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Flexible design clinical trial methodology in regulatory applications
H M James Hung1, Sue-Jane Wang, Robert O'Neill
1Division of Biometrics I, Office of Biostatistics, OTS, CDER, Food and Drug Administration, Silver Spring, MD 20994-0002, USA. hsienming.hung@fda.hhs.gov
Statistics in Medicine
|February 24, 2011
Summary
Adaptive clinical trial designs, including sample size reassessment and patient selection, are increasingly used. This work discusses challenges in trial sizing and potential biases from adaptive patient selection methods.
Area of Science:
- Clinical Trial Design
- Biostatistics
- Regulatory Science
Background:
- Adaptive and flexible clinical trial designs are gaining traction for pivotal registration trials.
- Sample size reassessment and adaptive patient selection are key adaptive designs appearing in regulatory submissions.
Purpose of the Study:
- To discuss the complexities of trial sizing with sample size reassessment in adaptive designs.
- To examine the impact of sample size reassessment on achieving the desired p-value threshold for clinical significance.
- To identify and present biases associated with adaptive patient selection designs.
Main Methods:
- Discussion of statistical considerations for sample size reassessment at interim trial points.
- Analysis of the influence of adaptive design features on the final p-value and statistical inference.
- Identification of potential biases arising from selecting patient subgroups based on accumulating data.
Main Results:
- Sample size reassessment requires careful planning to appropriately size trials and meet statistical thresholds.
- Adaptive patient selection introduces complexities regarding the definition of the 'overall' population.
- Biases can be incurred when statistical inferences are made on a modified overall population after adaptive selection.
Conclusions:
- Adaptive designs offer flexibility but necessitate thorough evaluation of their impact on trial validity and statistical power.
- Careful consideration of sample size reassessment and adaptive patient selection is crucial for robust clinical trial planning and regulatory success.
- Understanding and mitigating biases in adaptive patient selection is essential for reliable clinical trial outcomes.
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