An estimated 85 % of global health research investment is wasted; a total of one hundred billion US dollars in the year 2009 when it was estimated. The movement to reduce this waste recommends that previous studies be taken into account when prioritising, designing and interpreting new research. Yet current practice to summarize previous studies ignores two crucial aspects: promising initial results are more likely to develop into (large) series of studies than their disappointing counterparts, and conclusive studies are more likely to trigger meta-analyses than not so noteworthy findings. Failing to account for these aspects introduces ‘accumulation bias’, a term coined by our Machine Learning research group to study all possible dependencies potentially involved in meta-analysis. Accumulation bias asks for new statistical methods to limit incorrect decisions from health research while avoiding research waste.

Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning
Machine Learning

ter Schure, J. (2019). Efficient accumulation of scientific knowledge, research waste and accumulation bias. ERCIM News, 116, 8–9.