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Adherence, per-protocol effects, and the estimands framework.
1KeeneONStatistics, Berkshire, UK.
Pharmaceutical Statistics
|July 21, 2023
Summary
The traditional dichotomy of treatment effects in clinical trials, ITT or per-protocol, is insufficient. The ICH E9 (R1) estimands framework offers a better approach for defining treatment effects, considering intercurrent events.
Area of Science:
- Clinical Trials Methodology
- Statistical Analysis in Medicine
- Pharmaceutical Research
Background:
- Traditional statistical literature often simplifies treatment effects in clinical trials to either intention-to-treat (ITT) or per-protocol (PP) effects.
- The concept of 'per-protocol' effect and 'adherence' is frequently confusing, especially for non-statisticians, leading to misinterpretations of trial outcomes.
- Existing definitions of adherence and per-protocol effects do not adequately address the complexities of real-world clinical trial conduct, such as treatment discontinuation or the use of rescue medication.
Purpose of the Study:
- To highlight the limitations of the traditional ITT versus per-protocol dichotomy for describing treatment effects in clinical trials.
- To advocate for the adoption of the ICH E9 (R1) estimands framework for a more precise and clinically relevant definition of treatment effects.
- To emphasize the importance of clearly defining estimands and handling intercurrent events in clinical trial design and analysis.
Main Methods:
- Critically review the statistical literature's definitions and applications of intention-to-treat (ITT) and per-protocol (PP) effects.
- Analyze the ambiguities and clinical irrelevance arising from the simplistic ITT/PP dichotomy.
- Introduce and explain the principles of the ICH E9 (R1) estimands framework for defining treatment effects.
Main Results:
- The traditional ITT and per-protocol effect dichotomy is inadequate for capturing the range of clinically relevant treatment effects.
- The terms 'per-protocol' and 'adherence' are often poorly defined and lead to confusion among researchers and clinicians.
- The ICH E9 (R1) estimands framework provides a structured and comprehensive approach to defining treatment effects by explicitly considering intercurrent events.
Conclusions:
- The statistical literature should move beyond the simplistic ITT versus per-protocol framework for describing treatment effects.
- Implementing the ICH E9 (R1) estimands framework is crucial for improving the clarity and clinical relevance of treatment effect definitions in clinical trials.
- Proper definition and handling of intercurrent events within the estimands framework are essential for robust clinical trial interpretation.
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