Evidence map›Paper›PMID 29696154›Full record

ArticleContemporary clinical trials communications2018

Investigating the impact of design characteristics on statistical efficiency within discrete choice experiments: A systematic survey.

Thuva Vanniyasingam, Caitlin Daly, Xuejing Jin, Yuan Zhang, Gary Foster, Charles Cunningham, Lehana Thabane

Abstract read

In one paragraph

Article in Contemporary clinical trials communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Thuva VanniyasingamDepartment of Health Research Methods, Impact, and Evidence, McMaster University, Hamilton, ON, Canada.

Caitlin DalyDepartment of Health Research Methods, Impact, and Evidence, McMaster University, Hamilton, ON, Canada.

Xuejing JinDepartment of Health Research Methods, Impact, and Evidence, McMaster University, Hamilton, ON, Canada.

Yuan ZhangDepartment of Health Research Methods, Impact, and Evidence, McMaster University, Hamilton, ON, Canada.

Gary FosterDepartment of Health Research Methods, Impact, and Evidence, McMaster University, Hamilton, ON, Canada.

Charles CunninghamDepartment of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.

Lehana ThabaneDepartment of Health Research Methods, Impact, and Evidence, McMaster University, Hamilton, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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. OUTCOMES: Statistical 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.

Indexed as

Discrete choice experimentRelative D-efficiencyRelative D-errorStatistical efficiencySystematic survey

Identifiers

PMID29696154

DOI10.1016/j.conctc.2018.01.002

PMCPMC5898574

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.