Prediction interval

A prediction interval estimates the range in which the true effect of a future study is expected to lie, accounting for uncertainty and variation among studies.

Random-effects model: A statistical model that treats study effects as varying around an overall mean. It estimates the between-study variation that helps determine the interval’s width.
Systematic review: A structured synthesis that identifies and evaluates evidence addressing a focused research question. Prediction intervals help systematic reviews express how much study results may differ.
Credible interval: A Bayesian interval containing a specified proportion of a parameter’s posterior probability. It describes posterior uncertainty about a parameter rather than a frequentist prediction target.
Sampling distribution: The probability distribution of a statistic over repeated samples from a specified population. Its uncertainty component explains why an estimated average effect is not exact.
Between-study heterogeneity: Variation in underlying effects across studies beyond what sampling error explains. Greater heterogeneity widens the range of effects predicted for a future study.
Clinical trial: A study that prospectively evaluates interventions or exposures in human participants. A future trial’s true treatment effect is a direct target of prediction.
Tolerance interval: An interval intended to contain a specified proportion of a population with stated confidence. It targets coverage of a population, unlike prediction of one future study effect.
Variance: A measure of how widely values are dispersed around their mean. The model’s variance components govern expected spread across studies.
Standard error: An estimate of the sampling variability of a statistic across repeated samples. Uncertainty in the estimated mean contributes to prediction interval width.
Treatment effect: The difference in outcomes attributable to an intervention under a specified comparison. The interval can describe plausible treatment effects in a new setting.

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