BAYESIAN DATA AUGMENTATION FOR RECURRENT EVENTS UNDER INTERMITTENT ASSESSMENT IN OVERLAPPING INTERVALS WITH APPLICATIONS TO EMR DATA.

ABSTRACT: Electronic medical records (EMR) data contain rich information that can facilitate health-related studies but is collected primarily for purposes other than research. For recurrent events, EMR data often do not record event times or counts but only contain intermittently assessed and censored observations (i.e. upper and/or lower bounds for counts in a time interval) at uncontrolled times. This can result in non-contiguous or overlapping assessment intervals with censored event counts. Existing methods for analyzing intermittently assessed recurrent events assume disjoint assessment intervals with known counts (interval count data) due to a focus on prospective studies with controlled assessment times. We propose a Bayesian data augmentation method to analyze the complicated assessments in

SUBMITTER: Liu X 

PROVIDER: S-EPMC12393837 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature