![]() ![]() In the tested examples this method generated overall unbiased parameter estimates. Best performance was seen when the likelihood of being below LOQ was incorporated into the model. ![]() Omission of BQL data was associated with substantial bias in parameter estimates for all tested models even for seemingly small amounts of censored data. An improved standard for VPCs was suggested to better evaluate simulation properties both for data above and below LOQ. Different approaches for handling of BQL data were compared with estimation of the full dataset for 100 simulated datasets following models A, B, and C. The third model, C, an indirect response model illustrated a case where the variable of interest in some cases decreases below the LOQ before returning towards baseline. Model A was used to represent a case with BQL observations in an absorption phase of a PK model whereas model B represented a case with BQL observations in the elimination phase. Three typical ways in which BQL can occur in a model was investigated with simulations from three different models and different levels of the limit of quantification (LOQ). The purpose of this study is to investigate the impact of observations below the limit of quantification (BQL) occurring in three distinctly different ways and assess the best method for prevention of bias in parameter estimates and for illustrating model fit using visual predictive checks (VPCs). ![]()
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