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A better estimate of lognormal means on pharmacokinetic data
Author(s) -
Zhi H.,
Shen H.
Publication year - 2004
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1016/j.clpt.2003.11.160
Subject(s) - geometric mean , log normal distribution , mathematics , statistics , standard deviation , population , cmax , pharmacokinetics , population mean , geometric standard deviation , medicine , pharmacology , environmental health , estimator
It is well accepted that pharmacokinetic parameters, especially area under the concentration‐time curve (AUC) and maximum concentration (C max ), should be analyzed on the log‐scale with the assumption of lognormality. Currently pharmacokinetic data are typically summarized by arithmetic means and geometric means. A deeper look at the lognormal distribution reveals that they are actually estimating different parameters of the distribution. Arithmetic means are naÏve estimates of population means, while geometric means are plug‐in estimates of population medians. Suppose population means are the primary interest then we propose another procedure to estimate them. This procedure can provide an alternative way to summarize pharmacokinetic data. In fact, we show that this procedure is a more precise estimate for population means, especially when the standard deviation is large. The procedure is compared with the existing ones via simulations and applied to real pharmacokinetic studies. Some simulation results based on a four‐period crossover study are illustrated in the following table, which shows our estimates have much smaller mean squared errors in most cases. (See table) Clinical Pharmacology & Therapeutics (2004) 75 , P42–P42; doi: 10.1016/j.clpt.2003.11.160Regimen MSE OE /MSE AR MSE OE /MSE GMA 0.97 0.79 B 0.96 0.78 C 0.86 0.59 D 0.84 0.55AR‐ Arithmetic means; OE‐Our Estimates; GM‐ Geometric means.