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Readily available proxies for time of disease onset such as time of the firstdiagnostic code can lead to substantial risk prediction error if performinganalyses based on poor proxies. Due to the lack of detailed documentation andlabor intensiveness of manual annotation, it is often only feasible toascertain for a small subset the current status of the disease by a follow uptime rather than the exact time. In this paper, we aim to develop riskprediction models for the onset time efficiently leveraging both a small numberof labels on current status and a large number of unlabeled observations onimperfect proxies. Under a semiparametric transformation model for onset and ahighly flexible measurement error models for proxy onset time, we propose thesemisupervised risk prediction method by combining information from proxies andlimited labels efficiently. From an initial estimator solely based on thelabelled subset, we perform a one-step correction with the full data augmentingagainst a mean zero rank correlation score derived from the proxies. Weestablish the consistency and asymptotic normality of the proposedsemi-supervised estimator and provide a resampling procedure for intervalestimation. Simulation studies demonstrate that the proposed estimator performswell in finite sample. We illustrate the proposed estimator by developing agenetic risk prediction model for obesity using data from Partners BiobankElectronic Health Records (EHR).

Risk Prediction with Imperfect Survival Outcome Information from Electronic Health Records