TY - JOUR
T1 - Comparison of statistical analysis methods for object case best-worst scaling
AU - Cheung, Kei Long
AU - Mayer, Susanne
AU - Simon, Judit
AU - de Vries, Hein
AU - Evers, Silvia M. A. A.
AU - Kremer, Ingrid E. H.
AU - Hiligsmann, Mickael
N1 - Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2019/6/3
Y1 - 2019/6/3
N2 - Aims: Different methods have been used to analyze object case best-worst scaling (BWS). This study aims to compare the most common statistical analysis methods for object case BWS (i.e. the count analysis, multinomial logit, mixed logit, latent class analysis, and hierarchical Bayes estimation) and to analyze their potential advantages and limitations based on an applied example.Methods: Data were analyzed using the five analysis methods. Ranking results were compared among the methods, and methods that take respondent heterogeneity into account were presented specifically. A BWS object case survey with 22 factors was used as a case study, tested among 136 policy-makers and HTA experts from the Netherlands, Germany, France, and the UK to assess the most important barriers to HTA usage.Results: Overall, the five statistical methods yielded similar rankings, particularly in the extreme ends. Latent class analysis identified five clusters and the mixed logit model revealed significant preference heterogeneity for all, with the exception of three factors.Limitations: The variety of software used to analyze BWS data may affect the results. Moreover, this study focuses solely on the comparison of different analysis methods for the BWS object case.Conclusions: The most common statistical methods provide similar rankings of the factors. Therefore, for main preference elicitation, count analysis may be considered as a valid and simple first-choice approach. However, the latent class and mixed logit models reveal additional information: identifying latent segments and/or recognizing respondent heterogeneity.
AB - Aims: Different methods have been used to analyze object case best-worst scaling (BWS). This study aims to compare the most common statistical analysis methods for object case BWS (i.e. the count analysis, multinomial logit, mixed logit, latent class analysis, and hierarchical Bayes estimation) and to analyze their potential advantages and limitations based on an applied example.Methods: Data were analyzed using the five analysis methods. Ranking results were compared among the methods, and methods that take respondent heterogeneity into account were presented specifically. A BWS object case survey with 22 factors was used as a case study, tested among 136 policy-makers and HTA experts from the Netherlands, Germany, France, and the UK to assess the most important barriers to HTA usage.Results: Overall, the five statistical methods yielded similar rankings, particularly in the extreme ends. Latent class analysis identified five clusters and the mixed logit model revealed significant preference heterogeneity for all, with the exception of three factors.Limitations: The variety of software used to analyze BWS data may affect the results. Moreover, this study focuses solely on the comparison of different analysis methods for the BWS object case.Conclusions: The most common statistical methods provide similar rankings of the factors. Therefore, for main preference elicitation, count analysis may be considered as a valid and simple first-choice approach. However, the latent class and mixed logit models reveal additional information: identifying latent segments and/or recognizing respondent heterogeneity.
KW - Analysis
KW - best-worst scaling
KW - comparison
KW - methods
KW - object case
KW - DISCRETE-CHOICE EXPERIMENTS
KW - HEALTH TECHNOLOGY-ASSESSMENT
KW - CONJOINT-ANALYSIS
KW - PREFERENCES
KW - PERCEPTIONS
U2 - 10.1080/13696998.2018.1553781
DO - 10.1080/13696998.2018.1553781
M3 - Article
C2 - 30482068
SN - 1369-6998
VL - 22
SP - 509
EP - 515
JO - Journal of Medical Economics
JF - Journal of Medical Economics
IS - 6
ER -