Gitta Lubke

Gitta Lubke


Ph.D., VU University Amsterdam


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Gitta Lubke's research interests are in general latent variable modeling and data mining. In addition to the challenges of analysing complex human behavior such as psychiatric disorders, she is interested in the analysis of genetic data. Current substantive work focuses on data mining approaches and integrative data analysis applied to large multi county collections of data related to childhood aggression. Other areas of expertise include mixture modeling, twin models, measurement invariance and multi-group factor analysis, longitudinal analyses, and the analysis of categorical data.

Recent Publications

Hong M., Jacobucci R., Lubke G. (submitted). Deductive Data Mining.

Luningham J., McArtor D., Hendriks A., van Beijsterveld T., Lichtenstein P., Lundström S., Larsson H., Bartels M., Boomsma D., Lubke G. (submitted). Data integration methods for phenotype harmonization in multi-cohort genome-wide association studies with behavioral outcomes.

Mårland C., Lubke G.H., Degl’ Innocenti A., Råstam M., Gillberg C., Nilsson T., Lundström S. (submitted). A development of a brief screener for autism using item-response theory.


Campbell I., Lundstrom S., Larsson H., Lichtenstein P., Lubke G.H. (2018). The relation between the age at diagnosis of problem behaviors related to aggression and distal outcomes in Swedish children. European Child and Adolescent Psychiatry,


Bartels M., Hendriks AM, Mauri M, Krapohl E, Whipp A, Bolhuis K, Colodro Conde L, Luningham JM, Ip H, Hagenbeek F; Roetman PJ, Gatej R, Lamers A, Nivard MG, Dongen JJ, Lu Y, Middeldorp C, van Beijsterveldt T, Vermeiren R, Hankemeier T, Kluft C, Medland S, Lundstrom S, Rose R, Pulkkinen L, Vuoksimaa E, Korhonen T, Martin NM, Lubke GH, Finkenauer C, Fanos V, Tiemeier H, Lichtenstein P, Plomin R, Kaprio J,  DI. (2018). Childhood Aggression and the co-occurrence of behavioural and emotional problems:  Results across ages 3-16 years from multiple raters in 6 cohorts in the EU-ACTION project. European Child and Adolescent Psychiatry, 27(9), 1105-1121.


Luningham JM, Bartels M, Boomsma DI, Lubke GH (2018) Genetic and environmental contributions to overt aggression harmonized across multiple studies, nations, and raters in the ACTION consortium. Behavior Genetics 48(6), 491-492.


Lubke, G. H., McArtor, D. B., Boomsma, D. I., Bartels, M. (2018). Genetic and environmental contributions to the development of childhood aggression. Developmental Psychology. [Epub ahead of print 23 Oct 2017].

Lubke, G. H., Luningham, J. M. (2017) Fitting Latent Variable Mixture Models. Behaviour Research and Therapy, 98:91-102. NIHMSID 921045.

Bolhuis, K., Lubke, G. H., van der Ende, J., Bartels, M., van Beijsterveldt, C. E. M., Lichtenstein, P., Larsson, H., Jaddoe, V. W., Kushner, S. A., Verhulst, F. C., Boomsma, D. I., Tiemeier, H. (2017). Disentangling heterogeneity of childhood disruptive behavior problems into dimensions and subgroups. Journal of the American Academy of Child & Adolescent Psychiatry, 56(8), 678-686. PMID 28735697.

Luningham, J. M., McArtor, D. B., Bartels, M., Boomsma, D .I., Lubke, G. H., (2017) Sum Scores in Twin Growth Curve Models: Practicality Versus Bias. Behavior Genetics. 2017 Aug 5. doi: 10.1007/s10519-017-9864-0. [Epub ahead of print] NIHMSID 920907.

Justice, A. E., Winkler, T. W., Feitosa, M. F., Graff, M., Fisher, V. A., Young, K., ... & Ngwa, J. S.
(2017). Genome-wide meta-analysis of 241,258 Adults Accounting for Smoking Behavior Identifies Novel Loci for Obesity Traits. Nature Communications, 8: 14977. PMC5414044

Lubke, G. H., Campbell, I., McArtor, D. B., Miller, P. J., Luningham, J. M., van den Berg, S. M. (2017). Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update. Structural Equation Modeling. 24(2), 230-245. PMC5482523

Miller, P. J., Lubke, G. H., McArtor, D. B., Bergeman, C. S. (2016). Finding structure in data using multivariate tree boosting. Psychological Methods 21 (4), 583-602. PMC5142230.

McArtor, D. B., Lubke, G. H.,(2016). Extending multivariate distance matrix regression with an effect size measure and the distribution of the test statistic. Psychometrika DOI: 10.1007/s11336-016-9527-8. PMC5624863