Li, X., Dusseldorp, E., & Meulman, J. J. (2017). Meta-CART: A tool to identify interactions between moderators in meta-analysis. British Journal of Mathematical and Statistical Psychology, 70(1), 118-136. Article
Conversano, C., & Dusseldorp, E. (2017). Modeling Threshold Interaction Effects Through the Logistic Classification Trunk. Journal of Classification, 1-28. Article
Hofstetter, H., Dusseldorp, E., Zeileis, A., & Schuller, A.A. (2016). Modeling caries experience: Advantages of the use of the hurdle model. Caries Research, 50(6), 517-526. Article
Dusseldorp, E., Doove, L., & Van Mechelen, I. (2015). Quint: An R package for identification of subgroups of clients who differ in which treatment alternative is best for them. Behavior Research Methods. Article
Doove, L. L., Van Deun, K., Dusseldorp, E., & Van Mechelen, I. (2015). QUINT: A tool to detect qualitative treatment-subgroup interactions in randomized controlled trials. Psychotherapy Research, (ahead-of-print), 1-11. Article
Hofstetter, H., Dusseldorp, E., Van Empelen, P., & Paulussen, T.W. (2014). A primer on the use of cluster analysis or factor analysis to assess co-occurrence of risk behaviors. Preventive Medicine, 67(2), 141-146. Article
Dusseldorp, E., van Genugten, L., van Buuren, S., Verheijden, M. W., & van Empelen, P. (2014). Combinations of Techniques That Effectively Change Health Behavior: Evidence From Meta-CART Analysis.Health Psychology, 33 1530-1540. Article
Dusseldorp, E., & Van Mechelen, I. (2014). Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interactions. Statistics in
medicine, 33(2), 219-237. Article and Appendix.
Doove, L. L., Dusseldorp, E., Van Deun, K., & Van Mechelen, I. (2014). A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment-subgroup interactions. Advances in Data Analysis and Classification, 1-23. Article
Doove, L. L., Van Buuren, S., & Dusseldorp, E. (2014). Recursive partitioning for missing data imputation in the presence of interaction effects. Computational Statistics & Data Analysis, 72, 92-104. Article
Dusseldorp, E., Conversano, C., & Van Os, B.J. (2010). Combining an additive and tree-based regression model simultaneously: STIMA. Journal of Computational and Graphical and Statistics, 19 (3), 514-530. DOI: 10.1198/jcgs.2010.06089, Article and Appendix.
Manisera, M., Van der Kooij, A. J., and Dusseldorp, E. (2010). Identifying the component structure of satisfaction scales by nonlinear principal components analysis. Quality Technology & Quantitative Management, 7 (2), 97-115. Article
Conversano, C. & Dusseldorp E. (2010). Simultaneous Threshold Interaction Detection in Binary Classification. Proceedings of the 6th Conference of the Classification and Data Analysis Group of the Società Italiana di Statistica. Springer series on “Studies in Classification, Data Analysis, and Knowledge Organization”, Francesco Palumbo, Carlo N Lauro, & Michael Greenacre (Eds.), XXII, pp. 225-232. ISBN: 978-3-642-03738-2.
Dusseldorp, E. & Meulman, J. J. (2004). The regression trunk approach to discover treatment covariate interaction. Psychometrika, 69, 355-374. Article
Dusseldorp, E. & Meulman, J. J. (2002). Application of data mining tools in the behavioral sciences. In J. Meij (Ed.), Dealing with the data flood: Mining data, text and multimedia (pp. 220-234). The Hague: Netherlands Study Center for Technology Trends (STT) / Beweton.
Dusseldorp, E. & Meulman, J. J. (2001). Prediction in medicine by integrating regression trees into regression analysis with optimal scaling. Methods of Information in Medicine, 40, 403-409. Abstract.
Dusseldorp, E. (2001). Discovering Treatment Covariate Interaction: An Integration of Regression Trees and Multiple Regression. Unpublished doctoral thesis, Leiden University, Leiden, the Netherlands. Contents. Short summary in Dutch.
Dusseldorp, E. (1996). The study of aptitude treatment interaction by nonlinear methods: Evaluation of a psychosocial treatment for chronic obstructive pulmonary disease. Research report RR-96-03, Leiden: Department of Data Theory.