Jp. Hing et al., Is mixed effects modeling or naive pooled data analysis preferred for the interpretation of single sample per subject toxicokinetic data?, J PHARMA PH, 28(2), 2001, pp. 193-210
The purpose of this study,vas to evaluate whether mixed effects modeling (M
EM) performs better than either noncompartmental or compartmental naive poo
led data (NPD) analysis for the interpretation of single sample per subject
pharmacokinetic (PK) data. Using PK parameters determined during a toxicok
inetic study in rats, we simulated data sets that might emerge from similar
experiments. Data sets were simulated with varying numbers of animals at e
ach sampling lime (4-48) and the number of samples taken (1-3) from each in
dividual. Each data set was replicated 50 times and analyzed using several
variations of MEM that differed in the assumptions made regarding intraindi
vidual error, NPD, and a graphical noncompartmental method. These analyses
attempted to retrieve the underlying parameter and covariate effect values.
We compared these analysis methods,vith respect to how well the underlying
values were retrieved. All analysis methods performed poorly with single s
ample per subject data hut MEM gave less biased estimates under the simulat
ed conditions used here. MEM performance increased when covariate effects,w
ere sought in the analysis compared with analyses seeking only PK parameter
s. Decreasing the number of animals used per sampling time from 48 to 16 di
d not influence the quality of parameter estimates but further reductions (
< 16 animals per sampling time) resulted in a reduced proportion of accepta
ble estimates. Parameter estimate quality improved and worsened with MEM an
d NPD, respectively, when additional samples were obtained from each indivi
dual. Assumptions made regarding the magnitude of intraindividual error wer
e unimportant with single sample per subject data but influenced parameter
estimates if more samples were obtained from each individual. MEM is prefer
able to both NPD and noncompartmental approaches for the analysis of single
sample per subject data but even with MEM estimates of clearance are often
biased.