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Randomized test-treatment studies with an outlook on adaptive designs
Amra Hot1, Patrick M Bossuyt2, Oke Gerke3,4
1Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, 20246, Germany. a.hot@uke.de.
BMC Medical Research Methodology
|June 2, 2021
Summary
Randomized test-treatment studies evaluate diagnostic test impact on patient outcomes. Integrating adaptive designs can improve sample size calculations, addressing challenges in complex clinical trials.
Area of Science:
- Clinical Trial Design
- Biostatistics
- Medical Diagnostics
Background:
- Diagnostic accuracy studies assess new tests but not clinical impact.
- Randomized test-treatment studies evaluate diagnostic information's effect on patient outcomes.
- Inconsistent nomenclature exists for various randomized test-treatment study designs.
Purpose of the Study:
- To clarify diverse study designs for randomized test-treatment studies.
- To outline assumptions, benefits, and limitations of each design.
- To guide effect size derivation for sample size calculations and explore adaptive designs.
Main Methods:
- A pre-specified framework was used to describe study designs.
- Analysis considered underlying assumptions, advantages, and limitations.
- An outlook on adaptive designs within randomized test-treatment studies was provided.
Main Results:
- Adaptive designs are crucial for randomized test-treatment studies.
- Sample size calculations often rely on vague assumptions, leading to power issues.
- Sample size re-estimation during trials may improve accuracy.
Conclusions:
- Randomized test-treatment studies present implementation challenges due to complexity and parameter uncertainty.
- Limited research exists on adaptive designs in this context.
- Further research on adaptive designs for randomized test-treatment studies is recommended.
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