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Comparison of Seven Non-Linear Mixed Effect Model-Based Approaches to Test for Treatment Effect
Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated with high power, but sometimes at the cost of inflated type I error. Approaches to overcome this problem were published recently, such as model-averaging across drug models (MAD), individual model-ave...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959233/ https://www.ncbi.nlm.nih.gov/pubmed/36839782 http://dx.doi.org/10.3390/pharmaceutics15020460 |
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author | Chasseloup, Estelle Karlsson, Mats O. |
author_facet | Chasseloup, Estelle Karlsson, Mats O. |
author_sort | Chasseloup, Estelle |
collection | PubMed |
description | Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated with high power, but sometimes at the cost of inflated type I error. Approaches to overcome this problem were published recently, such as model-averaging across drug models (MAD), individual model-averaging (IMA), and combined Likelihood Ratio Test (cLRT). This work aimed to assess seven NLMEM approaches in the same framework: treatment effect assessment in balanced two-armed designs using real natural history data with or without the addition of simulated treatment effect. The approaches are MAD, IMA, cLRT, standard model selection (STDs), structural similarity selection (SSs), randomized cLRT (rcLRT), and model-averaging across placebo and drug models (MAPD). The assessment included type I error, using Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores from 817 untreated patients and power and accuracy in the treatment effect estimates after the addition of simulated treatment effects. The model selection and averaging among a set of pre-selected candidate models were driven by the Akaike information criteria (AIC). The type I error rate was controlled only for IMA and rcLRT; the inflation observed otherwise was explained by the placebo model misspecification and selection bias. Both IMA and rcLRT had reasonable power and accuracy except under a low typical treatment effect. |
format | Online Article Text |
id | pubmed-9959233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99592332023-02-26 Comparison of Seven Non-Linear Mixed Effect Model-Based Approaches to Test for Treatment Effect Chasseloup, Estelle Karlsson, Mats O. Pharmaceutics Article Analyses of longitudinal data with non-linear mixed-effects models (NLMEM) are typically associated with high power, but sometimes at the cost of inflated type I error. Approaches to overcome this problem were published recently, such as model-averaging across drug models (MAD), individual model-averaging (IMA), and combined Likelihood Ratio Test (cLRT). This work aimed to assess seven NLMEM approaches in the same framework: treatment effect assessment in balanced two-armed designs using real natural history data with or without the addition of simulated treatment effect. The approaches are MAD, IMA, cLRT, standard model selection (STDs), structural similarity selection (SSs), randomized cLRT (rcLRT), and model-averaging across placebo and drug models (MAPD). The assessment included type I error, using Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores from 817 untreated patients and power and accuracy in the treatment effect estimates after the addition of simulated treatment effects. The model selection and averaging among a set of pre-selected candidate models were driven by the Akaike information criteria (AIC). The type I error rate was controlled only for IMA and rcLRT; the inflation observed otherwise was explained by the placebo model misspecification and selection bias. Both IMA and rcLRT had reasonable power and accuracy except under a low typical treatment effect. MDPI 2023-01-30 /pmc/articles/PMC9959233/ /pubmed/36839782 http://dx.doi.org/10.3390/pharmaceutics15020460 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chasseloup, Estelle Karlsson, Mats O. Comparison of Seven Non-Linear Mixed Effect Model-Based Approaches to Test for Treatment Effect |
title | Comparison of Seven Non-Linear Mixed Effect Model-Based Approaches to Test for Treatment Effect |
title_full | Comparison of Seven Non-Linear Mixed Effect Model-Based Approaches to Test for Treatment Effect |
title_fullStr | Comparison of Seven Non-Linear Mixed Effect Model-Based Approaches to Test for Treatment Effect |
title_full_unstemmed | Comparison of Seven Non-Linear Mixed Effect Model-Based Approaches to Test for Treatment Effect |
title_short | Comparison of Seven Non-Linear Mixed Effect Model-Based Approaches to Test for Treatment Effect |
title_sort | comparison of seven non-linear mixed effect model-based approaches to test for treatment effect |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959233/ https://www.ncbi.nlm.nih.gov/pubmed/36839782 http://dx.doi.org/10.3390/pharmaceutics15020460 |
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