A frequentist design for basket trials using adaptive lasso
Lauren Kanapka1, Anastasia Ivanova1
1Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Abstract:
A basket trial aims to expedite the drug development process by evaluating a new therapy in multiple populations within the same clinical trial. Each population, referred to as a "basket", can be defined by disease type, biomarkers, or other patient characteristics. The objective of a basket trial is to identify the subset of baskets for which the new therapy shows promise. The conventional approach would be to analyze each of the baskets independently. Alternatively, several Bayesian dynamic borrowing methods have been proposed that share data across baskets when responses appear similar. These methods can achieve higher power than independent testing in exchange for a risk of some inflation in the type 1 error rate. In this paper we propose a frequentist approach to dynamic borrowing for basket trials using adaptive lasso. Through simulation studies we demonstrate adaptive lasso can achieve similar power and type 1 error to the existing Bayesian methods. The proposed approach has the benefit of being easier to implement and faster than existing methods. In addition, the adaptive lasso approach is very flexible: it can be extended to basket trials with any number of treatment arms and any type of endpoint.
Insights
This study introduces adaptive lasso, a novel frequentist approach for basket trials. It efficiently shares data across patient groups, offering similar power and error rates to Bayesian methods but with enhanced implementation speed and flexibility.
Area of Science:
- Clinical Trials
- Biostatistics
- Pharmacology
Background:
- Basket trials accelerate drug development by testing therapies across diverse patient populations.
- Traditional analysis treats each basket independently, potentially missing cross-basket treatment effects.
- Bayesian dynamic borrowing methods share data but risk type 1 error inflation.
Purpose of the Study:
- To propose a novel frequentist dynamic borrowing method for basket trials.
- To evaluate the performance of adaptive lasso in basket trial analysis.
- To offer a more implementable and faster alternative to existing methods.
Main Methods:
- A frequentist dynamic borrowing approach using adaptive lasso was developed.
- Simulation studies were conducted to compare adaptive lasso with existing methods.
- The method was assessed for power and type 1 error rates.
Main Results:
- Adaptive lasso demonstrated comparable statistical power to Bayesian dynamic borrowing methods.
- The proposed frequentist approach maintained acceptable type 1 error rates.
- Adaptive lasso proved easier to implement and faster than Bayesian alternatives.
Conclusions:
- Adaptive lasso provides an effective frequentist strategy for basket trials.
- This method offers a practical and efficient alternative for analyzing multi-population drug studies.
- The flexibility of adaptive lasso allows for extension to various trial designs and endpoints.
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