Chekroud, A.M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., Dwyer, D., Choi, K. (2021). The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry, 20(2), 154-170. https://doi.org/10.1002/wps.20882
Fokkema, M., Edbrooke-Childs, J. & Wolpert, M. (2021). Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data. Psychotherapy Research, 31(3), 313-325. https://doi.org/10.1080/10503307.2020.1785037     Manuscript. Supplementary tutorial. Supplementary data.


Fokkema, M. (2020). Fitting prediction rule ensembles with R package pre. Journal of Statistical Software, 92(12), 1-30. http://doi.org/10.18637/jss.v092.i12

van Loon, W., Fokkema, M., Szabo, B., de Rooij, M. (2020). Stacked penalized logistic regression for selecting views in multi-view learning. Information Fusion, 61, 113-123. https://doi.org/10.1016/j.inffus.2020.03.007

Van Loon, W., Fokkema, M., Szabo, B., De Rooij, M. (2020). Multi-view stacking: Choosing the meta-learner. https://arxiv.org/abs/2010.16271

Wolpert, M., Zamperoni, V., Napoleone, E., Patalay, P., Jacob, J., Fokkema, M., Promberger, M., Costa da Silva, L., Patel, M., & Edbrooke-Childs, J. (2020). Predicting mental health improvement and deterioration in a large community sample of 11- to 13-year-olds. European Child & Adolescent Psychiatry. http://doi.org/10.1007/s00787-019-01334-4


van Ballegooijen, W., Eikelenboom, M., Fokkema, M., Riper, H., van Hemert, A. M., Kerkhof, A. J., ... & Smit, J. H. (2019). Comparing factor structures of depressed patients with and without suicidal ideation, a measurement invariance analysis. Journal of Affective Disorders, 245, 180-187. http://doi.org/10.1016/j.jad.2018.10.108

Rohrbach, P., Dingemans, A.E., Spinhove, P., Van Ginkel, J., Fokkema, M., Van den Akker-Van Marle, E., Van Furth, E., Moessner, M. & Bauer, S. (2019). A randomized controlled trial of an internet-based intervention for eating disorders and the added value of expert-patient support: study protocol. Trials 20, 509. http://doi.org/10.1186/s13063-019-3574-2

de Rooij, M., Pratiwi, B.C., Fokkema, M., Dusseldorp, E. & Kelderman, H. (2019). The Early Roots of Statistical Learning in the Psychometric Literature: A review and two new results. Preprint: arxiv:1911.11463


Fokkema, M., Smits, N., Zeileis, A., Hothorn, T. & Kelderman, H. (2018). Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees. Behavior Research Methods, 50(5), 2016-2034.  http://doi.org/10.3758/s13428-017-0971-x

Fokkema, M. & Greiff, S. (2018). Would you prefer your coefficients with a little bias, or rather with a lot of variance? European Journal of Psychological Assessment, 34(6), 363-366. http://doi.org/10.1027/1015-5759/a000514

Driessen, E., Abbass, A. A., Barber, J. P., Gibbons, M. B. C., Dekker, J. J., Fokkema, M., ... & Town, J. M. (2018). Which patients benefit specifically from short-term psychodynamic psychotherapy (STPP) for depression? Study protocol of a systematic review and meta-analysis of individual participant data. BMJ open, 8(2), e018900.


Fokkema, M., & Greiff, S. (2017). How performing PCA and CFA on the same data equals trouble. European Journal of Psychological Assessment, 33(6), 399–402. http://doi.org/10.1027/1015-5759/a000460

Aardoom, J.J., Dingemans, A.E., Fokkema, M., Spinhoven, P., & Van Furth, E.F. (2017). Moderators of change in an Internet-based intervention for eating disorders with different levels of therapist support: What works for whom? Behaviour Research and Therapy, 89, 66-74. http://doi.org/10.1016/j.brat.2016.11.012

Kraan, T. C., Ising, H. K., Fokkema, M., Velthorst, E., van den Berg, D. P., Kerkhoven, M., ... & Wunderink, L. (2017). The effect of childhood adversity on 4-year outcome in individuals at ultra high risk for psychosis in the Dutch Early Detection Intervention Evaluation (EDIE-NL) Trial. Psychiatry Research, 247, 55-62. http://doi.org/10.1016/j.psychres.2016.11.014

