TSNP

"TSNP" (Two-Stage NonParametric) is a software tool for phase I/II clinical trial design, mainly focused on identifying the optimal biological dose (OBD) of immunotherapies. This tool employs a nonparametric approach, setting it apart from traditional Bayesian parametric designs commonly used in dose-finding studies. TSNP's methodology is centered around deriving closed-form estimates of joint toxicity-efficacy response probabilities under a monotonic increasing constraint for toxicity outcomes. These estimates are crucial in assessing the immunotherapy's toxicity-efficacy profiles at various doses, guiding the dose-finding process.

### Key Features and Approach:

1. Two-Stage Design: TSNP implements a two-stage clinical trial design: - First Stage: Focuses on exploring the toxicity profile of the immunotherapy to ensure patient safety.
- Second Stage: Aim to identify the OBD that balances toxicity and efficacy optimally, employing a utility function that considers both outcomes.

2. Nonparametric Estimation: Using nonparametric methods for estimating the joint toxicity-efficacy response probabilities allows for a flexible and transparent approach that does not rely heavily on assumptions about the data distribution.

3. Closed-Form Estimates: The ability to derive closed-form estimates for response probabilities simplifies the computational process and enhances the practical appeal of the TSNP design by making it more straightforward and efficient.

4. Superior Operating Characteristics: Simulation studies have demonstrated that TSNP provides superior operating characteristics, including better performance in identifying the OBD, compared to existing Bayesian parametric designs.

5. User-Friendly Software: To support its application in real-world clinical trials, TSNP is accompanied by user-friendly computational software. This software facilitates the implementation of the TSNP design, making it accessible to trial designers and researchers.

6. Comprehensive Illustrations and Examples: The tool provides detailed illustrations and examples, helping users understand how to implement the proposed design effectively in clinical trials.