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Adaptive enrichment designs for clinical trials
1Department of Statistics, Stanford University, Stanford, CA 94305, USA.
Abstract:
Modern medicine has graduated from broad spectrum treatments to targeted therapeutics. New drugs recognize the recently discovered heterogeneity of many diseases previously considered to be fairly homogeneous. These treatments attack specific genetic pathways which are only dysregulated in some smaller subset of patients with the disease. Often this subset is only rudimentarily understood until well into large-scale clinical trials. As such, standard practice has been to enroll a broad range of patients and run post hoc subset analysis to determine those who may particularly benefit. This unnecessarily exposes many patients to hazardous side effects, and may vastly decrease the efficiency of the trial (especially if only a small subset of patients benefit). In this manuscript, we propose a class of adaptive enrichment designs that allow the eligibility criteria of a trial to be adaptively updated during the trial, restricting entry to patients likely to benefit from the new treatment. We show that our designs both preserve the type 1 error, and in a variety of cases provide a substantial increase in power.
Insights
Adaptive enrichment designs improve clinical trials by updating patient eligibility criteria during the study. This targets treatments to patients most likely to benefit, enhancing trial efficiency and safety.
Area of Science:
- Clinical trial methodology
- Biostatistics
- Precision medicine
Background:
- Modern medicine increasingly uses targeted therapeutics for disease heterogeneity.
- Current clinical trials often enroll broad patient populations, necessitating post hoc analyses to identify responders.
- This approach can expose non-benefiting patients to adverse effects and reduce trial efficiency.
Purpose of the Study:
- To propose adaptive enrichment designs for clinical trials.
- To allow dynamic modification of eligibility criteria during a trial.
- To restrict trial enrollment to patients predicted to benefit from novel treatments.
Main Methods:
- Development of a class of adaptive enrichment designs.
- Simulation studies to evaluate design performance.
- Analysis of type 1 error preservation and power.
Main Results:
- Proposed designs maintain statistical type 1 error.
- Adaptive enrichment designs demonstrate substantial power increases in various scenarios.
- Improved identification of patient subsets who benefit from targeted therapies.
Conclusions:
- Adaptive enrichment designs offer a more efficient and ethical approach to clinical trials for targeted therapies.
- These designs enhance the precision of identifying patient subgroups likely to respond to novel treatments.
- Implementation can optimize resource allocation and patient safety in drug development.
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