Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction [article]

Jue Hou, Zijian Guo, Tianxi Cai
<span title="2021-05-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Risk modeling with EHR data is challenging due to a lack of direct observations on the disease outcome, and the high dimensionality of the candidate predictors. In this paper, we develop a surrogate assisted semi-supervised-learning (SAS) approach to risk modeling with high dimensional predictors, leveraging a large unlabeled data on candidate predictors and surrogates of outcome, as well as a small labeled data with annotated outcomes. The SAS procedure borrows information from surrogates
more &raquo; ... with candidate predictors to impute the unobserved outcomes via a sparse working imputation model with moment conditions to achieve robustness against mis-specification in the imputation model and a one-step bias correction to enable interval estimation for the predicted risk. We demonstrate that the SAS procedure provides valid inference for the predicted risk derived from a high dimensional working model, even when the underlying risk prediction model is dense and the risk model is mis-specified. We present an extensive simulation study to demonstrate the superiority of our SSL approach compared to existing supervised methods. We apply the method to derive genetic risk prediction of type-2 diabetes mellitus using a EHR biobank cohort.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.01264v1">arXiv:2105.01264v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eonzxzeeefelbh3gjpm6ifdfq4">fatcat:eonzxzeeefelbh3gjpm6ifdfq4</a> </span>
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