Semiparametric Analysis of Interval-Censored Data Subject to Inaccurate Diagnoses with A Terminal Event

2026-01-11

AI Summary

This study addresses the challenge of misclassified disease status in interval-censored data, particularly when diagnostic errors or biomarker measurement bias are compounded by terminal events such as death. The authors propose a novel semiparametric Cox proportional hazards model that explicitly incorporates diagnostic sensitivity and specificity into the modeling framework to account for uncertainty in disease onset times. The approach simultaneously accommodates terminal events and postmortem confirmation of disease status. Parameter estimation is achieved via nonparametric maximum likelihood combined with an efficient EM algorithm, yielding estimators that attain the semiparametric efficiency bound asymptotically. Simulation studies demonstrate favorable finite-sample performance. In an empirical analysis of Alzheimer’s disease, amyloid-beta was found to be significantly associated with AD onset, while tau protein emerged as a predictor of both AD incidence and mortality risk.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Security and Privacy: Data transparency and provenance

Abstract

Interval-censoring frequently occurs in studies of chronic diseases where disease status is inferred from intermittently collected biomarkers. Although many methods have been developed to analyze such data, they typically assume perfect disease diagnosis, which often does not hold in practice due to the inherent imperfect clinical diagnosis of cognitive functions or measurement errors of biomarkers such as cerebrospinal fluid. In this work, we introduce a semiparametric modeling framework using the Cox proportional hazards model to address interval-censored data in the presence of inaccurate disease diagnosis. Our model incorporates sensitivity and specificity of the diagnosis to account for uncertainty in whether the interval truly contains the disease onset. Furthermore, the framework accommodates scenarios involving a terminal event and when diagnosis is accurate, such as through postmortem analysis. We propose a nonparametric maximum likelihood estimation method for inference and develop an efficient EM algorithm to ensure computational feasibility. The regression coefficient estimators are shown to be asymptotically normal, achieving semiparametric efficiency bounds. We further validate our approach through extensive simulation studies and an application assessing Alzheimer's disease (AD) risk. We find that amyloid-beta is significantly associated with AD, but Tau is predictive of both AD and mortality.

Problem

• interval-censored data
• inaccurate diagnosis
• terminal event
• semiparametric analysis
• disease onset

Innovation

• interval-censored data
• imperfect diagnosis
• semiparametric model
• Cox proportional hazards
• EM algorithm