Publications
2022
Scholarly publications
de Koning, M-S. L., van Dorp, P., Assa, S., Hartman, M. H., Voskuil, M., Anthonio, R. L., Veen, D., Pundziute-Do Prado, G., Leiner, T., van Goor, H., van der Meer, P., van Veldhuisen, D. J., Nijveldt, R., Lipsic, E., & van der Harst, P. (2022). Rationale and Design of the Groningen Intervention Study for the Preservation of Cardiac Function with Sodium Thiosulfate after St-segment Elevation Myocardial Infarction (GIPS-IV) trial. American Heart Journal, 243, 167-176. https://doi.org/10.1016/j.ahj.2021.08.012
Mitratza, M., Goodale, B. M., Shagadatova, A., Kovacevic, V., van de Wijgert, J., Brakenhoff, T. B., Dobson, R., Franks, B., Veen, D., Folarin, A. A., Stolk, P., Grobbee, J., Cronin, M., & Downward, G. S. (2022). The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review. The Lancet Digital Health, 4(5), e370-e383. https://doi.org/10.1016/S2589-7500(22)00019-X
2021
Scholarly publications
van de Schoot, R., Winter, S. D., Griffioen, E., Grimmelikhuijsen, S., Arts, I., Veen, D., Grandfield, E., & Tummers, L. G. (2021). The Use of Questionable Research Practices to Survive in Academia Examined With Expert Elicitation, Prior-Data Conflicts, Bayes Factors for Replication Effects, and the Bayes Truth Serum. Frontiers in Psychology, 12, 1-14. [621547]. https://doi.org/10.3389/fpsyg.2021.621547
de Klerk, M., de Bree, E., Veen, D., & Wijnen, F. (2021). Speech discrimination in infants at family risk of dyslexia: Group and individual-based analyses. Journal of Experimental Child Psychology, 206, 1-26. [105066]. https://doi.org/10.1016/j.jecp.2020.105066
2020
Scholarly publications
Schat, E., van de Schoot, R., Kouw, W. M., Veen, D., & Mendrik, A. M. (2020). The data representativeness criterion: Predicting the performance of supervised classification based on data set similarity. PLoS One, 15(8 August), [e0237009]. https://doi.org/10.1371/journal.pone.0237009
van de Schoot, R., Veen, D., Smeets, L., Winter, S. D., & Depaoli, S. (2020). A Tutorial on Using The Wambs Checklist to Avoid The Misuse of Bayesian Statistics. In R. van de Schoot, & M. Miočevic (Eds.), Small sample size solutions: A Guide for Applied Researchers and Practitioners (1 ed., pp. 30-49). Routledge. https://doi.org/10.4324/9780429273872-4
Veen, D., Egberts, M. R., Van Loey, N. E. E., & van de Schoot, R. (2020). Expert Elicitation for Latent Growth Curve Models: The Case of Posttraumatic Stress Symptoms Development in Children With Burn Injuries. Frontiers in Psychology, 11(1197), [1197]. https://doi.org/10.3389/fpsyg.2020.01197
Veen, D. (2020). Alternative Information: Bayesian Statistics, Expert Elicitation and Information Theory in the Social Sciences. [Doctoral thesis 1 (Research UU / Graduation UU), Universiteit Utrecht]. Utrecht University.
https://dspace.library.uu.nl/bitstream/handle/1874/392402/5e3c2cee8d532.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/392402/5e3c2cee8d532.pdf?sequence=1
2019
Scholarly publications
NEON study group (2019). Meta-analysis of the outcomes of treatment of internal carotid artery near occlusion. British Journal of Surgery, 106(6), 665-671. https://doi.org/10.1002/bjs.11159
Veen, D., & Klugkist, I. (2019). Standard errors, priors, and bridge sampling: A Discussion of Liu et al. Journal of the Korean Statistical Society, 48(4), 515-517. https://doi.org/10.1016/j.jkss.2019.07.004
de Klerk, M., Veen, D., Wijnen, F., & de Bree, E. (2019). A step forward: Bayesian hierarchical modelling as a tool in assessment of individual discrimination performance. Infant Behavior and Development, 57, [101345]. https://doi.org/10.1016/j.infbeh.2019.101345
https://dspace.library.uu.nl/bitstream/handle/1874/390955/StepForward_Manuscript_NOTBlinded_NOTMarked_20190719.pdf?sequence=3
https://dspace.library.uu.nl/bitstream/handle/1874/390955/StepForward_Manuscript_NOTBlinded_NOTMarked_20190719.pdf?sequence=3
2018
Scholarly publications
Fox, J. P., Veen, D., & Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. https://doi.org/10.1027/1614-2241/a000153
Veen, D., Stoel, D., Schalken, N., Mulder, K., & van de Schoot, R. (2018). Using the Data Agreement Criterion to rank Experts' beliefs. Entropy, 20(8), [592]. https://doi.org/10.3390/e20080592
Other output
Veen, D. (Author). (2018). Correlational Statistics. Software
Veen, D. (Author). (2018). Sampling Correlations. Software
Veen, D. (Author). (2018). Effects-Coding simulation tool. Software
2017
Scholarly publications
Veen, D., Stoel, D., Zondervan - Zwijnenburg, M., & van de Schoot, R. (2017). Proposal for a five-step method to elicit expert judgment. Frontiers in Psychology, 8(DEC), [2110]. https://doi.org/10.3389/fpsyg.2017.02110
Other output
Veen, D. (Author). (2017). DAC. Software
2016
Other output
Fox, J. P. (Author), Klotzke, K. (Author), & Veen, D. (Author). (2016). GLMMRR: Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data: R package. Software
Veen, D., Van Loey, N. E. E., van Baar, A. L., Wijnen, F. N. K., & van de Schoot, A. G. J. (2016). Eliciting and Using Expert Information in Latent Growth Curve Models: Research Plan. Poster session presented at VNOP ISED CAS Research Days, Leiden.
2015
Professional publications
Luyten, H., Veen, D., & Meelissen, M. (2015). De relatie tussen leerling- en schoolkenmerken en digitale geletterdheid van 14-jarigen: secundaire analyses op de data van ICILS-2013. Stichting Kennisnet.
https://dspace.library.uu.nl/bitstream/handle/1874/349373/Rapport_Secundaire_analyses_op_de_Nederlandse_ICILS_2810_aug_2015_29.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/349373/Rapport_Secundaire_analyses_op_de_Nederlandse_ICILS_2810_aug_2015_29.pdf?sequence=1