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 design s 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 .
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 .
An empirical comparison of methods for analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening
Background A discrete choice experiment ( DCE ) is a preference survey which asks participants to make a choice among product portfolios comparing the key product characteristics by performing several choice tasks . Analyzing DCE data needs to account for within-participant correlation because choices from the same participant are likely to be similar . In this study , we empirically compared some commonly-used statistical methods for analyzing DCE data while accounting for within-participant correlation based on a survey of patient preference for colorectal cancer ( CRC ) screening tests conducted in Hamilton , Ontario , Canada in 2002 . Methods A two-stage DCE design was used to investigate the impact of six attributes on participants ' preferences for CRC screening test  and willingness to undertake the test . We compared six models for clustered binary outcomes ( logistic and probit regressions using cluster-robust st and ard error ( SE ) , r and om-effects and generalized estimating equation approaches ) and three models for clustered nominal outcomes ( multinomial logistic and probit regressions with cluster-robust SE and r and om-effects multinomial logistic model ) . We also fitted a bivariate probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes . The rank of relative importance between attributes and the estimates of βcoefficient within attributes were used to assess the model robustness . Results In total 468 participants with each completing 10 choices were analyzed . Similar results were reported for the rank of relative importance and βcoefficients across models for stage-one data on evaluating participants ' preferences for the test . The six attributes ranked from high to low as follows : cost , specificity , process , sensitivity , preparation and pain . However , the results differed across models for stage-two data on evaluating participants ' willingness to undertake the tests . Little within-patient correlation ( ICC ≈ 0 ) was found in stage-one data , but substantial within-patient correlation existed ( ICC = 0.659 ) in stage-two data . Conclusions When small clustering effect presented in DCE data , results remained robust across statistical models . However , results varied when larger clustering effect presented . Therefore , it is important to assess the robustness of the estimates via sensitivity analysis using different models for analyzing clustered data from DCE studies
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 design s 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 .
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 .