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Handling intercurrent events and missing data in non-inferiority trials using the estimand framework: A tuberculosis
Sunita Rehal1, Suzie Cro2, Patrick Pj Phillips3
1GlaxoSmithKline, Middlesex, UK.
Clinical Trials (London, England)
|June 6, 2023
Summary
This study proposes principled methods for handling intercurrent events and missing data in non-inferiority clinical trials. The proposed estimand framework and multiple imputation techniques provide statistically rigorous analyses for accurate interpretation.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Pharmaceutical Research
Background:
- The International Council for Harmonisation E9 addendum (2019) provides an estimand framework but lacks guidance for non-inferiority trials, particularly concerning intercurrent events and missing data.
- Principled analysis of non-inferiority studies is challenged by the need to define estimands that appropriately handle intercurrent events and missing values.
Purpose of the Study:
- To propose and demonstrate a framework for defining estimands in non-inferiority clinical trials, addressing intercurrent events and missing data.
- To introduce and evaluate multiple imputation methods for estimating these estimands, including sensitivity analyses.
Main Methods:
- A case study using a tuberculosis clinical trial to define a primary and an additional estimand for non-inferiority.
- Application of the "twofold" fully conditional specification multiple imputation algorithm and reference-based multiple imputation for binary outcomes.
- Comparison of results from proposed methods with the original study's per-protocol and intention-to-treat analyses.
Main Results:
- The proposed estimand framework, utilizing hypothetical and treatment policy strategies, aligns with the ICH E9 addendum.
- Multiple imputation methods, including "twofold" and reference-based approaches with sensitivity analyses, were applied to estimate the defined estimands.
- The analyses using proposed methods were consistent with the original study, indicating a failure to demonstrate non-inferiority.
Conclusions:
- Carefully constructed estimands and appropriate estimation methods offer a more principled and statistically rigorous approach to non-inferiority trial analysis.
- The proposed methodology enhances the accurate interpretation of estimands by utilizing all available information and addressing intercurrent events and missing data.
- The study highlights the importance of robust statistical frameworks for complex clinical trial designs, such as non-inferiority studies.
Keywords:
Binary outcomeestimandsintercurrent eventsmissing datamultiple imputationnon-inferiorityreference-based sensitivity analysissensitivity analysesMore Related Videos
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