Biological processes are full of variations
and so are responses to therapy as measured
in clinical research. Estimators of clinical efficacy
are, therefore, usually reported with a
measure of uncertainty, otherwise called dispersion.
This study aimed to review both the
flaws of data reports without measure of dispersion
and those with over-dispersion.
Examples of estimators commonly reported
without a measure of dispersion include:
1) number needed to treat;
2) reproducibility of quantitative diagnostic
4) Markov predictors;
5) risk profiles predicted from multiple
Data with large differences between
response magnitudes can be assessed for
over-dispersion by goodness of fit tests. The c2
goodness of fit test allows adjustment for overdispersion.
For most clinical estimators, the calculation of standard errors or confidence intervals is
possible. Sometimes, the choice is deliberately
made not to use the data fully, but to skip the
standard errors and to use the summary measures
only. The problem with this approach is
that it may suggest inflated results. We recommend
that analytical methods in clinical
research should always attempt to include a
measure of dispersion in the data. When large
differences exist in the data, the presence of
over-dispersion should be assessed and appropriate
Clinical research, uncertainty, standard error, confidence interval, sensitivity, specificity, reproducibility, Markov model, numbers needed to treat, logistic models, risk profiles, over-dispersion, variance inflating factor.
Ton J. Cleophas, Department Statistics, Circulation, Boston MA, c/o Department Medicine, Albert Schweitzer Hospital, Box 444, 3300 AK, Dordrecht, Netherlands. E-mail: firstname.lastname@example.org
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