Concept Architecture
Concept
Theoretically, Basket Trial is a clinical trial design that evaluates the effectiveness of a single therapeutic intervention across multiple diseases or tumour types that share a common molecular, genetic or biomarker-defined characteristic. The concept is founded on precision medicine, Bayesian statistical inference and adaptive clinical trial methodology. It exists to determine whether a targeted therapy demonstrates efficacy across biologically related populations irrespective of traditional disease classification.
Mathematically, Basket Trial designs are commonly represented using Bayesian hierarchical models that allow information to be shared across baskets while accounting for potential differences in treatment effects between disease subgroups. The statistical framework estimates both basket-specific and overall treatment effects through partial pooling, improving estimation efficiency while controlling borrowing between heterogeneous populations.
In practice, Basket Trials enrol participants into separate disease-specific baskets defined by a shared biomarker or molecular alteration. Treatment responses are analysed within each basket and, where appropriate, jointly across baskets using pre-specified statistical models. Basket trials are widely used in oncology drug development and increasingly provide clinical effectiveness evidence that informs subsequent health technology assessment and economic evaluation.
Purpose
Used to evaluate the effectiveness of a targeted intervention across multiple diseases sharing a common biological characteristic, improving the efficiency of evidence generation for precision medicine.
Mathematical Formulae
Primary Formula
Bayesian hierarchical model:
?? ~ N(?, ��)
where:
- ?? = treatment effect for basket i
- ? = overall mean treatment effect
- �� = between-basket variance
Supporting Formulae
Likelihood:
y? ~ N(??, �?�)
Posterior distribution:
P(? � Data) ? P(Data � ?) ? P(?)
Related Mathematical Methods
- Bayesian Hierarchical Modelling
- Bayesian Inference
- Partial Pooling
- Posterior Probability Estimation
- Adaptive Trial Design
- Response Rate Estimation
- Interim Analysis
Example
A precision oncology study evaluates a kinase inhibitor in patients with lung, colorectal, thyroid and biliary tract cancers, all of which possess the same actionable gene fusion. Separate response rates are estimated for each basket while a Bayesian hierarchical model shares information across tumour types. The posterior probability that the objective response rate exceeds the predefined efficacy threshold is calculated for each basket to determine whether further clinical development is justified. The resulting effectiveness estimates subsequently inform cost-effectiveness modelling for each indication.
Excel Implementation
| Function | Example Formula | Health Economics Application |
|---|---|---|
| AVERAGE | =AVERAGE(B2:B10) | Calculate mean treatment response across baskets. |
| STDEV.S | =STDEV.S(B2:B10) | Estimate between-basket variability. |
| IF | =IF(B2>=0.30,"Promising","Not Promising") | Apply response thresholds for individual baskets. |
| COUNTIFS | =COUNTIFS(C2:C100,"Responder") | Count responders within each basket. |
| SUM | =SUM(D2:D100) | Calculate total responses across all baskets. |
VBA (Optional)
VBA can automate basket-level response summaries, interim monitoring reports and preparation of datasets for Bayesian hierarchical analysis.
Sources
- Woodcock J, LaVange LM. Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both. N Engl J Med.
- Berry SM, Connor JT, Lewis RJ. The Platform Trial: An Efficient Strategy for Evaluating Multiple Treatments. JAMA.
- FDA. Master Protocols: Efficient Clinical Trial Design Strategies to Expedite Development of Oncology Drugs and Biologics.
- EMA. Guideline on the Clinical Evaluation of Anticancer Medicinal Products.
- NICE. Health Technology Evaluation Manual.
- ISPOR Good Practice Reports.
Related Concepts (2)
Library
Publications
1
Economic Evaluation in Clinical Trials — Glick, Doshi, Sonnad & Polsky, 2nd Edition ed., 2015 (Oxford University Press)
Practical guidance on conducting cost-effectiveness analyses alongside controlled trials, covering trial design, measurement of costs and quality-adjusted life years, handling censored and missing data, and reporting stochastic uncertainty. Volume 4 in the Handbooks in Health Economic Evaluation series.
BookView source →
Frequently Asked Questions (6)
What is a basket trial?
A trial design evaluating a single treatment, often targeting a specific genetic marker, across multiple disease types simultaneously, rather than one disease.
Source: Woodcock & LaVange 2017
What does a basket trial test across different diseases?
A basket trial tests a single treatment, usually one aimed at a specific genetic alteration, across patients who have that alteration regardless of where their cancer arose, gathering several tumour types under one protocol. This suits precision medicine, where a therapy targets a molecular feature that can occur in many cancers, since it lets the treatment be studied by the marker it targets rather than by organ of origin. It answers whether the target, not the tumour site, predicts response. It groups patients by biology. Woodcock and LaVange (2017) describe such designs.
Source: Woodcock & LaVange 2017
How does a basket trial work?
A basket trial works by enrolling patients with different diseases that share a common targeted feature, such as a specific genetic mutation, and giving them the same targeted treatment, with each disease type or subgroup forming a basket within the trial. The treatment's effect is assessed within and sometimes across the baskets. This structure allows a treatment aimed at a molecular target to be tested in all the diseases carrying that target within one trial. The design is often used when the targeted feature, rather than the disease site, is thought to determine response.
Source: Woodcock & LaVange 2017
Why are basket trials used?
Basket trials are used because some treatments target a molecular feature that occurs across several diseases, and testing the treatment in all of them together is more efficient than separate trials, especially when the feature is rare in any single disease. This allows patients across diseases who share the target to be studied, accelerating evaluation and making trials feasible for rare molecular subgroups. Basket trials suit the era of targeted and precision therapies, where a genetic or molecular target may predict response better than the disease type, enabling efficient assessment across conditions.
Source: Friedman, Furberg & DeMets 2015
What are the challenges of basket trials?
The challenges of basket trials include the possibility that a treatment targeting the same feature works differently across diseases, so pooling or comparing baskets requires care; small numbers within individual baskets, limiting the strength of conclusions for each disease; and statistical complexity in analysing multiple baskets while controlling error. Interpreting results when some baskets respond and others do not is difficult. These challenges mean basket trials are designed and analysed carefully, with attention to whether effects are consistent across diseases and to the limited evidence any single small basket can provide.
Source: Woodcock & LaVange 2017
How do basket trials relate to precision medicine?
Basket trials relate closely to precision medicine because they test treatments matched to a molecular target across the diseases that share it, embodying the precision-medicine idea that a genetic or molecular feature, rather than the disease site, can determine treatment response. By grouping patients by shared molecular characteristics, basket trials evaluate targeted therapies in the populations most likely to benefit. This makes them a key trial design for developing and assessing precision therapies, particularly in oncology, where targeting specific alterations across cancers is central to the precision-medicine approach.
Source: Woodcock & LaVange 2017
Trust Record
Verified by Dr Darrin Baines
British health economist
Professional identity: darrinbaines.org
Verification date: 12 Nov 2025
Content version: 1.0.0
Canonical Identity
- Persistent URI
- https://healtheconomics.wiki/concept/basket-trial
- Term code
- HE-ES-CTM-006
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