Advertisement

Pharmaceutisch Weekblad

, Volume 11, Issue 3, pp 76–82 | Cite as

Data-reduction problems in biopharmaceutics and pharmacokinetics

  • J. Zuidema
  • H. J. A. Wynne
Article

Abstract

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. Attention has been given to a distribution-free method for the determinations of confidence intervals based on Wilcoxons test.

Keywords

Biometry Confidence intervals Hypothesis testing Pharmacokinetics Probability Standard deviation Standard error of the mean 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literature

  1. 1.
    Schoolman HM, Bechtel JM, Best WR, Johnson AF. Statistics in medical research: principles versus practices. J Lab Clin Med 1968;71:357–67.PubMedGoogle Scholar
  2. 2.
    Ross DB, Charlotte NC. Use of controls in medical research. JAMA 1951;145:72–5.Google Scholar
  3. 3.
    Badgley RF. An assessment of research methods reported in 103 scientific articles from two Canadian medical journals. Can Med Assoc J 1961;85:246–51.PubMedGoogle Scholar
  4. 4.
    Schor S, Karten J. Statistical evaluation of medical journal manuscripts. JAMA 1961;195:1123–8.Google Scholar
  5. 5.
    Gore SM, Jones IG, Ritter EC. Misuse of statistical methods: critical assessment of articles in BMJ. Br Med J 1977;1:85–7.PubMedGoogle Scholar
  6. 6.
    Glantz SA. Biostatistics: how to detect, correct and prevent errors in medical literature. Circulation 1980;61:1–7.PubMedGoogle Scholar
  7. 7.
    Lionel NDW, Herxheimer A. Assessing reports of therapeutic trials. Br Med J 1970;3:637–40.PubMedGoogle Scholar
  8. 8.
    Feinstein AR. Clinical biostatistics. XXV. A survey of the statistical procedures in general medical journals. Clin Pharmacol Ther 1974;15:97–107.PubMedGoogle Scholar
  9. 9.
    Shuster JJ, Binion J, Moxley J, et al. [Editorial]. Statistical review process. JAMA 1976;235:534–5.Google Scholar
  10. 10.
    Anderson O, Nielsen MK, Erisen PB, Fenger M, Knudsen PJ. Absorption kinetics and steady-state plasma concentrations of two sustained-release preparations. J Pharm Sci 1983;72:158–61.PubMedGoogle Scholar
  11. 11.
    Modderman ESM, Merkus FWHM, Zuidema J, Hilbers HW, Warndorff T. Sex differences in the absorption of dapsone after intramuscular injection. Int J Leprosy 1983;51:359–65.Google Scholar
  12. 12.
    Boyce EG and Nappi JM. Is there significance beyond the t-test? Drug Intell Clin Pharm 1988;22:334–5.PubMedGoogle Scholar
  13. 13.
    Tukey JW. Exploratory data analysis. Washington: Addison Wesley Publishing Company, 1977.Google Scholar
  14. 14.
    Gardner MJ, Altman DG. Confidence intervals rather than P-values: estimation rather than hypothesis testing. Br Med J 1986;292:746–50.Google Scholar
  15. 15.
    Bulpitt CJ. Confidence intervals. Lancet 1987;1:494–7.PubMedGoogle Scholar
  16. 16.
    Katz D, Baptista J, Azer SP, Pike MC. Obtaining confidence intervals for the risk ratio in cohort studies. Biometrics 1978;34:369–71.Google Scholar
  17. 17.
    Steinijans VW, Diletti E. Statistical analysis of bioavailability studies: parametric and non-parametric confidence intervals. Eur J Clin Pharmacol 1983;24:127–36.PubMedGoogle Scholar

Copyright information

© Bohn, Scheltema & Holkema 1989

Authors and Affiliations

  • J. Zuidema
    • 1
  • H. J. A. Wynne
    • 2
  1. 1.Department of BiopharmaceuticsUniversity of UtrechtAD Utrechtthe Netherlands
  2. 2.Centre for BiostatisticsUniversity of UtrechtCH Utrechtthe Netherlands

Personalised recommendations