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Exploratory subgroup analysis in clinical trials by model selection.

Gerd K Rosenkranz1,2

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Summary
This summary is machine-generated.

This study introduces a resampling method to address selection bias and uncertainty when identifying patient subgroups for individualized medicine. The approach helps validate findings from subgroup analyses in clinical trials.

Keywords:
Bias reductionBootstrapEstimation after selectionSelection biasSelection uncertaintySelective inferenceSubgroup selection

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Pharmacogenomics

Background:

  • Growing demand for individualized medicines necessitates robust subgroup analyses in clinical trials.
  • Regulatory bodies require rigorous assessment of treatment effects within specific patient subgroups.
  • Identifying meaningful subgroups post-hoc presents challenges related to selection bias and uncertainty.

Purpose of the Study:

  • To develop and evaluate a statistical method for subgroup identification that accounts for selection bias and uncertainty.
  • To provide a resampling approach for replicating subgroup discovery processes.
  • To enable reliable effect estimation and variance calculation in subgroup analyses.

Main Methods:

  • A resampling approach is proposed to repeatedly simulate the subgroup discovery process.
  • Replicates are used to adjust effect estimates for selection bias.
  • Variance estimators are developed to incorporate selection uncertainty.

Main Results:

  • A simulation study demonstrated the method's performance in addressing selection bias and uncertainty.
  • The approach provides adjusted effect estimates and reliable variance estimators.
  • An oncology example illustrates the practical application of the method.

Conclusions:

  • The proposed resampling method offers a statistically sound way to validate subgroup findings from clinical trial data.
  • This approach is crucial for reliable individualized medicine development and regulatory submissions.
  • The method enhances the interpretability and trustworthiness of subgroup analyses.