Xu Steven Xu1, Min Yuan2, Hao Zhu3, Yaning Yang4, Hui Wang5, Honghui Zhou1, Jinfeng Xu6, Liping Zhang1, Jose Pinheiro1. 1. Janssen Research & Development, 920 Route 202, Raritan, NJ, 08869, USA. 2. School of Public Health Administration, Anhui Medical University, Hefei, China. 3. Division of Clinical Pharmacology, Office of Clinical Pharmacology, US Food and Drug Administration, Silver Spring, MD, USA. 4. Department of Statistics and Finance, University of Science and Technology of China, Hefei, China. 5. Athenex Inc., Conventus 1001 Main St, Buffalo, NY, 14203, USA. 6. Department of Statistics & Actuarial Science, University of Hong Kong, Hong Kong.
Abstract
AIMS: To clarify the hypothesis tests associated with the full covariate modelling (FCM) approach in population pharmacokinetic analysis, investigate the potential impact of multiplicity in population pharmacokinetic analysis, and evaluate simultaneous confidence intervals (SCI) as an approach to control multiplicity. METHODS: Clinical trial simulations were performed using a simple one-compartment pharmacokinetic 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 modelling practice to ensure more appropriate decision making.
AIMS: To clarify the hypothesis tests associated with the full covariate modelling (FCM) approach in population pharmacokinetic analysis, investigate the potential impact of multiplicity in population pharmacokinetic analysis, and evaluate simultaneous confidence intervals (SCI) as an approach to control multiplicity. METHODS: Clinical trial simulations were performed using a simple one-compartment pharmacokinetic 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 modelling practice to ensure more appropriate decision making.
Authors: D Menon-Andersen; B Yu; R Madabushi; V Bhattaram; W Hao; R S Uppoor; M Mehta; L Lesko; R Temple; N Stockbridge; T Laughren; J V Gobburu Journal: Clin Pharmacol Ther Date: 2011-07-27 Impact factor: 6.875
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