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In clinical studies it is common to assess the minimum drug concentration (C min ) in blood at the steady state. However it is not uncommon to have a small number of assay results that are below the lower limit of quantitation (LLOQ). While the 0<actual C min <LLOQ, some researchers are willing to estimate C min  by the either extreme. Using 0 underestimates the true value and precludes logarithmic transformation of C min  for statistical analysis and calculation of geometric mean. Using LLOQ for imputation results in over‐estimation. In either case the magnitude of bias was unknown. Other methods proposed included imputation with a small quantity (e.g. 12 LLOQ), exclusion of observations<LLOQ, or utilization of non‐parametric statistical analysis without imputation. However, statistical power would be reduced and/or bias introduced if C min  has an approximate log‐normal probability distribution. This abstract proposes two imputation methods when log of C min  follows a normal distribution. The first method derives the mean and variance for values<LLOQ via integration over the probability density function. The second method uses median of the tail probability distribution to impute a magnitude below LLOQ. Estimation based on each of the two methods is provided using a real data set. In this case, the estimated C min  is approximately 60% and 75% of LLOQ using mean and median methods, respectively. Monte Carlo simulation is used to assess the performance of the proposed methods.   Clinical Pharmacology & Therapeutics  (2004)  75 , P42–P42; doi:  10.1016/j.clpt.2003.11.157

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