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Influence of a priori information, designs, and undetectable data on individual parameters estimation and prediction of hepatitis c treatment outcome

Nguyen, THT and Guedj, J and Yu, J and Levi, M and Mentre, F (2013) Influence of a priori information, designs, and undetectable data on individual parameters estimation and prediction of hepatitis c treatment outcome. CPT: Pharmacometrics and Systems Pharmacology.

Abstract

Hepatitis C viral kinetic analysis based on nonlinear mixed effect models can be used to individualize treatment. For that purpose, it is necessary to obtain precise estimation of individual parameters. Here, we evaluated by simulation the influence on Bayesian individual parameter estimation and outcome prediction of a priori information on population parameters, viral load sampling designs, and methods for handling data below detection limit (BDL). We found that a precise estimation of both individual parameters and treatment outcome could be obtained using as few as six measurements in the first month of therapy. This result remained valid even when incorrect a priori information on population parameters was set as long as the parameters were identifiable and BDL data were properly handled. However, setting wrong values for a priori population parameters could lead to severe estimation/prediction errors if BDL data were ignored and not properly accounted in the likelihood function. 2013 ASCPT

Item Type: Article
Additional Information: pubid: 123 nvp_institute: NIBR contributor_address: (Nguyen, Guedj, Mentre) University of Paris Diderot, Sorbonne Paris Cite, Paris, France (Nguyen, Guedj) INSERM, UMR 738, Paris, France (Yu) Novartis Institutes for BioMedical Research, Cambridge, MA, United States (Levi) Novartis Pharmaceutical, East Hanover, NJ, United States (Mentre) AP-HP, Bichat Hospital, Biostatistics Service, F-75018 Paris, France
Date Deposited: 13 Oct 2015 13:12
Last Modified: 13 Oct 2015 13:12
URI: https://oak.novartis.com/id/eprint/21959

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