Data-reduction problems in biopharmaceutics and pharmacokinetics

The importance of the use of appropriate biostatistical methods is stressed. In this article some problems and common errors in the data-reduction methods applied in biopharmaceutical and pharmacokinetic research are discussed. A commonly used representation of a set of concentration-time curves is the so-called 'mean curve', a curve through the arithmetic means of concentrations at discrete time points. If individual curves are compared with the 'mean curve' it appears that important characteristics have disappeared while other, incorrect, characteristics have been created. Unreliable conclusions may result from this procedure. Rather every single concentration-time curve should be fitted by appropriate regression methods and the resulting parameters be considered as multiple characteristics of individual pharmacokinetic behaviour. In a second data-analysis step these parameters may be clustered into more or less homogeneous subgroups, which subsequently may be represented by a representative curve. Standard errors of the mean and confidence intervals based on standard errors of the mean instead of the standard deviation are often misused as dispersion measures to characterize the sample or population distribution. Standard errors of the mean and confidence intervals measure the precision of the mean of a sample and are sensitive to the sample size. Vertical bars (in curves) representing standard deviation, standard errors of the mean or confidence intervals suggest symmetrical distributions, but this is sometimes not justified. Deviations from normality appear to occur often. A simple graphical method to indicate the dispersion of non-normal sets is presented. Methods for the determination of confidence intervals for normal and non-normal distributions are discussed.(ABSTRACT TRUNCATED AT 250 WORDS)