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[Subgroup identification based on accelerated failure time model combined with adaptive elastic net].

H Wei1, P Kang1, Y Liu1

  • 1Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.

Nan Fang Yi Ke Da Xue Xue Bao = Journal of Southern Medical University
|April 14, 2021
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Summary

This study introduces a novel strategy using accelerated failure time (AFT) models to identify patient subgroups with improved treatment effects in clinical trials. Penalized AFT models demonstrated superior performance in detecting these subgroups, especially with higher covariate-to-sample size ratios.

Keywords:
accelerated failure time modeladaptive designadaptive elastic netsubgroup identificationsurvival data

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Identifying patient subgroups with differential treatment effects is crucial for personalized medicine in clinical trials.
  • Accelerated failure time (AFT) models offer a flexible framework for analyzing survival data.
  • Adaptive clinical trial designs allow for modifications based on accumulating data, potentially increasing efficiency.

Purpose of the Study:

  • To develop and evaluate a strategy for identifying subgroups with enhanced treatment effects in randomized clinical trials using AFT models.
  • To compare the performance of univariate and penalized AFT models in subgroup identification.
  • To assess the impact of adaptive design parameters on the power to detect treatment effects in subgroups.

Main Methods:

  • Fitted univariate and penalized AFT models (adaptive elastic net regularization) to identify candidate covariates for subgroup classification.
  • Employed a change-point algorithm for patient subgroup classification based on identified covariates.
  • Utilized a two-stage adaptive design to validate treatment effects within identified subgroups.
  • Conducted simulation studies to evaluate model performance under various scenarios, including different covariate correlations and sample size ratios.

Main Results:

  • Penalized AFT models, particularly those including main covariate effects, outperformed univariate models in detecting small treatment effect differences between subgroups.
  • The power of the two-stage adaptive design was maximized with significance level allocations of (α1=0.035, α2=0.015).
  • Model power remained stable despite varying covariate correlations. For a fixed sample size, power decreased as the covariate-to-sample size ratio increased but stabilized above a ratio of 1.
  • Penalized AFT models exhibited more stable parameter distributions across different survival time scenarios compared to univariate models.

Conclusions:

  • The proposed AFT model-based strategy effectively identifies patient subgroups with enhanced treatment effects in clinical trials.
  • Penalized AFT models are recommended for subgroup identification, especially when treatment effect differences are subtle and the covariate-to-sample size ratio is high.
  • The optimal significance level allocation for the two-stage adaptive design provides a valuable reference for future studies.
  • The choice of model (penalized vs. univariate AFT) impacts performance based on the covariate-to-sample size ratio and the magnitude of treatment effect heterogeneity.