Is mixed effects modeling or naive pooled data analysis preferred for the interpretation of single sample per subject toxicokinetic data?

被引:22
作者
Hing, JP
Woolfrey, SG
Greenslade, D
Wright, PMC
机构
[1] Univ Newcastle Upon Tyne, Dept Anaesthesia, Newcastle Upon Tyne NE1 4LP, Tyne & Wear, England
[2] Sanofi, Alnwick Res Ctr, Alnwick, Northd, England
关键词
computer simulations; toxicokinetics; destructive sampling; research study design; mixed effects model;
D O I
10.1023/A:1011507100493
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
The purpose of this study,vas to evaluate whether mixed effects modeling (MEM) performs better than either noncompartmental or compartmental naive pooled data (NPD) analysis for the interpretation of single sample per subject pharmacokinetic (PK) data. Using PK parameters determined during a toxicokinetic study in rats, we simulated data sets that might emerge from similar experiments. Data sets were simulated with varying numbers of animals at each sampling lime (4-48) and the number of samples taken (1-3) from each individual. Each data set was replicated 50 times and analyzed using several variations of MEM that differed in the assumptions made regarding intraindividual 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 sample per subject data hut MEM gave less biased estimates under the simulated conditions used here. MEM performance increased when covariate effects,were sought in the analysis compared with analyses seeking only PK parameters. Decreasing the number of animals used per sampling time from 48 to 16 did not influence the quality of parameter estimates but further reductions (< 16 animals per sampling time) resulted in a reduced proportion of acceptable estimates. Parameter estimate quality improved and worsened with MEM and NPD, respectively, when additional samples were obtained from each individual. Assumptions made regarding the magnitude of intraindividual error were unimportant with single sample per subject data but influenced parameter estimates if more samples were obtained from each individual. MEM is preferable 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.
引用
收藏
页码:193 / 210
页数:18
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