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

Linear regression analysis based on Buckley-James estimating equation.

J S Lin1, L J Wei

  • 1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38101.

Biometrics
|September 1, 1992
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

Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events.

Biometrics·2026
Same author

[Application and progress of artificial intelligence technology in interventional pulmonology].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases·2025
Same author

Progression-free survival as a surrogate endpoint in myeloma clinical trials: an evolving paradigm.

Blood cancer journal·2024
Same author

Advancing Transthyretin Amyloidosis Drug Development in an Evolving Treatment Landscape: Amyloidosis Forum Meeting Proceedings.

Advances in therapy·2024
Same author

[Exploration and contemplation of homogenized education of specialists in pulmonary and critical care medicine at member hospitals of a hospital consortium].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases·2024
Same author

Robust Alternatives to ANCOVA for Estimating the Treatment Effect via a Randomized Comparative Study.

Journal of the American Statistical Association·2023
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study proposes a simple method for regression analysis in linear models with survival data. The Buckley-James estimating equation provides a straightforward approach for drawing inferences.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Linear models are commonly used for regression analysis.
  • Survival data presents unique challenges due to censoring.
  • Accurate inference for regression parameters is crucial in survival analysis.

Purpose of the Study:

  • To propose a simple procedure for inference on regression parameters in linear models with survival data.
  • To adapt the Buckley-James estimating equation for this specific problem.
  • To illustrate the proposed method with a practical example.

Main Methods:

  • Utilizing the Buckley-James (1979) estimating equation.
  • Developing a procedure for drawing inference on regression parameters.
  • Applying the method to a survival data example.

Related Experiment Videos

Main Results:

  • A straightforward procedure for regression parameter inference in linear models with survival data was developed.
  • The method, based on the Buckley-James equation, proved effective in the illustrated example.
  • Demonstrated the feasibility and utility of the proposed approach.

Conclusions:

  • The proposed simple procedure offers a viable method for regression inference with survival data.
  • The Buckley-James estimating equation provides a robust foundation for such analyses.
  • The approach is practical and can be applied to real-world survival data problems.