What to Do When Medical Evidence Can Only Be Generated Through Routine Data: An Example From Pediatric Oncology

Children undergoing allogeneic stem cell transplantation often receive off-label rituximab treatment for Epstein-Barr virus reactivation, using adult dosing without pediatric evidence. This project aims to develop a clinical decision support tool (CDSS) that provides evidence-based dosing scenarios by analyzing real-world patient data. The CDSS integrates clinical and laboratory data across institutions and includes dynamic models that guide therapy decisions while incorporating feedback to improve over time. It addresses a critical evidence gap for vulnerable populations and represents a scalable approach for individualized care. The project also highlights the need for regulatory and infrastructural adaptations to support data-driven, post-approval evidence generation.

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Publication Details

Journal
Pediatric Blood & Cancer
Published
2025-12-22
DOI
https://doi.org/10.1002/1545-5017.70081
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

What to Do When Medical Evidence Can Only Be Generated Through Routine Data: An Example From Pediatric Oncology

Antje Walz, Dietrich Knoerzer, Birgit Burkhardt
Pediatric Blood & Cancer
Advanced Causal Inference Techniques
article

What to Do When Medical Evidence Can Only Be Generated Through Routine Data: An Example From Pediatric Oncology

Antje Walz, Dietrich Knoerzer, Birgit Burkhardt
article en

Abstract

Children undergoing allogeneic stem cell transplantation often receive off-label rituximab treatment for Epstein-Barr virus reactivation, using adult dosing without pediatric evidence. This project aims to develop a clinical decision support tool (CDSS) that provides evidence-based dosing scenarios by analyzing real-world patient data. The CDSS integrates clinical and laboratory data across institutions and includes dynamic models that guide therapy decisions while incorporating feedback to improve over time. It addresses a critical evidence gap for vulnerable populations and represents a scalable approach for individualized care. The project also highlights the need for regulatory and infrastructural adaptations to support data-driven, post-approval evidence generation.

Pediatric Blood & CancerVol. 73(2)
Roche (Switzerland) (CH), University Hospital Münster (DE), Credit Suisse (Switzerland) (CH)
Openalex Percentile: Top 24%
Advanced Causal Inference Techniques
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