Stef van Buuren

Professorship chair

Statistical Analysis of Incomplete Data (Utrecht University).

Research area

Missing values are the data we do not see. Missing data may lead us to misunderstand the world, draw incorrect conclusions and make poor decisions. In practice, data are always incomplete. How, then, can we draw valid conclusions?

Well, imagine what the complete data would look like, determine what is missing and why, and then try to recreate the missing data from what we know. Of course, this recreation can never be perfect, so we need to represent these new synthetic data as distributions instead of point values. If done correctly, the idea allows us to evade systematic errors in our judgment.

During my career in both TNO and academia, I have pioneered quantitative algorithms for multiple imputation of missing values. These methods learn plausible values from the observed data. The Multivariate Imputation by Chained Equations (MICE) algorithm has become the de facto standard for completing and analysing data in many fields.

Investigators both within and outside TNO and across many sciences rely on MICE. I apply MICE and related methodologies in many TNO projects, especially in child growth, development, healthy living, and projects for the World Health Organisation and the Bill & Melinda Gates Foundation.

Top publications

• van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03
• van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Boca Raton, FL: Chapman & Hall/CRC Press. https://stefvanbuuren.name/fimd/
• Weber, A. M., Rubio-Codina, M., Walker, S. P., van Buuren, S., Eekhout, I., Grantham-McGregor, S. M., et al. (2019). The D-score: A metric for interpreting the early development of infants and toddlers across global settings. BMJ Global Health, 4(6). doi: 10.1136/bmjgh-2019-001724.