Cardiovascular research: data dispersion issues

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 tests; 3) sensitivity / specificity; 4) Markov predictors; 5) risk profiles predicted from multiple logistic models. 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 over-dispersion. 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 adjustments made.

Abstract licence: CC BY-NC

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