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New strategies for confirmatory testing of secondary hypotheses on combined data from multiple trials
Marc Vandemeulebroecke1, Dieter A Häring1, Eva Hua2
1Novartis Pharma AG, Basel, Switzerland.
Background:
Pivotal evidence of efficacy of a new drug is typically generated by (at least) two clinical trials which independently provide statistically significant and mutually corroborating evidence of efficacy based on a primary endpoint. In this situation, showing drug effects on clinically important secondary objectives can be demanding in terms of sample size requirements. Statistically efficient methods to power for such endpoints while controlling the Type I error are needed.
Methods:
We review existing strategies for establishing claims on important but sample size-intense secondary endpoints. We present new strategies based on combined data from two independent, identically designed and concurrent trials, controlling the Type I error at the submission level. We explain the methodology and provide three case studies.
Results:
Different strategies have been used for establishing secondary claims. One new strategy, involving a protocol planned analysis of combined data across trials, and controlling the Type I error at the submission level, is particularly efficient. It has already been successfully used in support of label claims. Regulatory views on this strategy differ.
Conclusions:
Inference on combined data across trials is a useful approach for generating pivotal evidence of efficacy for important but sample size-intense secondary endpoints. It requires careful preparation and regulatory discussion.
Insights
Combining data from two clinical trials efficiently demonstrates drug efficacy on secondary endpoints. This approach requires careful planning and regulatory discussion for successful drug development.
Area of Science:
- Clinical trial methodology
- Drug efficacy evaluation
- Statistical inference
Background:
- Establishing drug efficacy typically requires two independent clinical trials with statistically significant primary endpoints.
- Assessing effects on secondary endpoints often demands larger sample sizes, posing challenges for statistical power.
- There is a need for statistically efficient methods to power secondary endpoints while controlling Type I error.
Approach:
- Review of existing strategies for secondary endpoint claims.
- Presentation of novel strategies using combined data from two concurrent, identically designed trials.
- Methodology for controlling Type I error at the submission level.
Key Points:
- A protocol-planned analysis of combined trial data offers a highly efficient strategy for secondary claims.
- This combined data approach has been successfully utilized for supporting drug label claims.
- Regulatory perspectives on the combined data strategy can vary.
Conclusions:
- Combining data across trials is valuable for generating pivotal evidence on important, sample size-intensive secondary endpoints.
- This inferential approach necessitates meticulous preparation and open regulatory dialogue.
- Successful implementation requires careful consideration of statistical power and Type I error control.
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