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

TL;DR

This paper presents a computationally efficient method using multistate semi-Markov models to analyze complex biomedical data streams, including interval-censored data, with applications to COVID-19 prophylaxis trials.

Contribution

The authors develop a novel MCEM algorithm with importance sampling for fitting semi-Markov models to interval-censored data, improving computational efficiency and flexibility.

Findings

REGEN-COV reduces asymptomatic infection risk

It shortens viral shedding duration

It lowers seroconversion rates among infected

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.