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 .
A Closer Look at Decision and Analyst Error by Including Nonlinearities in Discrete Choice Models: Implications on Willingness-to-Pay Estimates Derived from Discrete Choice Data in Healthcare
Background Most research ers in health economics cite r and om utility theory ( RUT ) when analysing discrete choice experiments ( DCEs ) . Under RUT , the error term is associated with the analyst ’s inability to properly capture the true choice processes of the respondent as well as the inconsistency or mistakes arising from the respondent themselves . Under such assumptions , it st and s to reason that analysts should explore more complex nonlinear indirect utility functions , than currently used in healthcare , to strive for better estimates of preferences in healthcare . Objective To test whether complex indirect utility functions decrease error variance for models that either implicitly ( i.e. the multinomial logit ( MNL ) model ) or explicitly ( i.e. entropy multinomial logit ( EMNL ) model ) account for error variance in health(care)-related DCEs ; and to determine the impact of complex indirect utility functions on willingness-to-pay ( WTP ) measures . Methods Using data from DCEs aim ed at healthcare-related decisions , we empirically compared ( 1 ) complex and simple indirect utility specifications in terms of goodness of fit , ( 2 ) their impact on WTP measures , including confidence intervals ( CIs ) based on the Delta method , the Krinsky and Robb-procedure , and Bootstrapping , and ( 3 ) MNL and EMNL model results . Results Complex indirect utility functions had a better model fit than simple specifications ( p < 0.05 ) . WTP estimates were quite similar across alternative specifications . The Delta method produced the most narrow CIs . The EMNL model showed that respondents apply simplifying strategies when answering DCE questions . Conclusion Complex indirect utility functions reduce error arisen from research ers , which can have important implication s for measures in healthcare such as the WTP , whereas EMNL provides insights into the behaviour of respondents when answering DCEs . Underst and ing how respondents answer DCE questions may allow research ers to construct DCEs that minimise scale differences , so that the decision error made across respondents is more homogeneous and therefore taken out as additional noise in the data . Hence , better estimates of preferences in healthcare can be provided
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 .