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• W2073287787 abstract "Statistical jargon isn’t particularly useful in everyday medical practice. When was the last time you mentioned P values, t tests, chi-square tests, or confidence intervals in a typical conversation with patients and families? However, I would argue that of the entities named in the previous sentence, the confidence interval (CI) is a concept worth a clinician’s time to understand.A confidence interval describes the range of plausible values for a result, given the information known from the study. Statistically, a 95% CI describes a range of values inside which the answer would lie 95 times, if the study were repeated 100 times. A more clinically useful description is the range within which we can be 95% sure the true population value lies. (Statistical purists cringe when they hear this definition, but it really isn’t that far off.)CIs and P values are mathematical cousins. If we accept statistical significance as a P value of <.05, a 95% CI that includes 0 (for studies looking at the absolute difference between 2 interventions) or 1 (for studies looking at ratios between 2 entities) in its range indicates that the study has not achieved this level of statistical significance.A P value says nothing about the magnitude of the effect being studied. A CI, in contrast, can be used to quantify the best case and worst case for the impact on a patient’s well-being one is likely to achieve. This is what makes it important for patient care.A range of plausible values — that is, a confidence interval — can be calculated for almost any study result, including those for diagnosis, treatment, and prognosis. The mathematical formula for CI involves the number of patients in the study, so that the more patients in a study, the narrower the CI. This makes sense: if a study has a large number of patients, chances are that individual variation and sampling error will affect the result, and the “true” result can be found within a smaller range.How would you use a CI? Let’s look at the article by Moro, et al, which showed that feeding a mixture of prebiotic oligosaccharides to newborns with a strong parental history of allergic disorders reduces the incidence of atopic dermatitis in the first 6 months after birth. They found a statistically significant difference between the rate of AD in the placebo group, 24%, and the rate in the group fed with prebiotics, which was 10%. The number needed to treat (NNT) in this instance is 1/(0.24–0.10), or 7. This NNT means that one would need to give 7 infants the prebiotic mixture to prevent 1 additional case of AD. That’s not a bad return on investment! However, one can calculate a 95% CI for this difference in proportions as ranging from 4 to 27. (For calculating CI for 2 proportions see http://vl.academicdirect.org/applied_statistics/binomial_distribution/ref/CIcalculator.xls.) The clinician may find it useful to consider whether the use of prebiotics is worthwhile if the “truth” is that 27 infants would need to receive this regimen to prevent one additional case of AD. A decision would depend on many other factors, including cost and difficulties in administering the treatment, adverse effects, and family preferences.So, don’t gloss over those CIs the next time you read an article. They give much more information than a P value and allow you to quantify the magnitude of the effect being measured. Below are other resources to learn about CIs." @default.
• W2073287787 created "2016-06-24" @default.
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• W2073287787 date "2007-04-01" @default.
• W2073287787 modified "2023-09-25" @default.
• W2073287787 title "Weighing the Evidence: How Confident Are You with Confidence Intervals? (Hint: You Might Be Missing Something)" @default.
• W2073287787 cites W2104312285 @default.
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• W2073287787 doi "https://doi.org/10.1542/gr.17-4-39" @default.
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