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Research Medium #intention-to-treat#per-protocol#dropout

ITT vs Per-Protocol

Dot grid of 100 participants showing all analyzed (ITT) vs completers only (PP), with divergent results.

A free, animated itt vs per-protocol you can read here or embed on any website, from Scrollchart.

ITT vs Per-Protocol

ITT vs Per-Protocol Analysis100 enrolled, 20 dropout. Which participants are included in analysis?RandomizedN=100Intervention (n=50)Control (n=50)Intention-to-Treat (ITT)All 100 analyzedDropouts counted in original armPros:Preserves randomizationConservative, avoids dropout biasRecommended by regulators (FDA/EMA)Per-Protocol (PP)Only 80 completersDropouts excludedRisk:Breaks randomizationDropout may be non-randomCan overestimate efficacy

Left: enrollment box (N=100) splitting to intervention (50) and control (50) dot grids, with 10 gray dots per arm representing dropouts. Right: two result boxes - ITT (all 100, RR=1.35, p=0.08 not significant) vs PP (80 completers, RR=1.62, p=0.01 significant). Explanation of divergence.

Good for

  • Clinical trial analysis explanation
  • Regulatory submission critique
  • Replication crisis discussions

Source & accuracy

This itt vs per-protocol is an editorial illustration built to represent the concept accurately. Where it shows figures, they are typical or representative values chosen to make the relationship clear, not a single underlying dataset. The diagram and its explainer are reviewed and maintained centrally, and updated over time as understanding improves.

Following the protocol versus following the patients

Intention-to-treat (ITT) analysis includes every enrolled participant in the arm they were randomly assigned to, regardless of whether they actually took the medication or completed the intervention. Per-protocol (PP) analysis counts only those who adhered to the assigned treatment.

ITT requires analyzing more participants in an arm than actually received that treatment, which dilutes the apparent effect but preserves the randomization. PP analyzes a more compliant subset, potentially inflating the effect size because people who adhere to treatment are often healthier or more motivated than those who don't.

When ITT and PP diverge

High dropout or non-adherence rates expose the gap between ITT and PP estimates. If a trial claims drug X is effective but ITT shows barely any benefit while PP shows substantial benefit, that divergence signals that the trial works only for adherent participants. The ITT result is closer to real-world effect because most patients will drop out or skip doses occasionally.

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Reference

What this is
A free, embeddable, animated itt vs per-protocol for any website.
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