Methods to Impute and Analyze Data with BLOQ Observations
Estimate AUC and its standard error
estimate AUC with censored maximum likelihood per time point
estimate AUC with Full censored maximum likelihood
estimate AUC with multivariate normal censored maximum likelihood
estimate AUCwith pairwise censored maximum likelihood
impute BLOQ's with various methods
imputing BLOQ's using censored maximum likelihood
imputing BLOQ's with a constant value
imputing BLOQ's using kernel density estimation
imputing BLOQ's using regression on order statistics
simulate data from Beal model with fixed effects
simulate data from Beal model with fixed and random effects
Provides methods for estimating the area under the concentration versus time curve (AUC) and its standard error in the presence of Below the Limit of Quantification (BLOQ) observations. Two approaches are implemented: direct estimation using censored maximum likelihood, and a two-step approach that first imputes BLOQ values using various methods and then computes the AUC using the imputed data. Technical details are described in Barnett et al. (2020), "Methods for Non-Compartmental Pharmacokinetic Analysis With Observations Below the Limit of Quantification," Statistics in Biopharmaceutical Research. <doi:10.1080/19466315.2019.1701546>.