Evidence map›Paper›PMID 41856319›Full record

ReviewTransplantation and cellular therapy2026

Statistical Methods for Chimeric Antigen Receptor Cell Therapy Outcomes: A Review and Practical Considerations.

Soyoung Kim, Kwang Woo Ahn, Brent Logan, Ruta Brazauskas, Min Chen, Marcelo Pasquini, Amy Moskop

Abstract readReview
In one paragraph

Review in Transplantation and cellular therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Soyoung KimDivision of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin; Center for International Blood and Marrow Transplant Research, Milwaukee, Wisconsin. Electronic address: skim@mcw.edu.
Kwang Woo AhnDivision of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin; Center for International Blood and Marrow Transplant Research, Milwaukee, Wisconsin.
Brent LoganDivision of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin; Center for International Blood and Marrow Transplant Research, Milwaukee, Wisconsin.
Ruta BrazauskasDivision of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin; Center for International Blood and Marrow Transplant Research, Milwaukee, Wisconsin.
Min ChenCenter for International Blood and Marrow Transplant Research, Milwaukee, Wisconsin.
Marcelo PasquiniDepartment of Hematology/Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio.
Amy MoskopCenter for International Blood and Marrow Transplant Research, Milwaukee, Wisconsin.

Funding

Data Resource for Analyzing Blood &Marrow TransplantsU24CA076518 · NCI · MEDICAL COLLEGE OF WISCONSIN · PI Amy M Moskop, Bronwen Shaw · 1998 to 2026
$105.2M
NCI NIH HHS U24 CA076518
6 · The paper itself

Abstract

Chimeric antigen receptor T cell (CAR-T) therapy is being increasingly used as a cutting-edge treatment for blood cancers. Although the number of scientific articles focused on its outcomes is increasing, there is limited literature providing guidance on appropriate statistical analysis methods for CAR-T therapy studies. Statistical approaches applied to CAR-T therapy outcome analysis often use methods adopted from hematopoietic cell transplantation (HCT); however, there are important differences between these therapies that must be accounted for in the outcomes analysis. Additionally, much of the literature on CAR-T therapy outcomes is focused on clinical trials, which differ from real-world settings in the long-term follow-up of these patients. There are some statistical challenges in analyzing CAR-T therapy data, such as defining patients' response to therapy and quantifying the duration of response. Although some outcomes, such as overall survival and treatment-related mortality, are seen in both HCT and CAR-T therapy studies, some post-therapy complications are unique to CAR-T cell therapies, and their statistical analysis should be given careful consideration. Other challenges include establishing the appropriate time origin for each outcome, determining censoring time, and defining competing risks. Here we propose to define common CAR-T therapy outcomes, describe appropriate statistical approaches for their analyses, and address these critical statistical challenges based on the observational data collected by the Center for International Blood and Marrow Transplant Research. We used observational data to illustrate these statistical methods for analyzing CAR-T therapy outcomes. © 2026 American Society for Blood and Marrow Transplantation. Published by Elsevier Inc. All rights reserved.

Indexed as

Immunotherapy, AdoptiveReceptors, Chimeric AntigenHumansTreatment OutcomeReceptors, Chimeric AntigenChimeric antigen receptor T cell therapyStatistical methods

Identifiers

PMID41856319
PMCPMC13171207

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.