Data Dispersion Issues

  • Ton J. Cleophas
  • Aeilko H. Zwinderman


Biological processes are full of variations, and so is clinical research. Statistics can give no certainties, only chances and, consequently, their results are often reported with a measure of dispersion, otherwise called uncertainty. Mostly, standard errors are calculated as a measure for dispersion in the data. For example, in a hypertension study a mean systolic blood pressure after active treatment of 125 mmHg compared to 135 mmHg after placebo treatment may indicate that either the treatment was clinically efficacious or that the difference observed is due to random variation. To answer this the standard errors of the mean results, 5 mmHg each, and a pooled standard error are calculated, √(52 + 52) = 7.07 mmHg. According to the Student’s t-test this result is statistically insignificant: the t-value = (135 – 125)/7.07 = 1.4, and should have been larger than approximately 2. With such a result it is, usually, concluded that the treatment effect is not different from a placebo effect, and that the calculated mean difference is due to random variation, rather than a true treatment effect.


True Treatment Effect Dispersion Issue Arrhythmic Patient Multiple Logistic Model Clinical Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig JG, Moher D, Rennie D, De Vet HC, for the STARD steering group (2003) Education and debate. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ 326:41–44Google Scholar
  2. Cleophas TJ, Zwinderman AH (2009a) Markow modeling. In: Statistics applied to clinical trials, 4th edn. Springer, Dordrecht, pp 212–213CrossRefGoogle Scholar
  3. Cleophas TJ, Zwinderman AH (2009b) Testing clinical trials for randomness. In: Statistics applied to clinical trials, 4th edn. Springer, Dordrecht, pp 355–366CrossRefGoogle Scholar
  4. Gardner MJ (1989) Confidence interval analysis. BMJ Productions, LondonGoogle Scholar
  5. Hojsgaard S, Halekoh U (2005) Overdispersion. Danish Institute of Agricultural Sciences, Copenhagen.
  6. Imbert-Bismut F, Messous D, Thibaut V et al (2004) Intra-laboratory analytical variability of biochemical markers of fibrosis and activity and reference ranges in healthy blood donors. Clin Chem Lab Med 42:323–333PubMedGoogle Scholar
  7. Lesterhuis W, Cleophas TJ (2009) Cardiovascular research: decision analysis using binary partitioning. Perfusion 22:88–91Google Scholar
  8. Levin MD, Van de Bos E, Van Ouwerkerk BM, Cleophas TJ (2008) Uncertainty of diagnostic tests. Perfusion 21:42–48Google Scholar
  9. Moses LE, Shapiro D, Littenberg B (1993) Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations. Stat Med 12:1293–1316PubMedCrossRefGoogle Scholar
  10. Tan M (2003) Describing data, variability and over-dispersion in medical research. In: Lu Y, Fang J (eds) Advanced medical statistics. World Scientific, River Edge, pp 319–332CrossRefGoogle Scholar
  11. Wasson JH, Sox HC, Neff RK, Goldman L (1985) Clinical prediction rules: applications and methodologic standards. N Engl J Med 313:793–799PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Ton J. Cleophas
    • 1
    • 2
  • Aeilko H. Zwinderman
    • 1
    • 3
  1. 1.Applied to Clinical TrialsEuropean Interuniversity College of Pharmaceutical MedicineLyonFrance
  2. 2.Department of MedicineAlbert Schweitzer HospitalDordrechtNetherlands
  3. 3.Department of Biostatistics and EpidemiologyAcademic Medical CenterAmsterdamNetherlands

Personalised recommendations