An evaluation of point and interval estimates in population pharmacokinetics using NONMEM analysis

Abstract

In a simulation study of the estimation of population pharmacokinetic parameters, including fixed and random effects, the estimates and confidence intervals produced by NONMEM were evaluated. Data were simulated according to a monoexponential model with a wide range of design and statistical parameters, under both steady state (SS) and non-SS conditions. Within the range of values for population parameters commonly encountered in research and clinical settings, NONMEM produced parameter estimates for CL, V, sigma CL, and sigma epsilon which exhibit relatively small biases. As the range of variability increases, these biases became larger and more variable. An important exception was bias in the estimate for sigma V which was large even when the underlying variability was small. NONMEM standard error estimates are appropriate as estimates of standard deviation when the underlying variability is small. Except in the case of CL, standard error estimates tend to deteriorate as underlying variability increases. An examination of confidence interval coverage indicates that caution should be exercised when the usual 95% confidence intervals are used for hypothesis testing. Finally, simulation-based corrections of point and interval estimates are possible but corrections must be performed on a case-by-case basis.