Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction

• URL: http://arxiv.org/abs/2105.01264v1
• Date: Tue, 4 May 2021 03:08:51 GMT
• Title: Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction
• Authors: Jue Hou, Zijian Guo and Tianxi Cai
• Abstract summary: We develop a surrogate assisted semi-supervised-learning (SAS) approach to risk modeling with high dimensional predictors. We demonstrate that the SAS procedure provides valid inference for the predicted risk derived from a high dimensional working model.
• Score: 3.10560974227074
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 along 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.

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