Assessing Treatment Effects with Pharmacometric Models: A New Method that Addresses Problems with Standard Assessments

• Published in: The AAPS journal 2021-05, Vol.23 (3), p.63-63, Article 63
• Main Authors: Chasseloup, Estelle, Tessier, Adrien, Karlsson, Mats O.
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - diagnosis; Alzheimer Disease - drug therapy; Analgesics - administration & dosage; Analgesics - pharmacokinetics; Anticonvulsants - administration & dosage; Anticonvulsants - pharmacokinetics; bias; Biochemistry; Biomedical and Life Sciences; Biomedicine; Biotechnology; Care and treatment; Clinical Trials as Topic - statistics & numerical data; Datasets as Topic; Diabetic Neuropathies - complications; Diabetic Neuropathies - diagnosis; Diabetic Neuropathies - drug therapy; drug effect; Female; Humans; Male; Medical research; Medicine, Experimental; Methods; Middle Aged; mixture models; Models, Biological; Nonlinear Dynamics; nonlinear mixed effect models; Nootropic Agents - administration & dosage; Nootropic Agents - pharmacokinetics; Pain - diagnosis; Pain - drug therapy; Pain - etiology; Pain Measurement - statistics & numerical data; Pharmacology/Toxicology; Pharmacy; Placebos - administration & dosage; Placebos - pharmacokinetics; Random Allocation; Research Article; Seizures (Medicine); Seizures - diagnosis; Seizures - drug therapy; Severity of Illness Index; Treatment Outcome; type I error
• ISSN: 1550-7416; 1550-7416
• DOI: 10.1208/s12248-021-00596-8

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.