Suicide Prevention By Extending Reseach


SUicide Prevention by Extending Research

Contemporary models explaining suicidal behaviour highlight the complex interaction between biological, environmental, psychological and social factors (O'Connor & Nock 2014; O'Connor, 2011). This complexity brings challenges not only for patients and clinicians, but also for scientists that study suicidal behavior. 

Together with an international consortium of suicide researchers and data experts,  I am applying modern psychometric techniques such as network analysis to exististing international databases with information on suicidal behavior. This project is called SUPER (SUicide Prevention by Extending Reseach). I received a highly competitive grand (fellowship GGZ) from the Netherlands Organisation for health research and development for the period 2017-2020.



In may 2017, we published the first article to apply networkanalysis within the field of suicidology. 

I made an online tutorial that offers a step by step guidance through the R code of the article. Data is also available.  Also, I added a novel analysis, not yet available at the time when the original article was written: accuracy and stability estimation (Epskamp and Fried, 2017).



Causality and suicidal behavior

Recent innovation in the field of computer science and graph theory allow us to estimate causality from observational data only. As counterintuitive as this may sound, this means that we can use existing crossectional databases to inform us which interventions are most likely to benefit suicidal patients, and thereby improve the evidence for suicide prevention.