Pharmacokinetic modeling of ethyl loflazepate (VictanR) and its main active metabolites

Ba, Burrows; Iliadis, Athanassios; Cano, J. P.

doi:10.1007/bf02367467

Cited by 161 publications

(7 citation statements)

References 8 publications

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“…Indeed, n occupies a linear position in the model and it merits a special consideration in the estimation procedure [18,24].…”

Section: Discussionmentioning

confidence: 99%

“…Further, reconsider the estimation procedure by eliminating the linear parameter n in the non-linear regression [18,24].…”

Section: Discussionmentioning

confidence: 99%

“…In the above expressions, the superscripts − 1 and T denote matrix inversion and transposition, respectively, and · denotes the matrix determinant [17,18].…”

Section: Discussionmentioning

confidence: 99%

“…A particular behaviour was noted for the estimates of n when compared with the K ( and h estimates; n was the less sensitive parameter in respect of the estimation criterion and the type of simulated measurement error. Indeed, n occupies a linear position in the model and it merits a special consideration in the estimation procedure [18,24].…”

Section: Discussionmentioning

confidence: 99%

“…The ML criterion is derived from the classical ML principle [17,18] when several variables are observed. From a geometrical point of view, ML minimizes the area of ellipses tangential to the predicted curve and centred at the observed data.…”

Section: Estimation Criteria and The Binding Modelmentioning

confidence: 99%

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Non-linear regression analysis with errors in both variables: estimation of co-operative binding parameters

Valsami

Iliadis

Macheras

2000

Biopharm. Drug Dispos.

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Four different parameter estimation criteria, the geometric mean functional relationship (GMFR), the maximum likelihood (ML), the perpendicular least-squares (PLS) and the non-linear weighted least squares (WLS), were used to fit a model to the observed data when both regression variables were subject to error. Performances of these criteria were evaluated by fitting the co-operative drug-protein binding Hill model on simulated data containing errors in both variables. Six types of data were simulated with known variances. Comparison of the criteria was done by evaluating the bias, the relative standard deviation (S.D.) and the root-mean-squared error (RMSE), between estimated and true parameter values. Results show that (1) for data with correlated errors, all criteria perform poorly; in particular, the GMFR and ML criteria. For data with uncorrelated errors, all criteria perform equally well with regard to the RMSE. (2) Use of GMFR and ML lead to lower values for S.D. but higher biases compared with WLS and PLS. (3) WLS performs less well when equal dispersion is applied to the two observed variables.

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“…Indeed, n occupies a linear position in the model and it merits a special consideration in the estimation procedure [18,24].…”

Section: Discussionmentioning

confidence: 99%

“…Further, reconsider the estimation procedure by eliminating the linear parameter n in the non-linear regression [18,24].…”

Section: Discussionmentioning

confidence: 99%

“…In the above expressions, the superscripts − 1 and T denote matrix inversion and transposition, respectively, and · denotes the matrix determinant [17,18].…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Estimation Criteria and The Binding Modelmentioning

confidence: 99%

See 3 more Smart Citations