  We have developed a strategy for the analysis of newly available binary data
to improve outcome predictions based on existing data (binary or non-binary).
Our strategy involves two modeling approaches for the newly available data, one
combining binary covariate selection via LASSO with logistic regression and one
based on logic trees. The results of these models are then compared to the
results of a model based on existing data with the objective of combining model
results to achieve the most accurate predictions. The combination of model
predictions is aided by the use of support vector machines to identify
subspaces of the covariate space in which specific models lead to successful
predictions. We demonstrate our approach in the analysis of single nucleotide
polymorphism (SNP) data and traditional clinical risk factors for the
prediction of coronary heart disease.
