Abstract
Background
Discrete choice experiments (DCEs) are increasingly used in health economics to address a wide range of health policy-related concerns.
Objective
Broadly adopting the methodology of an earlier systematic review of health-related DCEs, which covered the period 2001–2008, we report whether earlier trends continued during 2009–2012.
Methods
This paper systematically reviews health-related DCEs published between 2009 and 2012, using the same database as the earlier published review (PubMed) to obtain citations, and the same range of search terms.
Results
A total of 179 health-related DCEs for 2009–2012 met the inclusion criteria for the review. We found a continuing trend towards conducting DCEs across a broader range of countries. However, the trend towards including fewer attributes was reversed, whilst the trend towards interview-based DCEs reversed because of increased computer administration. The trend towards using more flexible econometric models, including mixed logit and latent class, has also continued. Reporting of monetary values has fallen compared with earlier periods, but the proportion of studies estimating trade-offs between health outcomes and experience factors, or valuing outcomes in terms of utility scores, has increased, although use of odds ratios and probabilities has declined. The reassuring trend towards the use of more flexible and appropriate DCE designs and econometric methods has been reinforced by the increased use of qualitative methods to inform DCE processes and results. However, qualitative research methods are being used less often to inform attribute selection, which may make DCEs more susceptible to omitted variable bias if the decision framework is not known prior to the research project.
Conclusions
The use of DCEs in healthcare continues to grow dramatically, as does the scope of applications across an expanding range of countries. There is increasing evidence that more sophisticated approaches to DCE design and analytical techniques are improving the quality of final outputs. That said, recent evidence that the use of qualitative methods to inform attribute selection has declined is of concern.
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Notes
Lower income countries in 2008–2012 included Kenya, South Africa, Thailand, China, Ghana, Vietnam, Ethiopia, Peru, Ukraine, India, Cuba, Nepal, Turkey, and Burkina Faso.
‘Other’ packages used included Gauss for two analyses; nGene (a Bayesian efficient design) for four analyses; and the statistical design procedure Gosset for one analysis; a D-efficient design advocated by Rose and Bliemer for one analysis; STATA for one design; a design described as “an experimental design algorithm optimizing orthogonality, attribute balance, and efficiency” for one design; and Street and Burgess Software for one design.
‘Other’ methods used in 2009–2012 included weighted probit [68]; OLS with a hetero-robust covariance matrix estimator [192]; a method described as “modelling including interaction effects” [45]; Cox’s proportional hazards model with time-dependent covariate [105]; weighted least squares regression to estimate utility weights [105]; multivariate ordered probit to estimate conjoint utility parameters [76]; mixed logit with hierarchical Bayesian modeling and ordered probit [115]; generalized estimated equations [109, 125]; random parameter logit estimated using a hierarchical Bayesian algorithim [208]; conditional logit and parameter weighting functions [160]; a series of multivariate regressions [50, 65]; a method described as Bayesian-like for preference weights [80]; OLS [87]; hierarchical Bayesian analysis [48, 70, 114, 205, 212]; multinomial exploded logit [177]; Firth’s unbiased estimator [193]; combined conditional logit and ranked logit model [127]; multivariate multilevel logistic regression [46]; generalized multinomial logit [119]; mixed effect logistic regression [184], error components mixed logit analysis [63]; a combination of Bayes theorem, Monte Carlo Markov chain procedure and the Metropolis Hastings algorithm [182]; and logistic and probit regression using cluster-robust standard error (SE), random effects and GEE and multinomial logistic and probit regressions with cluster-robust SE and random effects multinomial logistic model and probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes [94].
In 2009–2012, one study explored how changing the number of responses elicited from respondents might affect estimates of WTP [204]; another looked at parents’ preferences for management of attention-deficit hyperactivity disorder [206]; one study looked at general public preferences for long-term care [137]; another two studies looked at preferences for human papillomavirus vaccine, one case looking a societal preferences [207] and the other [63] looking at mothers’ preferences; another study looked at the valuation of diagnostic testing for idiopathic developmental disability by the general population [208]; another looked at various stakeholder groups’ preferences for coagulation factor concentrates to treat hemophilia [145]; one study looked at general public preferences for tele-endocopy services [158]; another compared Dutch and German preferences for health insurance amongst their populations [214]; one paper looked at public and decision maker preferences for pharmaceutical subsidy decisions [215]; one study explored how individuals perceive various coronary heart disease factors [203], whilst another described the relative importance of major adverse cardiac and cerebrovascular events to be used when analyzing trials [212]. Two other DCEs were performed on the area of quality improvement; one investigated how to best disseminate evidence-based practices to addiction service providers and administrators [205], while the other was used to investigate which indicators had the greatest impact on the decisions of health service inspectors concerning the assessment of quality of mental health care [211]. Other applications included a study on preferences of health workers in Burkina Faso for health-insurance payment mechanisms [209]; a study on how respondents valued mortality risk attributable to climate change reductions [210]; and a study on the preferences for reducing contaminated sites to reduce the risk for cancer [213].
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Conflict of interest
Dr. Michael D. Clark: no conflict of interest. Mr. Clark wrote the drafts of the paper, and then took on board feedback from co-authors and peer reviewers in order to further refine it. He will act as overall guarantor for this work. He also evaluated many of the analyses relating to the new review period (2009–2012), and conducted some of the literature searches. Dr. Domino Determann: no conflict of interest. Determann MD reviewed a significant proportion of DCE papers relating to the period 2009–2012, and conducted many of the literature searches. She also provided feedback on early drafts of the paper and suggested some amendments.
Professor Stavros Petrou: no conflict of interest. Professor Petrou supervised this new DCE review from the outset, and commented on drafts of the paper, suggesting edits.
Dr. Domenico Moro: no conflict of interest. Dr. Moro reviewed some papers involving the use of mixed logit or latent class models. He was part of the review team from the onset, and commented on drafts of the paper when appropriate.
Dr. Esther de Bekker-Grob: no conflict of interest. As the first author of a high-profile published review of the DCE literature [3] which reviewed the DCE literature for the period 2001–2008, this co-author helped to ensure consistency with the earlier published review in terms of application of review criteria. She also commented on drafts of the paper, and made some valuable contributions to the points raised by the paper in Sects. 9 and 10.
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Clark, M.D., Determann, D., Petrou, S. et al. Discrete Choice Experiments in Health Economics: A Review of the Literature. PharmacoEconomics 32, 883–902 (2014). https://doi.org/10.1007/s40273-014-0170-x
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DOI: https://doi.org/10.1007/s40273-014-0170-x