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Data are the new gold. Real data are always incomplete. Sometimes we can derive valid conclusions by just ignoring the missing data. More typically, the implications caused by the unknown data simply fail to evaporate. I have always been fascinated by the question of how the limited scope of information affects our judgement. Had the missing data been known, what would our conclusion have been?
During my career in both TNO and academia, I've pioneered quantitative algorithms for "filling up the missing data" (imputation). These methods learn plausible values from the observed data. Nowadays, the MICE algorithm is the de facto international standard for the analysis of incomplete data. Investigators across all sciences rely on MICE.
I've employed MICE and related methods in many TNO projects, especially in child growth, child development and healthy living. In the coming years, I like to implement and publish novel quantitative methods into JAMES web services. This will make growth charts prediction and the D-score, a new system for expressing child development on a quantitative scale, available to investigators worldwide. I collaborate with the World Health Organization and the Bill & Melinda Gates Foundation.