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The Added Value of Confidence Intervals

Since its formal introduction in 1991, evidence-based medicine/practice has received considerable attention. Defined as “the conscientious, explicit, and judicious use of best evidence in making decisions about the care of individual patients,”1 evidence-based practice embraces the integration of best research evidence, clinical expertise, and patient values.2 Clinicians are active participants not only in applying their expertise, but also in seeking out and interpreting research evidence. To allow the optimal transfer of information from research report to clinical practice, researchers must present their findings in an easy-to-understand format that provides the maximum amount of information efficiently.

When interpreting the results from studies investigating the merits of competing therapeutic interventions, the reliability or validity of clinical measurements, or the causal association of putative risk factors, clinicians and researchers are interested in the answers to 2 important questions: (1) Are the results likely due to chance? and (2) Are the findings clinically important? The former question considers statistical significance, and the latter question addresses clinical significance.

Statistical significance is dictated by tradition, with a critical P value of .05 typically being the requisite minimal value. Statistical significance is influenced by sample size, sample variability, and the magnitude of the observed effect. In contrast to the arbitrary standard for statistical significance, clinical importance is influenced by personal beliefs, risk of an adverse event, cost, and the feasibility of providing the intervention, test, or measure in practice. Because clinicians—and researchers for that matter—are likely to have different opinions concerning the magnitude of a clinically important difference, it is essential that authors provide their results …