Assessing Treatment Effects with Pharmacometric Models: A New Method that Addresses Problems with Standard Assessments
| Autor: | Estelle Chasseloup, Adrien Tessier, Mats O. Karlsson |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Percentile bias Pharmaceutical Science Datasets as Topic Pain Placebo Data type nonlinear mixed effect models Models Biological Severity of Illness Index Placebos Pharmaceutical Sciences Random Allocation Diabetic Neuropathies Alzheimer Disease Seizures Statistics Humans mixture models Nootropic Agents Mathematics Aged Pain Measurement Aged 80 and over Analgesics Clinical Trials as Topic type I error Random assignment drug effect Middle Aged Farmaceutiska vetenskaper Mixture model Clinical trial Treatment Outcome Nonlinear Dynamics Multiple comparisons problem Anticonvulsants Female Type I and type II errors Research Article |
| Zdroj: | The AAPS Journal |
| ISSN: | 1550-7416 |
| Popis: | Longitudinal pharmacometric models offer many advantages in the analysis of clinical trial data, but potentially inflated type I error and biased drug effect estimates, as a consequence of model misspecifications and multiple testing, are main drawbacks. In this work, we used real data to compare these aspects for a standard approach (STD) and a new one using mixture models, called individual model averaging (IMA). Placebo arm data sets were obtained from three clinical studies assessing ADAS-Cog scores, Likert pain scores, and seizure frequency. By randomly (1:1) assigning patients in the above data sets to “treatment” or “placebo,” we created data sets where any significant drug effect was known to be a false positive. Repeating the process of random assignment and analysis for significant drug effect many times (N = 1000) for each of the 40 to 66 placebo-drug model combinations, statistics of the type I error and drug effect bias were obtained. Across all models and the three data types, the type I error was (5th, 25th, 50th, 75th, 95th percentiles) 4.1, 11.4, 40.6, 100.0, 100.0 for STD, and 1.6, 3.5, 4.3, 5.0, 6.0 for IMA. IMA showed no bias in the drug effect estimates, whereas in STD bias was frequently present. In conclusion, STD is associated with inflated type I error and risk of biased drug effect estimates. IMA demonstrated controlled type I error and no bias. Supplementary Information The online version contains supplementary material available at 10.1208/s12248-021-00596-8. |
| Databáze: | OpenAIRE |
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