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Analysis of longitudinal trials with protocol deviation: a framework for relevant, accessible assumptions, and
James R Carpenter1, James H Roger, Michael G Kenward
1a Medical Statistics Department , London School of Hygiene & Tropical Medicine , London , UK.
Journal of Biopharmaceutical Statistics
|October 22, 2013
Summary
Protocol deviations in clinical trials lead to missing data, necessitating careful assumptions for analysis. This study proposes a framework using multiple imputation for longitudinal data to address these challenges, distinguishing between best-case and real-world treatment effects.
Area of Science:
- Clinical Trials Methodology
- Longitudinal Data Analysis
- Biostatistics
Background:
- Protocol deviations are common in clinical trials, leading to missing data and analytical challenges.
- Regulatory guidelines emphasize the importance of clearly stating assumptions for handling missing data in clinical trial analyses.
- Longitudinal quantitative outcome data requires specific approaches to address deviations and missingness.
Purpose of the Study:
- To define and differentiate between 'de jure' (best-case) and 'de facto' (real-world) estimands in the presence of protocol deviations.
- To establish a framework for making contextually appropriate assumptions about pre- and post-deviation data.
- To demonstrate the utility of multiple imputation for estimation and inference under these assumptions.
Main Methods:
- Definition of protocol deviations and their impact on longitudinal data.
- Development of a framework for assumptions regarding the joint distribution of pre- and post-deviation data.
- Application of multiple imputation for handling missing data and performing statistical inference.
- Illustration using data from a chronic asthma clinical trial.
Main Results:
- The proposed framework provides clarity on assumptions needed for analyzing longitudinal data with protocol deviations.
- Multiple imputation is shown to be a practical method for estimation and inference in this context.
- The distinction between de jure and de facto estimands aids in interpreting treatment effects under different scenarios.
Conclusions:
- A structured approach to assumptions and the use of multiple imputation can effectively address missing data from protocol deviations in longitudinal clinical trials.
- This methodology supports robust primary and sensitivity analyses, enhancing the reliability of clinical trial findings.
- The framework is applicable to various clinical trial settings with longitudinal quantitative outcomes.
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