Figure 3.

This illustration shows several steps in producing common cognitive endpoints using data from different sources/cohorts. The first column shows a theoretical set of data from a variety of sources with unknown disparity, for which the value of the data in aggregated form is unknown. When data is collated from these various sources, the disparity can be quantified and constructs emerge. Based on the degree of disparity, different statistical methods for harmonizing the constructs can be applied to minimize disparity. Finally, these constructs can be refactored into data that would allow investigators to perform comparisons across these datasets, thereby improving the value and extending the usability of the data in big data analyses.