Abstract Number:
2785
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Gail Potter (1), Allyson Mateja (2), Eric Chu (3), Daniel Rubin (4), David Smith (5), Victor De Gruttola (6)
Institutions:
(1) National Institute of Health / NIAID, Rockville, MD, (2) Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick MD, (3) Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, (4) FDA, Washington, DC, (5) University of California, San Diego, San Diego, CA, (6) Harvard University, Cambridge MA
Co-Author(s):
Allyson Mateja
Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research
Eric Chu
Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research
First Author:
Presenting Author:
Abstract Text:
SARS-CoV-2 viral load is frequently used as an endpoint in Phase 2 COVID-19 treatment trials. Meta-analyses of multiple trials have missingness by design due to different sampling schedules, missingness not by design, and left-truncation at the lower limit of quantification (LLOQ). We compare three viral load imputation models. The first treats infection status as a latent variable. For uninfected people, viral load is represented as a point mass at zero; for infected people, viral load is modeled as lognormally distributed with left-truncation at LLOQ. Hence, the allotment of probability mass <LLOQ to infected and uninfected people is driven by the untestable assumption of log normality of viral load values <LLOQ for infected people. To avoid the need for this assumption, a second approach directly models "<LLOQ" as a point mass and values above LLOQ as lognormally distributed. Temporal correlation is modeled by inclusion of individual-level random intercepts. A third approach involves individual linear interpolation plus random noise estimated from the empirical covariance structure. We compare the models in simulation and apply them to COVID-19 treatment trials in outpatients.
Keywords:
multiple imputation|viral load|longitudinal data|latent variable|mixture model|interpolation
Sponsors:
Biometrics Section
Tracks:
Missing Data
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