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Bayesian Data Augmentation for Recurrent Events under Intermittent Assessments in Overlapping Intervals

Liu, Xin
http://orcid.org/0000-0001-9278-9237
http://rave.ohiolink.edu/etdc/view?acc_num=osu1700870740534545

Abstract Details

2023, Doctor of Philosophy, Ohio State University, Biostatistics.
Electronic medical records (EMR) data contain rich information that can facilitate health-related studies but are 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 analyzing intermittently assessed interval-censored recurrent events assume disjoint assessment intervals (interval count data) due to a focus on prospective studies with controlled assessment times. We propose two Bayesian data augmentation methods to analyze the complicated assessments in EMR data for recurrent events. In a Gibbs sampler, the first method imputes exact event times by rejecting simulations of non-homogeneous Poisson process times that are incompatible with the assessments. Based on the independent increments property of Poisson processes, we implement a series of efficiency improvement techniques to speed up the rejection sampling. The second method applies a random walk reversible jump MCMC algorithm (RJMCMC) where the new event history is proposed by perturbing a previously accepted event history with birth, death, and jitter moves.
Patrick Schnell (Advisor)
Guy Brock (Committee Member)
Matthew Pratola (Committee Member)
Michael Pennell (Committee Member)
Ajit Chaudhari (Committee Member)
227 p.
Biostatistics
Bayesian data augmentation; Recurrent events; Electronic medical records; Intermittent assessments; Interval censoring; Poisson processes; Renewal processes; Rejection sampling; Reversible jump MCMC

Recommended Citations

Citations

  • Liu, X. (2023). Bayesian Data Augmentation for Recurrent Events under Intermittent Assessments in Overlapping Intervals [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1700870740534545

    APA Style (7th edition)

  • Liu, Xin. Bayesian Data Augmentation for Recurrent Events under Intermittent Assessments in Overlapping Intervals. 2023. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1700870740534545.

    MLA Style (8th edition)

  • Liu, Xin. "Bayesian Data Augmentation for Recurrent Events under Intermittent Assessments in Overlapping Intervals." Doctoral dissertation, Ohio State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=osu1700870740534545

    Chicago Manual of Style (17th edition)

osu1700870740534545
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© 2023, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.