A multivariable approach for risk markers from pooled molecular data with only partial overlap.

ABSTRACT:

Background

Increasingly, molecular measurements from multiple studies are pooled to identify risk scores, with only partial overlap of measurements available from different studies. Univariate analyses of such markers have routinely been performed in such settings using meta-analysis techniques in genome-wide association studies for identifying genetic risk scores. In contrast, multivariable techniques such as regularized regression, which might potentially be more powerful, are hampered by only partial overlap of available markers even when the pooling of individual level data is feasible for analysis. This cannot easily be addressed at a preprocessing level, as quality criteria in the different studies may result in differential availability of markers - even after imputation.

SUBMITTER: Stelzer AS 

PROVIDER: S-EPMC6642584 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature