Dataset Information

Mode and Frame Matter: Assessing the Impact of Survey Mode and Sample Frame in Choice Experiments.

ABSTRACT: Background. Choice experiments (CE) are applied in health economics to elicit public preferences and willingness to pay (WTP). CEs are frequently administered as Internet-based surveys. Internet surveys have recognized advantages, but concerns exist about the representativeness of Internet samples, data quality, and the impact on elicited values. Aim. We conducted the first study in health comparing an Internet-based CE survey with the more traditional general population mail survey. We also compared the Internet-based and mail CE surveys with computer-assisted personal interviews (CAPIs), which are commonly used to elicit health state valuations. Methods. Two separate samples were drawn from 2 United Kingdom (UK) volunteer Internet panels (IPs), CAPIs were undertaken with respondents sampled from UK Census Output Areas, and mail surveys were sent to UK households drawn from the postcode address file (PAF). Each mode received more than 1000 respondents. We compared modes and frames using objective measures (response rate, sample representativeness of the UK population, elicited values, theoretical validity, and cost per response) and subjective/self-reported measures (time taken to complete the study, perceived study consequentiality, and stated attribute nonattendance). This study intentionally confounded the survey modes and sample frame by choosing sample frames that are typically used by researchers for each mode. Results. Estimated WTP differs across mode-frame pairs. On most measures, CAPIs dominated. They are more expensive, however. On all measures, except response rates, Internet surveys dominated the mail survey. They were also cheaper. Conclusion. Researchers using IPs should pay attention to response rates and be aware that the quality of IPs differs. Given the importance of perceived consequentiality and attribute attendance in CEs, future research should address their impact across modes and frames.

SUBMITTER: Watson V

PROVIDER: S-EPMC6843610 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Mode and Frame Matter: Assessing the Impact of Survey Mode and Sample Frame in Choice Experiments.

Watson Verity V Porteous Terry T Bolt Tim T Ryan Mandy M

Medical decision making : an international journal of the Society for Medical Decision Making 20190915 7

<b>Background.</b> Choice experiments (CE) are applied in health economics to elicit public preferences and willingness to pay (WTP). CEs are frequently administered as Internet-based surveys. Internet surveys have recognized advantages, but concerns exist about the representativeness of Internet samples, data quality, and the impact on elicited values. <b>Aim.</b> We conducted the first study in health comparing an Internet-based CE survey with the more traditional general population mail surve ...[more]

PMID: 31524051

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Project description:ObjectivesThis study reviews simulation studies of discrete choice experiments to determine (i) how survey design features affect statistical efficiency, (ii) and to appraise their reporting quality.OutcomesStatistical efficiency was measured using relative design (D-) efficiency, D-optimality, or D-error.MethodsFor this systematic survey, we searched Journal Storage (JSTOR), Since Direct, PubMed, and OVID which included a search within EMBASE. Searches were conducted up to year 2016 for simulation studies investigating the impact of DCE design features on statistical efficiency. Studies were screened and data were extracted independently and in duplicate. Results for each included study were summarized by design characteristic. Previously developed criteria for reporting quality of simulation studies were also adapted and applied to each included study.ResultsOf 371 potentially relevant studies, 9 were found to be eligible, with several varying in study objectives. Statistical efficiency improved when increasing the number of choice tasks or alternatives; decreasing the number of attributes, attribute levels; using an unrestricted continuous "manipulator" attribute; using model-based approaches with covariates incorporating response behaviour; using sampling approaches that incorporate previous knowledge of response behaviour; incorporating heterogeneity in a model-based design; correctly specifying Bayesian priors; minimizing parameter prior variances; and using an appropriate method to create the DCE design for the research question. The simulation studies performed well in terms of reporting quality. Improvement is needed in regards to clearly specifying study objectives, number of failures, random number generators, starting seeds, and the software used.ConclusionThese results identify the best approaches to structure a DCE. An investigator can manipulate design characteristics to help reduce response burden and increase statistical efficiency. Since studies varied in their objectives, conclusions were made on several design characteristics, however, the validity of each conclusion was limited. Further research should be conducted to explore all conclusions in various design settings and scenarios. Additional reviews to explore other statistical efficiency outcomes and databases can also be performed to enhance the conclusions identified from this review.

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:BackgroundGenome annotation is a crucial component of RNA-seq data analysis. Much effort has been devoted to producing an accurate and rational annotation of the human genome. An annotated genome provides a comprehensive catalogue of genomic functional elements. Currently, at least six human genome annotations are publicly available, including AceView Genes, Ensembl Genes, H-InvDB Genes, RefSeq Genes, UCSC Known Genes, and Vega Genes. Characteristics of these annotations differ because of variations in annotation strategies and information sources. When performing RNA-seq data analysis, researchers need to choose a genome annotation. However, the effect of genome annotation choice on downstream RNA-seq expression estimates is still unclear. This study (1) investigates the effect of different genome annotations on RNA-seq quantification and (2) provides guidelines for choosing a genome annotation based on research focus.ResultsWe define the complexity of human genome annotations in terms of the number of genes, isoforms, and exons. This definition facilitates an investigation of potential relationships between complexity and variations in RNA-seq quantification. We apply several evaluation metrics to demonstrate the impact of genome annotation choice on RNA-seq expression estimates. In the mapping stage, the least complex genome annotation, RefSeq Genes, appears to have the highest percentage of uniquely mapped short sequence reads. In the quantification stage, RefSeq Genes results in the most stable expression estimates in terms of the average coefficient of variation over all genes. Stable expression estimates in the quantification stage translate to accurate statistics for detecting differentially expressed genes. We observe that RefSeq Genes produces the most accurate fold-change measures with respect to a ground truth of RT-qPCR gene expression estimates.ConclusionsBased on the observed variations in the mapping, quantification, and differential expression calling stages, we demonstrate that the selection of human genome annotation results in different gene expression estimates. When conducting research that emphasizes reproducible and robust gene expression estimates, a less complex genome annotation may be preferred. However, simpler genome annotations may limit opportunities for identifying or characterizing novel transcriptional or regulatory mechanisms. When conducting research that aims to be more exploratory, a more complex genome annotation may be preferred.

Dataset Information

Mode and Frame Matter: Assessing the Impact of Survey Mode and Sample Frame in Choice Experiments.

Publications

Mode and Frame Matter: Assessing the Impact of Survey Mode and Sample Frame in Choice Experiments.

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