achieve higher statistical power compared to the conventional Bonferroni correction. Herein, we derive the correlation between any two (stratified or unstratified) log-rank statistics for a 2×2 factorial design with a survival time endpoint, such that the overall type I error for multiple treatment comparisons can be properly controlled. In addition, we allow for adjustment of prognostic factors in the treatment comparisons and conduct simultaneous inference on the effect sizes. We use simulation studies to show that the proposed methods perform well in realistic situations. We then provide an application to a recently completed randomized controlled clinical trial on alcohol dependence. Finally, we discuss extensions of our approach to other factorial designs and multiple endpoints.
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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.
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Project description:We explore what researchers can gain or lose by using three widely used models for the analysis of discrete choice experiment data-the random parameter logit (RPL) with correlated parameters, the RPL with uncorrelated parameters and the hybrid choice model. Specifically, we analyze three data sets focused on measuring preferences to support a renewable energy programme to grow seaweed for biogas production. In spite of the fact that all three models can converge to very similar median WTP values, they cannot be used indistinguishably. Each model is based on different assumptions, which should be tested before their use. The fact that standard sample sizes usually applied in environmental valuation are generally unable to capture the outcome differences between the models cannot be used as a justification for their indistinct application.
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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