An augmented pseudo likelihood approach for multistate modeling in cross-sectional studies

Music City Center 

Understanding the population-level natural history of chronic diseases is essential for developing effective targeted prevention strategies. Intensity-based multistate analyses estimate transition rates between disease states but often rely on longitudinal data, which may be impractical when resource constraints limit data collection to a cross-sectional sample from the target population. We propose an innovative framework for augmenting cross-sectional data from the target population with auxiliary longitudinal data from other populations with which transition intensity estimates can be identified and estimated. Importantly, this data augmentation approach facilitates the specification of semi-Markov models for a three-state process accommodating recurrent transitions between disease-free and diseased states. The method is evaluated through extensive simulation studies and we apply it to study the population-level natural history of cervical precancer using cross-sectional data from cervical cancer screening studies in two populations.

multistate current status data

portable natural history models

recurrent processes

data integration

semi-Markov models