Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Regularized estimation in the accelerated failure time model with high-dimensional covariates.

Jian Huang1, Shuangge Ma, Huiliang Xie

  • 1Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa 52242, USA. jian@stat.uiowa.edu

Biometrics
|September 21, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Systemic inflammatory response index is associated with acute kidney injury following cardiac surgery: A retrospective cohort study using the MIMIC database.

PloS one·2026
Same author

Phosphorylation of STAT3 mediates the induction of cyclooxygenase-2 by cortisol in the human amnion at parturition.

Science signaling·2015
Same author

Hysteroscopy prior to repeat embryo transfer may improve pregnancy outcomes for asymptomatic women with repeated implantation failure.

The journal of obstetrics and gynaecology research·2015
Same author

Fibroblast growth factor 23 and incident CKD in type 2 diabetes.

Clinical journal of the American Society of Nephrology : CJASN·2014
Same author

Fibroblast growth factor-23 and cardiovascular events in CKD.

Journal of the American Society of Nephrology : JASN·2013
Same author

Effects of dietary phosphate restriction and phosphate binders on FGF23 levels in CKD.

Clinical journal of the American Society of Nephrology : CJASN·2013

This study introduces two regularization methods, LASSO and threshold-gradient-directed regularization, for accelerated failure time models. These techniques improve variable selection and estimation in complex datasets with censored data.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Accelerated failure time (AFT) models are crucial for survival data analysis.
  • Handling censored data and multiple covariates presents statistical challenges.
  • Regularization techniques are essential for variable selection and model stability.

Purpose of the Study:

  • To propose and evaluate two regularization methods for AFT models with multiple covariates.
  • To adapt Stute's weighted least squares method for enhanced estimation and variable selection.
  • To provide robust parameter tuning and variance estimation strategies.

Main Methods:

  • Implementation of LASSO and threshold-gradient-directed regularization.
  • Utilizing Stute's weighted least squares with Kaplan-Meier weights for censoring.

Related Experiment Videos

  • Employing V-fold cross-validation and modified Akaike's Information Criterion for tuning.
  • Applying bootstrap methods for variance estimation.

    • Main Results:

    • The proposed regularization methods demonstrate effectiveness in estimation and variable selection for AFT models.
    • The weighted least squares approach facilitates computational feasibility in multi-covariate settings.
    • Simulation studies and a real data example validate the proposed methodology.

    Conclusions:

    • The developed regularization techniques offer a robust approach for analyzing censored survival data with multiple covariates.
    • The methods enhance the performance of accelerated failure time models.
    • This work provides valuable tools for biostatistical and survival data analysis.