Big and disparate data: considerations for pediatric consortia

Abstract

Purpose of review: Increasingly, there is a need for examining exposure disease associations in large, diverse datasets to understand the complex determinants of pediatric disease and disability. Recognizing that children's health research consortia will be important sources of big data, it is crucial for the pediatric research community to be knowledgeable about the challenges and opportunities that they will face. The present review will provide examples of existing children's health consortia, highlight recent pooled analyses conducted by children's health research consortia, address common challenges of pooled analyses, and provide recommendations to advance collective research efforts in pediatric research.

Recent findings: Formal consortia and other collective-science initiatives are increasingly being created to share individual data from a set of relevant epidemiological studies to address a common research topic under the concept that the joint effort of many individual groups can accomplish far more than working alone. There are practical challenges to the participation of investigators within consortia that need to be addressed in order for them to work.

Summary: Researchers who access consortia with data centers will be able to go far beyond their initial hypotheses and potentially accomplish research that was previously thought infeasible or too costly.

Figure 1

Construction of Harmonized Variables Across Disparate Studies using Standardized Terms within an Ontology. Using maternal education as an illustrative example, variable harmonization starts by detailing the response levels of the variable across disparate studies and then mapping to common terms that encompass all of the response levels. These terms then form the response-levels of the new, harmonized variable that can be used in pooled analyses.