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Ignorability and Parameter Estimation in Longitudinal Pharmacokinetic Studies

In the analysis of longitudinal pharmacokinetic data, both balanced (equal number of samples per subject) and unbalanced data are used. It is implicitly assumed that the process that caused the missing data can be ignored. A simulation study was performed to determine the effect of ignoring the missing data (i.e., “ignorability”) on the accuracy and precision of parameter estimation in longitudinal pharmacokinetic studies. A two‐compartment model with multiple intravenous bolus inputs was assumed. Subjects with balanced data sets had six samples, and those with unbalanced data had 1 to 5 samples missing (i.e., supplied in a decreasing order from 5 to 1 samples). The proportion of subjects with 1 to 5 samples missing varied from 25% to 75% in a fixed sample size of 100. The effect of ignorability was studied at intersubject variability ranging from 15% to 60% for a drug assumed to be dosed at its elimination half‐life. One hundred replicate data sets of 100 subjects each were simulated for each missing data scenario. The accuracy of parameter estimation was not significantly affected by the amount of ignorable missing data at any given level of variability. However, the precision of parameter estimation was affected by the degree of “missingness.”