Intention-to-Treat (ITT) vs Modified ITT (mITT) vs Per Protocol (PP) vs As Treated (AT) vs Complier Average Causal Effect (CACE): Choosing the Right Analysis in Clinical Trials

Strategy	Core Logic	Maintains Randomization?	Bias Profile	Best Use Case
Intention-to-Treat (ITT)	Analyze everyone as randomized, regardless of what happened next.	✅ Yes	🔽 Lowest bias (but dilution risk)	Primary analysis for effectiveness (policy relevance, real-world impact).
Modified ITT (mITT)	ITT with defined exclusions (e.g., no post-randomization data at all).	⚠️ Partially	Moderate (selection creep)	When minimal follow-up is missing—pragmatic compromise.
Per Protocol (PP)	Keep only strict adherers, exclude violators.	❌ No	🔼 High (selection bias)	Secondary analysis to explore efficacy under perfect use.
As Treated (AT)	Re-sort patients by what they actually received.	❌ No	🔼 High (confounding)	Exploratory; mimics observational analysis. Not valid for primary inference.
Complier Average Causal Effect (CACE)	Estimates effect among true compliers only.	✅ Yes (under IV assumptions)	Medium	When non-compliance is substantial but symmetric. More causal than AT/PP.

🧠 Interpretive Flowchart

When deciding which analysis to apply, follow this sequence:

Primary aim = effectiveness?→ Use ITT as default .
Large dropout with zero data?→ Consider mITT (must predefine rules, symmetric).
Want efficacy under ideal adherence?→ Add PP as sensitivity.
Major crossover or non-adherence?→ Report ITT but add CACE to salvage causal interpretation.
Tempted by AT?→ Remember: this is essentially an observational re-analysis — use only as exploratory.

IV assumptions behind CACE

When you use randomization as an instrumental variable (IV), four key assumptions must hold:

Relevance: Randomization actually influences treatment received (i.e., assignment matters).
Exclusion restriction: Randomization affects the outcome only through the treatment, not directly.
Independence: Randomization is independent of potential outcomes (held by design in an RCT).
Monotonicity: No “defiers”: no one systematically does the opposite of their assignment.

How it’s calculated

It uses an instrumental variable (IV) framework, with randomization as the instrument:

CACE ≈ ITT effect / Proportion of compliers

Example:

Regulators and CONSORT guidance: ITT must always be presented, even if secondary analyses are added.
Non-Inferiority trials: ITT alone is dangerous (dilution may favor NI); both ITT + PP must agree.
Pragmatic vs Explanatory framing: Pragmatic = ITT; Explanatory = PP. Choice of analysis reflects the clinical stake.
Policy makers vs clinicians: Policy → ITT; Clinicians → may look at PP/AT to understand “what happens if patients really take it”.