Methods: We developed an early stage prototype of Evidence Synthesis Engine (ESE), a software platformthat enables users to compare and synthesize evidence from multiple data sources, to build robust and accurate predictive models to estimate and stratify disease risks, to validate risk equations and to quantify the accuracty of a predictive model in different subpopulations.
Results: We applied the tool to data from Framingham Heart Study, Atherosclerosis Risk in Communities Study, and Cardiovascular Health Study. We demonstrated that users can evaluate the performance of predictive models for different subpopulations in real time. Several metrics were used for model evaluation: cumulative incidence, calibration plots, receiver operating curve and net reclassification index. The prototype also allows users to generate and then assess models for risks of MI, stroke or heart failure, for a predefined subpopulation from selected datasets.
Conclusion: ESE proves to be a useful tool for advancing the development and application of predictive modeling in medicine.