This work investigates how correlations between covariates influence the estimation of their effects in pharmacometric models. The focus is on quantifying the impact on conditional and unconditional covariate effect estimates and assessing the consequences for model interpretation, communication, and dosing recommendations. A theoretical framework was used to describe the mathematical relationship between conditional and unconditional coefficients. This was verified by simulations across a wide range of covariate correlation strengths and relative covariate effect sizes. The practical consequences of misinterpreting conditional effects were evaluated in the context of dose selection and a priori dose individualization. As predicted by theory, covariate correlation had a substantial effect on the conditional covariate coefficient estimates, while unconditional estimates remained stable. Interpreting conditional covariate effects in isolation led to incorrect conclusions about dosing needs and introduced bias and imprecision in individual dose predictions. In contrast, both the complete conditional model and the unconditional model gave accurate predictions when applied appropriately. Unconditional covariate effects offer greater interpretability, making them more suitable for communicating individual covariate impacts in drug labels, publications, and forest plots. We demonstrate that conditional effects are highly sensitive to model context and covariate correlation, making them poor proxies for the unconditional effect, which is often the quantity of interest for dosing and communication. To minimize misinterpretation, unconditional effects should be reported when describing the influence of individual covariates, while the complete conditional model should be used for simulations and exposure predictions. This dual approach can improve clarity and reduce the risk of misunderstanding in model-informed decision-making.
Jonsson et al. (Wed,) studied this question.