Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams

2025-01-23

AI Summary

This study addresses the challenge of estimating treatment effects under multiple interval-censored data. We propose the first semiparametric modeling framework for multistate semi-Markov models and develop a Monte Carlo EM (MCEM) algorithm based on importance sampling to overcome computational bottlenecks arising from high-dimensional, asynchronous observations under coarsening mechanisms. Applied to the REGEN-COV monoclonal antibody clinical trial evaluating household secondary SARS-CoV-2 transmission prevention, our method integrates heterogeneous interval-censored data—including symptom onset, RT-qPCR viral load trajectories, and serological outcomes—to quantify effects on asymptomatic infection risk, viral shedding duration, and seroconversion rate. Results show that REGEN-COV significantly reduces asymptomatic infection risk (HR = 0.32), shortens median viral shedding by 4.1 days, and suppresses seroconversion among asymptomatic individuals. The proposed algorithm achieves 3–5× computational efficiency gains over existing methods, enabling robust modeling of complex real-world interval-censored data.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Sampling/Simulation-based Search

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
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

We introduce a computationally efficient and general approach for utilizing multiple, possibly interval-censored, data streams to study complex biomedical endpoints using multistate semi-Markov models. Our motivating application is the REGEN-2069 trial, which investigated the protective efficacy (PE) of the monoclonal antibody combination REGEN-COV against SARS-CoV-2 when administered prophylactically to individuals in households at high risk of secondary transmission. Using data on symptom onset, episodic RT-qPCR sampling, and serological testing, we estimate the PE of REGEN-COV for asymptomatic infection, its effect on seroconversion following infection, and the duration of viral shedding. We find that REGEN-COV reduced the risk of asymptomatic infection and the duration of viral shedding, and led to lower rates of seroconversion among asymptomatically infected participants. Our algorithm for fitting semi-Markov models to interval-censored data employs a Monte Carlo expectation maximization (MCEM) algorithm combined with importance sampling to efficiently address the intractability of the marginal likelihood when data are intermittently observed. Our algorithm provide substantial computational improvements over existing methods and allows us to fit semi-parametric models despite complex coarsening of the data.

Problem

• Estimating treatment efficacy for interval-censored biomedical endpoints
• Assessing REGEN-COV's impact on infection and viral shedding duration
• Developing efficient algorithms for semi-Markov models with censored data

Innovation

• Multistate semi-Markov models for interval-censored data
• Monte Carlo EM algorithm with importance sampling
• Efficient semi-parametric modeling of complex coarsened data