Risk prediction with imperfect survival outcome information from electronic health records

Jue Hou, Stephanie F. Chan, Xuan Wang, Tianxi Cai

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Readily available proxies for the time of disease onset such as the time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor proxies. Due to the lack of detailed documentation and labor intensiveness of manual annotation, it is often only feasible to ascertain for a small subset the current status of the disease by a follow-up time rather than the exact time. In this paper, we aim to develop risk prediction models for the onset time efficiently leveraging both a small number of labels on the current status and a large number of unlabeled observations on imperfect proxies. Under a semiparametric transformation model for onset and a highly flexible measurement error model for proxy onset time, we propose the semisupervised risk prediction method by combining information from proxies and limited labels efficiently. From an initially estimator solely based on the labeled subset, we perform a one-step correction with the full data augmenting against a mean zero rank correlation score derived from the proxies. We establish the consistency and asymptotic normality of the proposed semisupervised estimator and provide a resampling procedure for interval estimation. Simulation studies demonstrate that the proposed estimator performs well in a finite sample. We illustrate the proposed estimator by developing a genetic risk prediction model for obesity using data from Mass General Brigham Healthcare Biobank.

Original languageEnglish (US)
Pages (from-to)190-202
Number of pages13
Issue number1
StatePublished - Mar 2023

Bibliographical note

Funding Information:
Authors Jue Hou and Stephanie F. Chan made equal contributions to the paper and are co-first authors.

Publisher Copyright:
© 2021 The International Biometric Society.


  • current status data
  • measurement error
  • risk prediction
  • semisupervised learning

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural


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