Evidence-appraisal glossary
Interim analysis
An interim analysis is a planned examination of trial data before enrollment and follow-up are complete. Independent monitors compare treatment groups partway through to decide whether to continue, stop early for clear benefit or harm, or halt for futility, using pre-specified statistical rules that guard against being misled by early, unstable results.
Also called: Interim look, Interim data analysis, Interim monitoring.
What it is. An interim analysis looks at accumulating outcome data while a trial is still running, usually reviewed by an independent Data Monitoring Committee. Its job is to protect participants (stop if one arm is clearly harmful or clearly superior) and resources (stop for futility when a meaningful benefit looks unachievable).
Why it needs rules. Peeking repeatedly at data inflates the chance of a false-positive, so trials pre-specify how many looks are allowed and spend their "alpha" (error budget) across them, using methods like O'Brien-Fleming or Haybittle-Peto boundaries.
How to use it when reading a study. Check whether interim analyses were pre-planned and boundary methods named. Be cautious with trials stopped early for benefit: they tend to overestimate the treatment effect, especially with few events. Ask whether stopping rules were followed, how many looks occurred, and whether the reported effect size shrinks in later or pooled analyses. Unplanned or post-hoc interim looks weaken confidence.
This is a plain-language methodology definition for reading research. It is general education, not medical advice.