Evidence map›Paper›PMID 27436671›Full record

ArticleBMJ open2016

Simulation study to determine the impact of different design features on design efficiency in discrete choice experiments.

Thuva Vanniyasingam, Charles E Cunningham, Gary Foster, Lehana Thabane

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Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
–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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Discrete choice experiments to generate utility values for multi-attribute utility instruments: a systematic review of methods.The European journal of health economics : HEPAC : health economics in prevention and care · 2020
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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

4 authors.

Thuva VanniyasingamDepartment of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada.
Charles E CunninghamDepartment of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada.
Gary FosterDepartment of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada Biostatistics Unit, Father Sean O'Sullivan Research Centre, St. Joseph's Healthcare, Hamilton, Ontario, Canada.
Lehana ThabaneDepartment of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada Biostatistics Unit, Father Sean O'Sullivan Research Centre, St. Joseph's Healthcare, Hamilton, Ontario, Canada Departments of Paediatrics and Anaesthesia, McMaster University, Hamilton, Ontario, Canada Centre for Evaluation of Medicine, St. Joseph's Healthcare, Hamilton, Ontario, Canada Population Health Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesDiscrete choice experiments (DCEs) are routinely used to elicit patient preferences to improve health outcomes and healthcare services. While many fractional factorial designs can be created, some are more statistically optimal than others. The objective of this simulation study was to investigate how varying the number of (1) attributes, (2) levels within attributes, (3) alternatives and (4) choice tasks per survey will improve or compromise the statistical efficiency of an experimental design. DESIGN AND

methodsA total of 3204 DCE designs were created to assess how relative design efficiency (d-efficiency) is influenced by varying the number of choice tasks (2-20), alternatives (2-5), attributes (2-20) and attribute levels (2-5) of a design. Choice tasks were created by randomly allocating attribute and attribute level combinations into alternatives. OUTCOME: Relative d-efficiency was used to measure the optimality of each DCE design.

resultsDCE design complexity influenced statistical efficiency. Across all designs, relative d-efficiency decreased as the number of attributes and attribute levels increased. It increased for designs with more alternatives. Lastly, relative d-efficiency converges as the number of choice tasks increases, where convergence may not be at 100% statistical optimality.

conclusionsAchieving 100% d-efficiency is heavily dependent on the number of attributes, attribute levels, choice tasks and alternatives. Further exploration of overlaps and block sizes are needed. This study's results are widely applicable for researchers interested in creating optimal DCE designs to elicit individual preferences on health services, programmes, policies and products.

Indexed as

Choice BehaviorPatient PreferenceResearch DesignSurveys and QuestionnairesHumansconjoint analysisdesign efficiencydiscrete choice experimentpatient preferences

Identifiers

PMID27436671
PMCPMC4964187

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LicenceCC BY-NC
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Registered trials

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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.