Researchers in Delhi are conducting a double-blind RCT comparing a new oral antibiotic (Drug A) with standard therapy (Drug B) for community-acquired pneumonia in adults. After randomization of 400 participants, the trial coordinator discovers that 60 participants assigned to Drug A and 40 participants assigned to Drug B were enrolled based on a clerical error in the randomization list—the true randomization sequence was not followed for these 100 patients. The remaining 300 participants were randomized correctly. The investigators are now debating how to analyze the data. Which approach is most consistent with the intention-to-treat (ITT) principle?

Explanation

## Intention-to-Treat (ITT) Analysis: Handling Randomization Protocol Violations ### Definition of ITT **Key Point:** Intention-to-treat analysis includes **all randomized participants in the groups to which they were assigned**, regardless of whether they received the intended treatment, adhered to the protocol, or were randomized correctly. The key principle is **analyze as randomized**, not **analyze as treated**. ### Why ITT Is the Gold Standard **High-Yield:** ITT preserves the **balance of baseline characteristics** achieved by randomization, even if the randomization process itself was flawed. This prevents selection bias that would arise from post-hoc exclusions. ### ITT vs. Per-Protocol Analysis | Aspect | ITT | Per-Protocol | |--------|-----|-------------| | **Includes** | All randomized participants | Only those who adhered to protocol | | **Analyzes by** | Assigned group | Actual treatment received | | **Bias risk** | Lower (preserves randomization) | Higher (introduces selection bias) | | **Statistical power** | May be reduced if non-adherence is high | May appear higher but is spurious | | **Clinical relevance** | Reflects real-world effectiveness | Reflects efficacy under ideal conditions | | **When to use** | Primary analysis (regulatory standard) | Sensitivity/secondary analysis only | ### Application to This Scenario Even though 100 participants were randomized incorrectly: 1. **They were still randomized** (assigned to a group by a random process, albeit with a clerical error) 2. **Excluding them introduces selection bias** — the remaining 300 would no longer be a representative random sample 3. **ITT analysis maintains the intent** of randomization — to balance unknown confounders 4. **The error is documented** — sensitivity analyses can assess its impact **Mnemonic:** **ITT = Include, Treat, Total** - **Include** all randomized participants - **Treat** them according to assigned group (not received) - **Total** analysis preserves randomization balance ### Why Exclusion Is Wrong Excluding the 100 participants would: - Reduce sample size and statistical power - Introduce selection bias (the remaining 300 may differ systematically from the excluded 100) - Violate the randomization principle - Produce biased effect estimates ### Sensitivity Analysis **Clinical Pearl:** After the primary ITT analysis, investigators should perform a **sensitivity analysis** excluding the 100 incorrectly randomized participants to assess whether conclusions change. If results are robust, confidence in the findings increases. **Tip:** In exam questions, when asked about handling protocol violations or randomization errors, the answer is almost always **ITT (analyze as randomized)** unless the question explicitly asks about per-protocol or sensitivity analyses.