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

Regression analysis of multivariate grouped survival data

S W Guo1, D Y Lin

  • 1Department of Biostatistics, University of Michigan, Ann Arbor 48109.

Biometrics
|September 1, 1994
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

[Research on the evaluation of the automatic construction effectiveness of a flexible point cloud deformation algorithm for nasal prostheses].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology·2026
Same author

[Analysis of adjuvant chemotherapy efficacy based on GLUT1 expression levels after radical resection of pancreatic cancer].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2026
Same author

[Study on the consistency between the trajectory of small opening and closing movements and the empirical hinge axis movement of the mandibular incisal point of healthy individuals].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology·2026
Same author

[Clinical value of secondary surgical resection for recurrent or metastatic pancreatic cancer after initial surgery].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2026
Same author

Checking the Cox Proportional Hazards Model with Interval-Censored Data.

Journal of the American Statistical Association·2025
Same author

Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State.

Journal of the American Statistical Association·2025

This study introduces a new method for analyzing multivariate failure time data with grouped or discrete measurements. The proposed approach ensures accurate statistical inference by accounting for within-cluster dependence.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Multivariate failure time data present unique challenges due to multiple event types or correlated observations within clusters.
  • Failure times are frequently grouped or measured discretely, necessitating specialized analytical methods.

Purpose of the Study:

  • To develop a robust statistical framework for analyzing discrete multivariate failure time data.
  • To estimate regression parameters and survival probabilities while accommodating unspecified dependence structures.

Main Methods:

  • Formulation of marginal distributions using a grouped-data proportional hazards model.
  • Application of generalized estimating equations (GEE) adapted from Liang and Zeger (1986).
  • Construction of robust estimators for limiting covariance matrices.

Related Experiment Videos

Main Results:

  • Proposed estimators are consistent and asymptotically normal.
  • Simulation studies confirm the adequacy of asymptotic approximations for practical applications.
  • Ignoring intracluster dependence leads to invalid statistical inference.

Conclusions:

  • The developed GEE approach provides a valid method for analyzing discrete multivariate failure time data.
  • Accurate variance-covariance estimation is crucial for reliable statistical inference in clustered survival data.
  • The methodology is applicable to various fields, including psychological experiments.