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Treatment noncompliance in randomized experiments: statistical approaches and design issues
Brad J Sagarin1, Stephen G West2, Alexander Ratnikov1
1Department of Psychology.
Psychological Methods
|April 30, 2014
Summary
Understanding treatment noncompliance in randomized experiments is crucial for valid causal inference. This review covers 7 statistical approaches, from traditional intention-to-treat to newer methods like complier average causal effect, to address compliance challenges.
Area of Science:
- Biostatistics
- Epidemiology
- Clinical Trials
Background:
- Treatment noncompliance in randomized experiments compromises causal inference and treatment effect interpretability.
- Addressing noncompliance is essential for robust study findings.
Purpose of the Study:
- To provide a nontechnical review of 7 statistical analysis strategies for handling treatment noncompliance.
- To compare traditional and newer approaches in terms of application, assumptions, and estimation.
Main Methods:
- Review of 3 traditional methods: intention-to-treat, as-treated, and per-protocol analysis.
- Review of 4 newer methods: complier average causal effect, dose-response estimation, propensity score analysis, and treatment effect bounding.
Main Results:
- Each method offers a different perspective on treatment effects, ranging from assignment effects to effects in compliers or dose-response relationships.
- Newer methods provide more nuanced estimates by accounting for varying degrees of compliance.
Conclusions:
- The choice of method depends on the research question and the specific assumptions one is willing to make.
- Understanding these diverse analytical strategies enhances the validity and interpretability of findings from randomized experiments with noncompliance.
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