A novel method based on unbiased correlations tests for covariate selection in nonlinear mixed effects models: The COSSAC approach

Abstract

Building a covariate model is a crucial task in population pharmacokinetics and pharmacodynamics in order to understand the determinants of the interindividual variability. Identifying a good covariate model usually requires many runs. Several procedures have been proposed in the past to automatize this task. The most commonly used is Stepwise Covariate Modeling (SCM). Here, we present a novel stepwise method based on statistical tests between individual parameters sampled from their conditional distribution and the covariates. This strategy, called the COnditional Sampling use for Stepwise Approach based on Correlation tests (COSSAC), makes use of the information contained in the current model to choose which parameter-covariate relationship to try next. This strategy greatly reduces the number of covariate models tested, while retaining on its search path the models improving the log-likelihood (LL). In this article, we detail the COSSAC method and its implementation in Monolix, and evaluate its performance. The performance was assessed by comparing COSSAC to the traditional SCM method on 17 representative data sets. For the large majority of cases (15 out of 17), the final covariate model is identical (11 cases) or very similar (4 cases with LL differences less than 3.84) with both procedures. Yet, COSSAC requires between 2 to 20 times fewer runs than SCM. This represents a decisive speed up, especially for models that take long to run and would not be tractable using the SCM method.