From Information Management
There is a common problem often associated with managing data across scientific disciplines. As the stock of information rapidly grows through scientific discoveries, a major data management challenge emerges as data professionals try to tap prior research findings.
Current methods to aggregate quantitative findings (meta-analysis) have limitations. They assume that prior studies share similar designs and substantive factors. They rarely do.
Take for example studies estimating basal metabolic rate – the measure of human energy expenditure. Study results can have important implications for understanding human metabolism and developing obesity and malnutrition interventions.
Over 47 studies have estimated BMR. But these calculations are based on different body measures, such as fat mass, weight, age, and height – to name a few. How do we combine those studies into a single equation to get usable insights?
To address this issue, my colleagues and I designed a new method for aggregating prior work into a meta model, called “generalized model aggregation” (GMA). Building on advances in data analytics and computational power this method enables one to combine previous studies, even when they have heterogeneous designs and measures.
We used the BMR problem as an empirical case to apply GMA. Using only the models available from the literature, we estimated a new model that takes into account all the different body measures considered in prior studies for estimating GMA. Then, on a separate dataset, we compared our equation’s predictive power against older equations as well as state-of-the-art equations used by the World Health Organization and Institute of Medicine.
Our equation outperformed all other equations available, including the more recent ones.