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Updated: Jun 29, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Tree-based exploratory identification of predictive biomarkers in non-randomized data.

Julia Krzykalla¹, Maral Saadati², Wiebke Hielscher³

¹Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.

BMC Medical Research Methodology

|June 27, 2026

Summary

This summary is machine-generated.

Identifying predictive biomarkers in non-randomized studies is crucial for stratified medicine. This research combines the predMOB method with confounder adjustment strategies, showing that a mix of covariate adjustment and Inverse Probability of Treatment Weighting (IPTW) is most effective.

Keywords:

Confounder adjustment Non-randomized data PredMOB Predictive factors Treatment effect modification

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Area of Science:

Biostatistics
Translational Medicine
Clinical Trial Methodology

Background:

Stratified medicine relies on identifying predictive biomarkers.
Existing methods often require randomized data, limiting applicability to real-world clinical registries and non-randomized studies.
Confounder adjustment in treatment effect heterogeneity research typically uses regression models.

Purpose of the Study:

To adapt the predMOB tree-based method for identifying predictive factors in the presence of confounding.
To evaluate strategies for combining predMOB with established confounder adjustment techniques.
To assess the performance of these combined methods in simulation studies and real clinical data.

Main Methods:

Integration of the predMOB algorithm with covariate adjustment.
Integration of the predMOB algorithm with Inverse Probability of Treatment Weighting (IPTW).
Extensive simulation studies to assess the accuracy of predictive factor identification under confounding.

Main Results:

The proposed strategies successfully identify predictive factors even with confounding.
A combination of covariate adjustment and IPTW demonstrated the most robust performance.
The methods were successfully applied to the German Breast Cancer Study Group (GBSG) trial 2 and Acute Myeloid Leukemia Study Group (AMLSG) 16-10 study data.

Conclusions:

The predMOB method can be effectively combined with standard confounder adjustment strategies for biomarker discovery in non-randomized data.
Combined covariate adjustment and IPTW offer a robust approach for identifying predictive factors in the presence of confounding.
These methods enhance the potential for stratified medicine using routinely collected clinical data.