Clinical epidemiology and measures of effect

Overview & Definition

A parent hands you an abstract claiming a therapy halves their child's risk of a complication, and you must decide whether to start it. The word "halves" is a relative risk reduction, and on its own it tells you almost nothing useful, because the same 50 percent reduction means a very different absolute benefit when the baseline risk is 2 in 100 than when it is 20 in 100. Clinical epidemiology is the set of methods that turns a study's raw counts into the numbers that drive that decision, and its measures of effect are the vocabulary in which the answer must be given. ^{[1] [8]}

Clinical epidemiology applies epidemiological methods to the questions clinicians ask at the bedside — therapy, harm, prognosis, and aetiology. Its currency is the measure of effect, the statistic that quantifies the strength of the link between an exposure (or a treatment) and an outcome. Two facts about any outcome anchor everything else: incidence counts new cases over time, while prevalence counts existing cases at a single point. A risk is a probability of the outcome, between 0 and 1, while an odds is a ratio of the probability of the event to the probability of no event, which can run from 0 to infinity. ^[8]

The central distinction you must hold is between relative and absolute measures. A relative measure tells you how many times more likely the outcome is in one group than another, while an absolute measure tells you how many extra or fewer events the exposure actually produces. They answer different questions, they can disagree about whether an effect is large, and a report that gives only the relative figure is hiding the more useful number. This page owns the computation and interpretation of these measures; the appraisal process and hierarchy of evidence belong to the evidence-based medicine leaf, and sensitivity, specificity, and likelihood ratios belong to the diagnostic accuracy leaf. ^{[1] [14]}

References14ShowHide

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