• 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 Societ├á 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.