I was visiting University of Groningen a few days ago for an interdisciplinary meeting about the gap between clinical practice and methodology, and ran into Dr Laura Bringmann who I know well from our time together at University of Leuven.
Since Laura and colleagues (i.e. Casper Albers, Jojanneke Bastiaansen, and Yoram Kunkels) have started a fascinating project on addressing the very gap that our meeting was about, I decided to use the opportunity to pick Laura’s brain regarding her project. We recorded a 15 minute discussion in which we talk about ESM data, potential advantages of temporal over cross-sectional data, recent methodological advances in network modeling, time-varying network models, and how to choose the best model for the data from the large amount of models that are available now.
Most importantly, Laura introduces the fantastic international and multidisciplinary research project she has been setting up with colleagues. For this project, different groups of researchers independently analyze the same dataset of one patient from the dataset shared by Aaron Fisher that he recently described here in more detail. The goal for each of the research groups is then to provide clinical recommendations for this particular patient based on their results. Laura’s main question is how similar these clinical recommendations are.
You can listen the audio file here:
Laura and colleagues are currently analysing the main results we discuss above, and aim to be able to disseminate the results around spring 2018. We hope you enjoyed the conversation, feel free to leave feedback below. If there is sufficient interest, I might do more of these in the near future on network-related topics.
The papers Laura and I talk about are listed below.
- Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., Tuerlinckx, F. (2013). A network approach to psychopathology: new insights into clinical longitudinal data. PloS One, 8(4), e60188. (PDF)
- Bringmann, L. F., Hamaker, E. L., Vigo, D. E., Aubert, A., Borsboom, D., & Tuerlinckx, F. (2016). Changing Dynamics: Time-Varying Autoregressive Models Using Generalized Additive Modeling. Psychological Methods. (PDF)
- Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2017). Discovering psychological dynamics in time-series data. Multivariate Behavioral Research. (PDF)
- Fisher, A. J., Reeves, J. W., Lawyer, G., Rubel, J. A. (2017). A Network Model for Integrating Contemporaneous and Temporal Effects: Mapping the Idiographic Dynamics of Mood and Anxiety. (PDF)
- Haslbeck J. M. B., Waldorp L. J. (under review). mgm: Structure Estimation for Time-Varying Mixed Graphical Models in high-dimensional Data. The Journal of Statistical Software. (PDF)
- Kossakowski, J. J., Groot, P. C., Haslbeck, J. M. B., Borsboom, D., & Wichers, M. (2017). Data from “Critical Slowing Down as a Personalized Early Warning Signal for Depression.” Journal of Open Psychology Data. http://doi.org/10.5334/jopd.29.
- Krone, T., Albers, C. J., Timmerman, M. E. (2016). Comparison of Estimation Procedures for Multilevel AR(1) Models. Frontiers in Psychology 7(1038). (PDF)
- Molenaar, P. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 37–41. http://doi.org/10.1207/s15366359mea0204
- Schuurman, N. K., Ferrer, E., de Boer-Sonnenschein, M., & Hamaker, E. L. (2016). How to Compare Cross-Lagged Associations in a Multilevel Autoregressive Model. Psychological Methods, 21(2), 206–221. (PDF)
People & labs
And here are the labs and people Laura mentioned: the Ilab, the TRANS-ID project, the Idiographic Dynamics Lab of Aaron Fisher, the Utrecht Dynamic Modeling Lab, Peter Molenaar, Jojanneke Bastiaansen, Yoram Kunkels, and Casper Albers, Noémi Schuurman, Denny Borsboom, and Francis Tuerlinckx.
Phew. We probably forgot someone, and apologize profusely already. Let us know!
Credits: the intro song is “Electric Lady Supastar” by Scomber, licensed under Creative Commons Attribution Noncommercial (3.0). The song was not changed or adapted.