Cargando…

Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling

PURPOSE: This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer’s disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). METHODS: A baseline IRT model characterizing the ADAS-cog was built based on dat...

Descripción completa

Detalles Bibliográficos
Autores principales: Ueckert, Sebastian, Plan, Elodie L., Ito, Kaori, Karlsson, Mats O., Corrigan, Brian, Hooker, Andrew C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4153970/
https://www.ncbi.nlm.nih.gov/pubmed/24595495
http://dx.doi.org/10.1007/s11095-014-1315-5
_version_ 1782333360174006272
author Ueckert, Sebastian
Plan, Elodie L.
Ito, Kaori
Karlsson, Mats O.
Corrigan, Brian
Hooker, Andrew C.
author_facet Ueckert, Sebastian
Plan, Elodie L.
Ito, Kaori
Karlsson, Mats O.
Corrigan, Brian
Hooker, Andrew C.
author_sort Ueckert, Sebastian
collection PubMed
description PURPOSE: This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer’s disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). METHODS: A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. RESULTS: IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. CONCLUSION: A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11095-014-1315-5) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4153970
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-41539702014-09-04 Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling Ueckert, Sebastian Plan, Elodie L. Ito, Kaori Karlsson, Mats O. Corrigan, Brian Hooker, Andrew C. Pharm Res Research Paper PURPOSE: This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer’s disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). METHODS: A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. RESULTS: IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. CONCLUSION: A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11095-014-1315-5) contains supplementary material, which is available to authorized users. Springer US 2014-03-05 2014 /pmc/articles/PMC4153970/ /pubmed/24595495 http://dx.doi.org/10.1007/s11095-014-1315-5 Text en © The Author(s) 2014 https://creativecommons.org/licenses/by/2.0/ Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
spellingShingle Research Paper
Ueckert, Sebastian
Plan, Elodie L.
Ito, Kaori
Karlsson, Mats O.
Corrigan, Brian
Hooker, Andrew C.
Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling
title Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling
title_full Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling
title_fullStr Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling
title_full_unstemmed Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling
title_short Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling
title_sort improved utilization of adas-cog assessment data through item response theory based pharmacometric modeling
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4153970/
https://www.ncbi.nlm.nih.gov/pubmed/24595495
http://dx.doi.org/10.1007/s11095-014-1315-5
work_keys_str_mv AT ueckertsebastian improvedutilizationofadascogassessmentdatathroughitemresponsetheorybasedpharmacometricmodeling
AT planelodiel improvedutilizationofadascogassessmentdatathroughitemresponsetheorybasedpharmacometricmodeling
AT itokaori improvedutilizationofadascogassessmentdatathroughitemresponsetheorybasedpharmacometricmodeling
AT karlssonmatso improvedutilizationofadascogassessmentdatathroughitemresponsetheorybasedpharmacometricmodeling
AT corriganbrian improvedutilizationofadascogassessmentdatathroughitemresponsetheorybasedpharmacometricmodeling
AT hookerandrewc improvedutilizationofadascogassessmentdatathroughitemresponsetheorybasedpharmacometricmodeling
AT improvedutilizationofadascogassessmentdatathroughitemresponsetheorybasedpharmacometricmodeling