A Bayesian approach for PK/PD modeling with PD data below limit of quantification.

A Bayesian approach to handling below limit of quantification (BLOQ) pharmacodynamic (PD) data in pharmacokinetic/pharmacodynamic (PK/PD) modeling is described. The inhibitory sigmoid Emax model is used to illustrate the implementation of the Bayesian approach for modeling BLOQ PD data. Details on how to implement this Bayesian approach via the Markov-chain Monte Carlo (MCMC) technique using WinBUGS software are presented. A simulation study was conducted to evaluate the performance of the proposed Bayesian approach and to compare the Bayesian approach with two other ad hoc approaches: replacing BLOQ data with ½LOQ, and ignoring the BLOQ data. The simulation study indicates that the proposed Bayesian approach performs better than the other two ad hoc approaches and should be considered in practice as a complementary tool for BLOQ data analysis. A case study with real PK/PD data is provided to illustrate the application of the Bayesian approach of handling BLOQ PD data in PK/PD modeling.