Deleting 'irrational' responses from discrete choice experiments: a case of investigating or imposing preferences? Understanding the impact of economic evidence on clinical decision making: a discrete choice experiment in cardiology. An empirical comparison of methods for analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening Valuing SF-6D Health States Using a Discrete Choice Experiment Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. 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 Does attribute framing in discrete choice experiments influence willingness to pay? Results from a discrete choice experiment in screening for colorectal cancer. Eliciting public preference for health-care resource allocation in South Korea. Eliciting health state utilities for Dupuytren’s contracture using a discrete choice experiment General practitioners' preferences for the organisation of primary care: a discrete choice experiment. Use of a decision aid to help caregivers discuss terminal disease status with a family member with cancer: a randomized controlled trial. Presenting risk information to people with diabetes: evaluating effects and preferences for different formats by a web-based randomised controlled trial. Evaluating a complex system-wide intervention using the difference in differences method: the Delivering Choice Programme Does the Inclusion of a Cost Attribute Result in Different Preferences for the Surgical Treatment of Primary Basal Cell Carcinoma? Citizen Participation in Patient Prioritization Policy Decisions: An Empirical and Experimental Study on Patients' Characteristics Random Regret-Based Discrete-Choice Modelling: An Application to Healthcare Improving decision making about clinical trial participation – a randomised controlled trial of a decision aid for women considering participation in the IBIS-II breast cancer prevention trial
Investigation of the ' rationality ' of responses to discrete choice experiments ( DCEs ) has been a theme of research in health economics . Responses have been deleted from DCEs where they have been deemed by research ers to ( a ) be ' irrational ' , defined by such studies as failing tests for non-satiation , or ( b ) represent lexicographic preferences . This paper outlines a number of reasons why deleting responses from DCEs may be inappropriate after first review ing the theory underpinning rationality , highlighting that the importance placed on rationality depends on the approach to consumer theory to which one ascribes . The aim of this paper is not to suggest that all preferences elicited via DCEs are rational . Instead , it is to suggest a number of reasons why it may not be the case that all preferences labelled as ' irrational ' are indeed so . Hence , deleting responses may result in the removal of valid preferences ; induce sample selection bias ; and reduce the statistical efficiency and power of the estimated choice models . Further , evidence suggests r and om utility theory may be able to cope with such preferences . Finally , we discuss a number of implication s for the design , implementation and interpretation of DCEs and recommend caution regarding the deletion of preferences from stated preference experiments The present study aims to evaluate the impact of  cost-effectiveness information  on clinical decision making using discrete choice experiment ( DCE ) methodology . Data were collected through a self-completed question naire  administered to Italian cardiologists in June 2007 ( n = 129 respondents , 1143 observations ) . The question naire asked clinicians to make choices between paired scenarios , across which three key dimensions were identified and varied : ( 1 ) quality of clinical evidence , ( 2 ) size of health gain ( reduction of relative and absolute risk ) , and ( 3 ) economic impact ( incremental cost-effectiveness ratio ) . A r and om effects probit model was used to estimate clinicians ' preferences for the different dimensions , while the heterogeneity of preferences was tested in a model with interaction terms . Dominance tests were used to assess the consistency of responses . The results indicate that Italian cardiologists regard economic impact ( cost-effectiveness ) as an important factor in their decision making . Economic evidence is valued more highly among clinicians with a higher self-assessed level of knowledge regarding economic evaluation techniques , as well as among younger professionals ( age<45 ) . While relevant study limitations should be acknowledged , our results suggest that DCEs can be used to elicit clinicians ' decision-making criteria and to inform the allocation of re sources for future research in a logical manner . Italian cardiologists appear to take  cost-effectiveness information into account when deciding whether to use new treatments 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 Background . SF-6D utility weights are conventionally produced using a st and ard gamble ( SG ) . SG-derived weights consistently demonstrate a floor effect not observed with other elicitation techniques . Recent advances in discrete choice methods have allowed estimation of utility weights . The objective was to produce Australian utility weights for the SF-6D and to explore the application of discrete choice experiment ( DCE ) methods in this context . We hypothesized that weights derived using this method would reflect the largely monotonic construction of the SF-6D . Methods . We design ed an online DCE and administered it to an Australia-representative online panel ( n = 1017 ) . A range of specifications investigating nonlinear preferences with respect to additional life expectancy were estimated