mITT vs CACE in Clinical Trials: Cleaning Convenience or Causal Clarity?
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🧪 Modified Intention-to-Treat (mITT): Cleaning for Convenience, at a Cost
🎯 What It Really Estimates
mITT estimates the effect of being assigned and partially engaging with the treatment. It excludes post-randomization non-starters—those who never began treatment or missed key data.
🔍 It sounds tidy, but breaks the causal chain. Why? Because exclusions after randomization violate the principle of exchangeability.
🚨 Why It’s Statistically Dangerous
- Breaks randomization: Removing patients post-randomization reshuffles the group characteristics in an uncontrolled way.
- Selection bias: Patients who start treatment may differ systematically (healthier, more motivated).
- Unclear interpretation: Is it treatment effect or a byproduct of selective inclusion?
🔍 Secret Insight: mITT may look like "clean" data, but it's like cleaning a randomized poker game by discarding hands you don’t like. You’re now playing a different game.
🧬 Complier Average Causal Effect (CACE): Modeling Reality, Respecting Randomization
🎯 What It Really Estimates
CACE aims to estimate what the treatment does to people who would comply no matter what—those who would take treatment if assigned and not if not assigned.
🔬 It isolates the causal effect among “baseline compliers” using instrumental variable (IV) techniques or principal stratification.
🛠 How It Works
- Everyone stays in the dataset—no one is removed.
- Mathematical models estimate a latent subgroup (the compliers).
- CACE = ITT effect ÷ Proportion of compliers.
🧠 This protects the exchangeability granted by randomization while honing in on realistic adherence.
✅ Summary: Core Differences
| Aspect | ITT | mITT | CACE |
| Keeps all randomized? | ✅ Yes | ❌ No | ✅ Yes (but re-focuses) |
| Preserves randomization? | ✅ Yes | ❌ No | ✅ Yes |
| Excludes anyone? | No | Yes—by design | No (but focuses analysis on compliers) |
| Causal interpretation? | Yes (policy level) | No (biased) | Yes (compliance-adjusted) |
📊 Head-to-Head: mITT vs CACE
| Feature | mITT | CACE |
| Keeps everyone? | ❌ No | ✅ Yes |
| Preserves randomization? | ❌ No | ✅ Yes |
| Causal interpretation? | ❌ Biased | ✅ Yes, for compliers |
| Method basis | Exclusions, convenience | IV/Principal stratification |
| Real-world insight | 🚫 Limited | ✅ High clinical fidelity |
🧠 Visual Analogy: Trial as a Gym
| Population | Analytic View | Method |
| Everyone invited | ITT | Full RCT |
| Only who entered | mITT | Post-hoc cut |
| Those who’d always comply | CACE | IV modeling |
🎯 Sentence Practice
Try completing this:
“I want to estimate the effect of [intervention] in patients who…”
- “...are offered treatment” → Use ITT
- “...start the treatment” → Use mITT (beware!)
- “...would comply if assigned” → Use CACE
🔍 Secret Insight Sidebar
- mITT may be accepted by journals, but not defensible as causal inference.
- CACE is rigorously causal, but needs assumptions (no defiers, exclusion restriction).
- Don’t confuse mITT cleanliness for clarity. It filters out more than data—it filters out meaning.
✅ Key Takeaways
- mITT introduces bias—convenient but not causal.
- CACE keeps the rigor of ITT while adjusting for real-world compliance.
- Use CACE when you care about what works for patients who follow the plan.
- Preserve randomization at all costs—don’t toss out the experiment with the dropout.