Meijer, E., Van Laar, C., Gebhardt, W.A., Fokkema, M., Van den Putte, B., Dijkstra, A., Fong, G. T., & Willemsen, M.C. (2017). Identity change among smokers and ex-smokers: Findings from the ITC Netherlands survey. Psychology of Addictive Behaviors, 31(4), 465-478. http://doi.org/10.1037/adb0000281


De Beurs, D. Fokkema, M., O'Connor, R. (2016). Optimizing the assessment of suicidal behavior: The application of curtailment techniques. Journal of Affective Disorders, 196, 218-224. http://doi.org/10.1016/j.jad.2016.02.033


De Beurs, D.P., Fokkema, M., De Groot, M.H., De Keijser, J. & Kerkhof, A.J.F.M. (2015). Longitudinal measurement invariance of the Beck Scale for Suicide Ideation. Psychiatry Research, 225(3), 368–373. http://doi.org/10.1016/j.psychres.2014.11.075

Fokkema, M., Smits, N., Kelderman, & Penninx, B.W.J.H. (2015). Connecting clinical and actuarial prediction with rule-based methods. Psychological Assessment, 27(2), 636-644. http://doi.org/10.1037/pas0000072

Zhang, B., Gao, Q., Fokkema, M., Alterman, V., Liu, & Q. (2015). Adolescent Interpersonal Relationships, Social Support and Loneliness in High Schools: Mediation Effect and Gender Differences. Social Science Research, 53, 104–117. http://doi.org/10.1016/j.ssresearch.2015.05.003


Fokkema, M., Smits, N., Finkelman, M. D., Kelderman, H., & Cuijpers, P. (2014). Curtailment: A method to reduce the length of self-report questionnaires while maintaining diagnostic accuracy. Psychiatry Research, 215(2), 477-482.

Fokkema, M., Smits, N., Kelderman, H., Carlier, I. V., & Van Hemert, A. M. (2014). Combining decision trees and stochastic curtailment for assessment length reduction of test batteries used for classification. Applied Psychological Measurement, 38(1), 3-17. http://doi.org/10.1177/0146621613494466

Geraedts, A.S., Fokkema, M., Kleiboer, A.M., Smit, F., Wiezer, N.W., Majo, M.C., Van Mechelen, W., Cuijpers, P., & Penninx, B.W.J.H. (2014). The longitudinal prediction of costs due to health care uptake and productivity losses in a cohort of employees with and without depression or anxiety. Journal of Occupational and Environmental Medicine, 56(8), 794-801. http://doi.org/10.1097/JOM.0000000000000234


Fokkema, M., Smits, N., Kelderman, H., & Cuijpers, P. (2013). Response shifts in mental health interventions: An illustration of longitudinal measurement invariance. Psychological Assessment, 25(2), 520-531. http://doi.org/10.1037/a0031669

Rietdijk, J., Fokkema, M., Stahl, D., Valmaggia, L., Ising, H. K., Dragt, S., ... , van der Gaag, M. (2013). The distribution of self-reported psychotic-like experiences in non-psychotic help-seeking mental health patients in the general population; a factor mixture analysis. Social Psychiatry and Psychiatric Epidemiology, 49(3), 349-358. http://doi.org/10.1007/s00127-013-0772-1

2011 and before

Zhang, B., Fokkema, M., Cuijpers, P., Li, J., Smits, N., & Beekman, A. (2011). Measurement invariance of the center for epidemiological studies depression scale (CES-D) among Chinese and Dutch elderly. BMC Medical Research Methodology, 11(1), 74. http://doi.org/10.1186/1471-2288-11-74

De Wit, L. M., Fokkema, M., van Straten, A., Lamers, F., Cuijpers, P., & Penninx, B. W. (2010). Depressive and anxiety disorders and the association with obesity, physical, and social activities. Depression and Anxiety, 27(11), 1057-1065. http://doi.org/10.1002/da.20738