using a r and om-effects probit model . The preferred model was then used to estimate a preference index such that full health and death were valued at 1 and 0 , respectively , to provide an algorithm for Australian cost-utility analyses . Results . Physical functioning , pain , mental health , and vitality were the largest drivers of utility weights . Combining levels to remove illogical orderings did not lead to a poorer model fit . Relative to international SG-derived weights , the range of utility weights was larger with 5 % of health states valued below zero . Conclusion s. DCEs can be used to investigate preferences for health profiles and to estimate utility weights for multi-attribute utility instruments . Australian cost-utility analyses can now use domestic SF-6D weights . The comparability of DCE results to those using other elicitation methods for estimating utility weights for quality -adjusted life-year calculations should be further investigated Attribute generation for  discrete choice experiments ( DCEs ) is often poorly reported , and it is unclear whether this element of research is conducted rigorously . This paper explores issues associated with developing attributes for DCEs  and contrasts different qualitative approaches . The paper draws on eight studies , four developed attributes for measures , and four developed attributes  for more ad hoc policy questions . Issues that have become apparent through these studies include the following : the theoretical framework for r and om utility theory and the need for attributes that are neither too close to the latent construct nor too intrinsic to people 's personality ; the need to think about attribute development as a two-stage process involving conceptual development followed by refinement of language to convey the intended meaning ; and the difficulty in resolving tensions inherent in the reductiveness of condensing complex and nuanced qualitative findings into precise terms . The comparison of alternative qualitative approaches suggests that the nature of data collection will depend both on the characteristics of the question ( its sensitivity , for example ) and the availability of existing qualitative information . An iterative , constant comparative approach to analysis is recommended . Finally , the paper provides a series of recommendations for improving the reporting of this element of DCE studies 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 OBJECTIVE Recent review s of discrete choice methodology identified method ological issues warranting further exploration , including the issue of " framing . " The objective of this study was to conduct a method ological exploration of the effect of attribute framing on marginal rates of substitution ( MRS ) , including willingness to pay ( WTP ) from a discrete choice experiment ( DCE ) , within the context of colorectal cancer screening preferences . METHODS The survey , a fractional factorial design of a two-alternative , unlabeled experiment , was mailed to a sample of 1920 subjects in NSW , Australia . Participants were r and omized to one of four alternative " frames " of information . Attributes included : accuracy of the test for finding cancers , accuracy of the test for finding large polyps , how good the test is at saying you do n't have cancer , cost , dietary and medication restrictions and sample collection . A mixed logit model was used to estimate preferences ; MRS between attributes , including WTP , was calculated . RESULTS A total of 1157 surveys from 1920 ( 60.2 % ) were returned . Accuracy of the test for finding cancer was most likely to influence choice of test , followed by accuracy of the test for finding large polyps . Under some circumstances , framing of the attributes ( e.g. , cancers found vs. cancers missed ) influenced the relative importance of attributes . Attribute framing significantly influenced estimates of WTP , and benefit : harm trade-offs that were calculated from MRS . CONCLUSIONS Attribute framing can influence willingness to pay and benefit : harm trade-offs from DCEs . Appropriate design and analysis methods should be explored to further characterize the influence and extent of framing in discrete choice studies OBJECTIVES To identify the principles the public considers important and the trade-offs between different values in health-care re source allocation practice s. METHODS This study approached the issue in both qualitative and quantitative ways . In a qualitative study , two focus groups discussed the issues of re source allocation in health care . To facilitate the discussion , a simple ranking task and a series of pairwise choice practice s were implemented . A discrete choice experiment survey question naire was also administered to a sample of the general population . Attributes and levels were determined through literature review s and the results from the focus group interview . We used a r and om-effect probit model to assess the effects of each attribute . RESULTS Through the focus group interviews , we found strong public support for the principle of equal opportunity . The participants thought that the severity of disease was the most important criterion when setting priorities . The majority supported the idea that the most disadvantaged should have the highest priority even when their health gains are less than those of others . The discrete choice experiment results showed that the severity of disease , health gains , and patients ' socioeconomic status significantly influence their choices , with each parameter having an expected sign . CONCLUSION The results showed that Koreans support not only health maximization but also equal