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Investigating Stability in Subgroup Identification for Stratified Medicine.

G M Hair¹, T Jemielita¹, S Mt-Isa²

¹BARDS, Merck & Co., Inc, Rahway, New Jersey, USA.

Pharmaceutical Statistics

|June 26, 2024

Summary

Assessing subgroup stability is crucial in stratified medicine. This study introduces a method to evaluate subgroup robustness, finding patient subgroups are less stable when treatment effects are unclear.

Keywords:

Bayesian benefit‐risk subgroup analysis survival data

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Area of Science:

Biostatistics
Clinical Trial Methodology
Pharmacometrics

Background:

Subgroup analyses identify treatment effect heterogeneity.
Existing methods often neglect subgroup assignment stability.
Statistical challenges like multiplicity and bias are common.

Purpose of the Study:

To propose and evaluate a method for assessing subgroup stability in stratified medicine.
To identify factors influencing subgroup instability.
To enhance the robustness of subgroup identification.

Main Methods:

Utilized Bayesian credible subgroups for initial subgroup identification.
Employed nonparametric bootstrap for assessing subgroup stability at patient and subgroup levels.
Proposed subgroup stability as a sensitivity analysis.

Main Results:

Patients were more likely to switch subgroups ('jumpers') with unclear treatment effects.
Subgroups demonstrated greater stability when treatment effects were strong and convincing.
Identified potential drivers of subgroup instability.

Conclusions:

Subgroup stability analysis is essential for validating identified subgroups in stratified medicine.
The proposed method, applicable to various endpoints and identification techniques, enhances the reliability of subgroup findings.
This approach contributes to more robust personalized medicine strategies.