Affiliations: Health Informatics Institute, University of South Florida, Tampa, FL, USA
Correspondence:
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Corresponding author: Hye-Seung Lee, Health Informatics Institute, 3650 Spectrum Blvd., Suite 100, University of South Florida, Tampa, FL 33612, USA. E-mail: [email protected].
Abstract: When prediction is a goal, validation utilizing data outside of the prediction effort is desirable. Typically, data is split into two parts: one for a development and one for validation. But this approach becomes less attractive when predicting uncommon events, as it substantially reduces power. When predicting uncommon events within a large prospective cohort study, we propose the use of a nested case-control design, which is an alternative to the full cohort analysis. By including all cases but only a subset of the non-cases, this design is expected to produce a result similar to the full cohort analysis. In our framework, variable selection is conducted and a prediction model is fit on those selected variables in the case-control cohort. Then, the fraction of true negative predictions (specificity) of the fitted prediction model in the case-control cohort is compared to that in the rest of the cohort (non-cases) for validation. In addition, we propose an iterative variable selection using random forest for missing data imputation, as well as a strategy for a valid classification. Our framework is illustrated with an application featuring high-dimensional variable selection in a large prospective cohort study.
Keywords: Nested case-control, high dimensional variable selection, validation, penalized regression, random forest imputation