opportunity , fair re source allocation , and e quality Background and purpose An internet-based discrete choice experiment ( DCE ) was conducted to elicit preferences for a wide range of Dupuytren ’s contracture (DC)-related health states . An algorithm was subsequently developed to convert these preferences into health state utilities that can be used to assess DC ’s impact on quality of life and the value of its treatments . Methods Health state preferences for varying levels of DC h and severity were elicited via an internet survey from a sample of the UK adult population . Severity levels were defined using a combination of contractures ( 0 , 45 , or 90 degrees ) in 8 proximal interphalangeal and metacarpophalangeal joints of the index , middle , ring , and little fingers . Right-h and ed , left-h and ed , and ambidextrous respondents indicated which h and was preferable in each of the 10 r and omly-selected h and -pairings comparing different DC severity levels . For consistency across comparisons , anatomically precise digital h and drawings were used . To anchor preferences onto the traditional 0–1 utility scale used in health economic evaluations , unaffected h and s were assigned a utility of 1.0 whereas the utility for a maximally affected h and ( i.e. , all 8 joints set at 90 degrees of contracture ) was derived by asking respondents to indicate what combination of attributes and levels of the EQ-5D-5L profile most accurately reflects the impact of living with such h and . Conditional logistic models were used to estimate indirect utilities , then rescaled to the anchor points on the EQ-5D-5L . Results Estimated utilities based on the responses of 1,745 qualified respondents were 0.49 , 0.57 , and 0.63 for completely affected dominant h and s , non-dominant h and s , or ambidextrous h and s , respectively . Utility for a dominant h and with 90-degree contracture in t h e metacarpophalangeal joints of the ring and little fingers was estimated to be 0.89 . Separately , reducing the contracture of metacarpophalangeal joint for a little finger from 50 to 12 degrees would improve utility by 0.02 . Interpretation DC is associated with substantial utility decre- ments . The algorithms presented herein provide a robust and flexible framework to assess utility for varying degrees of DC severity OBJECTIVES To examine GPs ' preferences for organisational characteristics in general practice with focus on aspects that can potentially mitigate problems with GP shortages . METHODS A simple r and om sample of 1823 GPs ( corresponding to half of all GPs in Denmark ) was drawn at the beginning of 2010 , and a response rate of 68 % was obtained . A discrete choice experiment ( DCE ) is applied , and attributes included are :  practice type ( solo/shared ) , number of GPs in general practice , collaboration with other practice s ( yes/no ) , change in weekly working hours ( administrative versus patient related ) and change in yearly surplus . Multinomial logit analyses ( with and without interaction variables ) are used , and marginal rates of substitution are calculated . RESULTS GPs working in solo practice s have different preferences for the organisational attributes compared to GPs in shared practice s. The compensation needed for GPs to re-organise from solo to shared practice is associated with the size of the practice . GP characteristics such as age , working hours and surplus affect their willingness to undergo organisational changes . CONCLUSIONS Our results are of relevance to decision makers in design ing policies aim ed at influencing GPs ' organisation in order to overcome problems related to shortages PURPOSE We tested whether a decision aid explaining how to discuss the approach of death with a family member with cancer would help family caregivers decide to discuss a terminal prognosis . PATIENTS AND METHODS We r and omly assigned caregivers of terminally ill patients with cancer to a group that received a video and a companion workbook that showed either how they can discuss the prognosis with their patient ( experimental arm ) or how cancer pain can be controlled ( control arm ) . At baseline and 1 month , we evaluated the decision to discuss terminal prognosis as the primary outcome . At 0 , 1 , 3 , and 6 months , we assessed the caregivers ' decisional conflict and satisfaction as secondary outcomes using a Decision Conflict Scale ( DCS ) . RESULTS We found no difference in changes in the decision to discuss terminal prognosis between the two groups . Conflict ( P = .003 ) , uncertainty ( P = .019 ) , and value clarity ( P = .007 ) subscale scores and total DCS score ( P = .008 ) improved from baseline to 1 month significantly more in the experimental arm than in the control arm . Over 6 months , the significant between-group differences continued for the conflict ( P = .031 ) , uncertainty ( P = .014 ) , and value clarity ( P = .039 ) subscale scores and total DCS score ( P = .040 ) . CONCLUSION Decision aids can help caregivers , with the aid of trained professionals , to communicate with patients about their terminal illness OBJECTIVE Web-based patient information is widespread and information on the benefits and risks of treatments is often difficult to underst and . We therefore evaluated different risk presentation formats - numerical , graphical and others - addressing the pros and cons of tight control versus usual treatment approaches for diabetes . METHOD DESIGN R and omised controlled trial . SETTING Online . Publicity disseminated via Diabetes UK . PARTICIPANTS People with diabetes or their carers . INTERVENTIONS Control group information based on British Medical Journal ' Best Treatments ' . Four intervention groups received enhanced information re sources : ( 1 ) detailed numerical information ( absolute/relative risk , numbers-needed-to-treat ) ; ( 2 ) ' anchoring ' to familiar risks or descriptions ; ( 3 ) graphical ( bar charts , thermometer scales , crowd figure formats ) ; ( 4 ) combination of 1 - 3 .  OUTCOMES Decision conflict scale ( DCS , a measure of uncertainty ) ; satisfaction with information ; further free text responses for qualitative content analysis . RESULTS Seven hundred and ten people visited the website and were r and omised . Five hundred and eight completed the question naire for quantitative data . Mean DCS scores ranged from 2.12 to 2.24 for the five r and omisation groups , indicating neither clear delay or vacillation about decisions ( usually DCS>2.5 ) nor tending to make decisions ( usually DCS<2.0 ) . There were no statistically significant effects of the interventions on DCS , or satisfaction with information .  Two hundred and fifty-six participants provided responses for qualitative analysis : most found graphical representations helpful , specifically bar chart formats ; many found other graphic formats ( thermometer style , crowd figures/smiley faces ) and ' anchoring ' information unhelpful , and indicated information overload . Many negative experiences with healthcare indicate a challenging context for effective information provision and decision support . CONCLUSION Online evaluation of different risk representation formats was feasible . There was a lack of intervention effects on quantitative outcomes , perhaps reflecting already well-informed participants from the Diabetes UK patient organisation . The large qualitative data set included many comments about what participants found helpful as formats for communicating risk information . PRACTICE IMPLICATION S These findings assist the design of online decision aids and the representation of risk information . The challenge is to provide more information , in appropriate and clear formats , but without risking information overload . Interactive web design s hold much promise to achieve this Objectives We report the use of difference in differences ( DiD ) methodology to evaluate a complex , system-wide healthcare intervention . We use the worked example of evaluating the  Marie Curie Delivering Choice Programme ( DCP ) for advanced illness in a large urban healthcare economy . Methods DiD was selected because a r and omised controlled trial was not feasible . The method allows for before and after comparison of changes that occur in an intervention site with a matched control site . This enables analysts to control for the effect of the intervention in the absence of a local control . Any policy , seasonal or other confounding effects over the test period are assumed to have occurred in a balanced way at both sites . Data were obtained from primary care trusts . Outcomes were place of death , inpatient admissions , length of stay and costs . Results Small changes were identified between pre- and post-DCP outputs in the intervention site . The proportion of home deaths and median cost increased slightly , while the number of admissions per patient and the average length of stay per admission decreased slightly . None of these changes was statistically significant . Conclusions Effects estimates were limited by small numbers accessing new services and selection bias in sample population and comparator site . In evaluating the effect of a complex healthcare intervention , the choice of analysis method and output measures is crucial . Alternatives to r and omised controlled trials may be required for evaluating large scale complex interventions and the DiD approach is suitable , subject to careful selection of measured outputs and control population Background : Nowadays , an increasing number of discrete-choice experiments ( DCEs ) incorporate cost as an attribute . However , the inclusion of a cost attribute , particularly within collectively funded healthcare systems , can be challenging because health services or goods are generally not traded in a market situation and individuals are not used to paying for a service or a good at the point of consumption . Objective : To examine whether the inclusion of a cost attribute in a DCE results in different preferences regarding a surgical treatment  for primary basal cell carcinoma ( BCC ) compared with a DCE without a cost attribute . Methods : A r and omized study was performed in which the impact of a cost attribute on the general public ’s preferences for a surgical treatment ( Mohs micrographic surgery [ MMS ] or st and ard excision [ SE ] ) to remove BCC was examined . This was done by comparing the outcomes of two DCEs , one with a cost attribute ( DCE_cost ) and one without ( DCE_nocost ) . Six attributes ( recurrence , re-excision , travel time , surgical time , waiting time for surgical results , costs ) and their levels were selected , based on results of a clinical trial , a cost-effectiveness study , a review and a focus group of patients who had recently received treatment for BCC . Outcomes of both DCEs were compared in terms of theoretical validity , relative importance of the attributes and the rank order of preferences . Results : A total of 615 respondents ( n = 303 for DCE_nocost ; n = 312 for DCE_cost ) were interviewed by telephone . This gave an overall response rate of 38%.Respondents in DCE_nocost preferred a surgical treatment with a lower probability of recurrence , lower surgery time , lower waiting time and no risk for a re-excision . Respondents in DCE_cost showed the same preferences , but also preferred a treatment with less travel