Risk prediction with imperfect survival outcome information from electronic health records

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 f...

Full description

Saved in:
Bibliographic Details
Published in:Biometrics 2023-03, Vol.79 (1), p.190-202
Main Authors: Hou, Jue, Chan, Stephanie F., Wang, Xuan, Cai, Tianxi
Format: Article
Language:English
Subjects:
Algorithms
Annotations
Asymptotic methods
Computer Simulation
current status data
Electronic Health Records
Electronic medical records
Error analysis
Genetic transformation
Health risk assessment
Labels
measurement error
Normality
Prediction models
Proxies
Resampling
Risk
Risk Factors
risk prediction
semisupervised learning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.13599