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Statistical Methods for Chimeric Antigen Receptor Cell Therapy Outcomes: A Review and Practical Considerations
Soyoung Kim1, Kwang Woo Ahn1, Brent Logan1
1Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin; Center for International Blood and Marrow Transplant Research, Milwaukee, Wisconsin.
Transplantation and Cellular Therapy
|March 19, 2026
Summary
Statistical analysis for chimeric antigen receptor T (CAR-T) cell therapy outcomes requires specialized methods. This study addresses challenges in analyzing CAR-T cell therapy data, offering guidance for accurate interpretation of real-world evidence.
Area of Science:
- Hematology
- Biostatistics
- Oncology
Background:
- Chimeric antigen receptor T (CAR-T) cell therapy is a novel treatment for blood cancers, with increasing research publications.
- Current statistical analyses often adapt methods from hematopoietic cell transplantation (HCT), overlooking key differences between therapies.
- Limited guidance exists for appropriate statistical analysis of CAR-T cell therapy outcomes, especially in real-world settings.
Purpose of the Study:
- To define common outcomes for CAR-T cell therapy studies.
- To propose appropriate statistical approaches for analyzing CAR-T cell therapy data.
- To address statistical challenges unique to CAR-T cell therapy, such as response definition and duration, time origin, censoring, and competing risks.
Main Methods:
- Review and synthesis of statistical methodologies for CAR-T cell therapy outcome analysis.
- Application and illustration of proposed statistical methods using observational data from the Center for International Blood and Marrow Transplant Research (CIBMTR).
- Focus on defining outcomes and addressing challenges like response quantification and long-term follow-up.
Main Results:
- Identification of specific statistical challenges in CAR-T cell therapy data analysis, including defining patient response and quantifying response duration.
- Discussion of unique post-therapy complications in CAR-T cell therapy requiring careful statistical consideration.
- Demonstration of statistical methods using real-world observational data to analyze CAR-T cell therapy outcomes.
Conclusions:
- Standard statistical methods may not fully capture the complexities of CAR-T cell therapy outcomes.
- Development and application of tailored statistical approaches are crucial for accurate interpretation of CAR-T cell therapy research.
- This work provides a framework for robust statistical analysis of CAR-T cell therapy, enhancing the understanding of its efficacy and safety in real-world practice.

