Multiple imputation models for SARS-CoV-2 viral load

Oregon Convention Center 

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.

multiple imputation

viral load

longitudinal data

latent variable

mixture model

interpolation 

Biometrics Section