Full Covariate Modeling Approach in Population Pharmacokinetics: Understanding the Underlying Hypothesis Tests and Implications of Multiplicity

Abstract

Objective

To clarify the hypothesis tests associated with the full covariate modeling (FCM) approach in population pharmacokinetic (PPK) analysis, investigate the potential impact of multiplicity in PPK analysis, and evaluate simultaneous confidence intervals (SCI) as an approach to control multiplicity.

Methods

Clinical trial simulations were performed using a simple one-compartment PK model. Different numbers of covariates, sample sizes, effect sizes of covariates, and correlations among covariates were explored. The false positive rate (FPR) and power were evaluated.

Results

The FPR for the FCM approach dramatically increases with number of covariates. The chance of incorrectly selecting ≥1 seemingly "clinically relevant" covariates can be increased from 5% to a 40% - 70% range for 10 - 20 covariates. The SCI approach may provide appropriate control of the family-wise FPR, allowing more appropriate decision making. As a result, the power detecting real effects without incorrectly identifying non-existing effects can be greatly improved by the SCI approach compared to the approach in current practice. The performance of the SCI approach is driven by the ratio of sample size to number of covariates. The FPR can be controlled at 5% and 10% using the SCI approach when the ratio was ≥ 20 and 10, respectively.

Conclusion

The FCM approach still lies within the framework of statistical testing, and therefore multiplicity is an issue for this approach. It is imperative to consider multiplicity reporting and adjustments in FCM modeling practice to ensure more appropriate decision making.

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