Fisher信息矩阵用于非线性混合效应的多重效应模型：用于的药代动(13)

from the parameter estimates for each method, considering only the subset of datasets fulfilling all convergence conditions.

We were also interested in comparing the distribution of the observed SE provided by each of the estimation methods to the empirical SE and to the predicted SE. In this case, we considered only the subset of datasets for which both the convergence and the variance–covariance matrix of estimation were obtained. For the distribution of the SE provided by the SAEM algorithm, we considered both methods of computation of the SE, the Louis’s principle and the linearization.

2.5 Comparison of results for estimation methods with and without linearization

Using the previous simulations, we also compared the three methods of estimation: FO, FOCE and the SAEM algorithm. For each parameter, the relative bias as well as the relative RMSE were computed for the S datasets fulfilling convergence conditions(S≤1000), which, forΨl, the lth parameter of the population vectorΨ, are given by: Bias(Ψl)

1=S

inserm-00371363, version 1 - 27 Mar 2009

∑

s=1

S

s Ψ0 Ψll 0 Ψ

l

(14)

RMSE(

Ψl)=

(15)

s the estimated value of Ψ for the sthsimulated datasets and Ψ0 the true value. with Ψlll

3 Results

3.1 Comparison of MF with and without linearization

The SE predicted through the use of the SAEM algorithm on a large dataset (SAEM_LI and SAEM_LO) and those predicted by PFIM 3.0 are reported in Table 2 as relative SE, i.e. SE divided by the true value of the parameter, noted RSE and expressed in %. Overall, whatever the method, the RSE of the population parameters were very close for the fixed effects with a difference of at most 1.3% for βC50 between the SE predicted by PFIM and the one given by

2

for SAEM_LO. Regarding the variance parameters, RSE were also very close, except for ωC50

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