Assessment of non-linear mixed effects model-based approaches to test for drug effect using simulated data: type I error and power properties

Abstract

1AbstractPharmacometric approaches achieves higher power to detect a drug effect compared to traditional statistical hypothesis tests. Known drawbacks come from the model building process where multiple testing and model misspecification are major causes for type I error inflation. IMA is a new approach using mixture models and the likelihood ratio test (LRT) to test for drug effect. It previously showed type I error control and unbiased drug estimates in the context of two-arms balanced designs using real placebo data, in comparison to the standard approach (STD). The aim of this study was to extend the assessment of IMA and STD regarding type I error, power, and bias in the drug effect estimates under various types of model misspecification, with or without LRT calibration. Two classical statistical approaches, t-test and Mixed-Effect Model Repeated Measure (MMRM), were also added to the comparison. The focus was a simulation study where the extent of the model misspecification is known, using a response model with or without drug effect as motivating example in two sample size scenarios.The IMA performances were overall not impacted by the sample size or the LRT calibration, contrary to STD which had better type I error results with the larger sample size and calibrated LRT. In terms of power STD required LRT calibration to outperform IMA. T-test and MMRM had both controlled type I error. The t-test had a lower power than both STD and IMA while MMRM had power predictions similar to IMA. IMA and STD had similarly unbiased drug effect estimates, with few exceptions.IMA showed again encouraging performances (type I error control and unbiased drug estimates) and presented reasonable power predictions. The IMA performances were overall more robust towards model mis-specification compared to STD. IMA confirmed its status of promising NLMEM-based approach for hypothesis testing of the drug effect and could be used in the future, after further evaluations, as primary analysis in confirmatory trials.