time and lower costs . Overall , respondents in both DCEs showed the same preference for a surgical treatment : MMS  was preferred over SE . Conclusion : Results suggest that , in this population , the inclusion of a cost attribute in a DCE leads to the same preference regarding a surgical treatment to remove BCC as a DCE without a cost attribute . However , further research in different setting s is needed to confirm these findings Health systems worldwide are grappling with the need to control costs to maintain system viability . With the combination of worsening economic conditions , an aging population and reductions in tax revenues , the pressures to make structural changes are expected to continue growing . Common cost control mechanisms , e.g. curtailment of patient access and treatment prioritization , are likely to be adversely viewed by citizens . It seems therefore wise to include them in the decision making processes that lead up to policy changes . In the context of a multilevel iterative mixed- method design a quantitative survey representative of the German population ( N = 2031 ) was conducted to probe the acceptance of priority setting in medicine and to explore the practicability of direct public involvement . Here we focus on preferences for patients ' characteristics ( medical aspects , lifestyle and socio-economic status ) as possible criteria for prioritizing medical services . A question naire with closed response options was fielded to gain insight into attitudes toward broad prioritization criteria of patient groups . Furthermore , a discrete choice experiment was used as a rigorous approach to investigate citizens ' preferences toward specific criteria level in context of other criteria . Both the question naire and the discrete choice experiment were performed with the same sample . The citizens ' own health and social situation are included as explanatory variables . Data were evaluated using corresponding analysis , contingency analysis , logistic regression and a multinomial exploded logit model . The results show that some medical criteria are highly accepted for prioritizing patients whereas socio-economic criteria are rejected Background A new modelling approach for analysing data from discrete-choice experiments ( DCEs ) has been recently developed in transport economics based on the notion of regret minimization-driven choice behaviour . This so-called R and om Regret Minimization ( RRM ) approach forms an alternative to the dominant R and om Utility Maximization ( RUM ) approach . The RRM approach is able to model semi-compensatory choice behaviour and compromise effects , while being as parsimonious and formally tractable as the RUM approach . Objectives Our objectives were to introduce the RRM modelling approach to healthcare-related decisions , and to investigate its usefulness in this domain . Methods Using data from DCEs aim ed at determining valuations of attributes of osteoporosis drug treatments and human papillomavirus ( HPV ) vaccinations , we empirically compared RRM models , RUM models and Hybrid RUM – RRM models in terms of goodness of fit , parameter ratios and predicted choice probabilities . Results In terms of model fit , the RRM model did not outperform the RUM model significantly in the case of the osteoporosis DCE data ( p = 0.21 ) , whereas in the case of the HPV DCE data , the Hybrid RUM – RRM model outperformed the RUM model ( p < 0.05 ) . Differences in predicted choice probabilities between RUM models and ( Hybrid RUM- ) RRM models were small . Derived parameter ratios did not differ significantly between model types , but trade-offs between attributes implied by the two models can vary substantially . Conclusion Differences in model fit between RUM , RRM and Hybrid RUM – RRM were found to be small . Although our study did not show significant differences in parameter ratios , the RRM and Hybrid RUM – RRM models did feature considerable differences in terms of the trade-offs implied by these ratios . In combination , our results suggest that RRM and Hybrid RUM – RRM modelling approach hold the potential of offering new and policy-relevant insights for health research ers and policy makers Background : Decision aids may improve informed consent in clinical trial recruitment , but have not been evaluated in this context . This study investigated whether decision aids ( DAs ) can reduce decisional difficulties among women considering participation in the International Breast Cancer Intervention Study -II ( IBIS-II ) trial . Methods : The IBIS-II trial investigated breast cancer prevention with anastrazole in two cohorts : women with increased risk ( Prevention ) , and women treated for ductal carcinoma in situ ( DCIS ) . Australia , New Zeal and and United Kingdom participants were r and omised to receive a DA ( DA group ) or st and ard trial consent material s ( control group ) . Question naires were completed after deciding about participation in IBIS-II ( post decision ) and 3 months later ( follow-up ) . Results : Data from 112 Prevention and 34 DCIS participants were analysed post decision ( 73 DA ; 73 control ) ; 95 Prevention and 24 DCIS participants were analysed at follow-up ( 58 DA ; 61 control ) . There was no effect on the primary outcome of decisional conflict . The DCIS – DA group had higher knowledge post decision , and the Prevention-DA group had lower decisional regret at follow-up . Conclusions : This was the first study to evaluate a DA in the clinical trial setting . The results suggest DAs can potentially increase knowledge and reduce decisional regret about clinical trial participation
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 .