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

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 EMR data for recurrent events. Within a Gibbs sampler, event times are imputed by generating sets of event times from non-homogeneous Poisson processes and rejecting proposed sets that are incompatible with constraints imposed by assessment data. Based on the independent increments property of Poisson processes, we implement three techniques to speed up this rejection sampling imputation method for large EMR datasets: independent sampling by partitioning, truncated generation, and sequential sampling. In a simulation study we show our method accurately estimates parameters of log-linear Poisson process intensities. Although the proposed method can be applied generally to EMR data of recurrent events, our study is specifically motivated by identifying risk factors for falls due to cancer treatment and its supportive medications. We used the proposed method to analyze an EMR dataset comprising 5501 patients treated for breast cancer. Our analysis provides evidence supporting associations between certain risk factors (including classes of medications) and risk of falls.