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The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
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The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

High-dimensional variable selection in meta-analysis for censored data.

Fei Liu1, David Dunson, Fei Zou

  • 1IBM T. J. Watson Research Center, Yorktown Heights, New York 10598, USA. feiliu@us.ibm.com

Biometrics
|August 17, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new hierarchical model for selecting important predictors in high-dimensional data from multiple studies. The method efficiently handles censored data and identifies key factors for time-to-event predictions.

Related Experiment Videos

Last Updated: Jun 10, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

  • Biostatistics
  • Computational Biology
  • Genomics

Background:

  • High-dimensional data presents challenges in predictor selection for time-to-event analyses.
  • Existing multistage testing approaches can be inefficient for complex datasets.
  • Integrating data from multiple studies requires addressing study-to-study heterogeneity.

Purpose of the Study:

  • To develop a novel hierarchical model for robust predictor selection in high-dimensional, multi-study settings.
  • To explicitly model and account for study-to-study heterogeneity.
  • To efficiently estimate shrinkage for high-dimensional predictors with censored data.

Main Methods:

  • A hierarchical Bayesian model incorporating an accelerated failure time model for censored data.
  • A Monte Carlo Expectation-Maximization (MC-EM) algorithm for model fitting.
  • Maximum a posteriori (MAP) estimation for sparse predictor selection, inspired by Relevance Vector Machines (RVM).

Main Results:

  • The proposed method demonstrates efficient predictor selection and shrinkage estimation.
  • The approach effectively handles censored data and study heterogeneity.
  • Simulation studies show competitive performance against existing methods.

Conclusions:

  • The hierarchical model provides a powerful and efficient alternative for predictor selection in high-dimensional, multi-study data.
  • The method's intrinsic thresholding property aids in identifying relevant predictors.
  • Applicable to complex biological datasets, such as gene expression data from cancer studies.