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

Flexible Bayesian modelling for survival data

P Gustafson1

  • 1Department of Statistics, University of British Columbia, Vancouver, Canada. gustaf@stat.ubc.ca

Lifetime Data Analysis
|October 27, 1998
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

Analysis of negligence claims after carotid artery interventions over 20 years in Sweden.

Scandinavian journal of surgery : SJS : official organ for the Finnish Surgical Society and the Scandinavian Surgical Society·2025
Same author

Maternal and pregnancy predictive risk factors for having a compensated maternal injury claim: a Swedish nationwide cohort study.

Scientific reports·2023
Same author

The role of prolactin in the suppression of Crh mRNA expression during pregnancy and lactation in the mouse.

Journal of neuroendocrinology·2017
Same author

Role of primary T-cell immunodeficiency and hepatitis B coinfection on spontaneous clearance of hepatitis C: The BC Hepatitis Testers Cohort.

Journal of viral hepatitis·2016
Same author

Disconnected by design: analytic approach in treatment networks having no common comparator.

Research synthesis methods·2016
Same author

Beta-interferon exposure and onset of secondary progressive multiple sclerosis.

European journal of neurology·2015
Same journal

Shared frailty sieve estimation for dependent left truncated and interval censored data.

Lifetime data analysis·2026
Same journal

Functional win-fractions regression models for composite outcomes.

Lifetime data analysis·2026
Same journal

Variable selection in causal semiparametric transformation models with all-or-nothing treatment compliance.

Lifetime data analysis·2026
Same journal

Correction to: A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model.

Lifetime data analysis·2026
Same journal

Unobserved heterogeneity in threshold regression based on the hitting times of a reflected Brownian motion for recurrent hypoglycemia.

Lifetime data analysis·2026
Same journal

Variable selection with broken adaptive ridge regression for interval-censored competing risks data.

Lifetime data analysis·2026
See all related articles

This study introduces a flexible hierarchical Bayes model for failure time data analysis. It relaxes common assumptions, allowing for time-dependent covariate effects and non-additive interactions, improving model accuracy.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Failure time data analysis often relies on proportional hazards and additive effects assumptions.
  • These assumptions can limit the accuracy and applicability of statistical models.
  • Relaxing these assumptions is crucial for more robust failure time analysis.

Purpose of the Study:

  • To present a hierarchical Bayes model that relaxes standard assumptions in failure time data analysis.
  • To explicitly model time-dependent covariate effects.
  • To accommodate non-additive effects between explanatory variables.

Main Methods:

  • Development of a hierarchical Bayes model.
  • Incorporation of time-dependent covariate effects.
  • Utilization of a modified neural network structure to relax additivity assumptions.

Related Experiment Videos

  • Data-driven penalty for assumption violations.

    • Main Results:

    • The proposed model successfully relaxes the proportional hazards and additive effects assumptions.
    • Time-dependent and non-additive effects are effectively modeled.
    • The hierarchical structure provides a parsimonious penalty for assumption violations, determined by the data.

    Conclusions:

    • The hierarchical Bayes model offers a more flexible and accurate approach to failure time data analysis.
    • It provides a data-driven method to penalize deviations from standard assumptions.
    • This approach enhances the analysis of complex survival data where assumptions may not hold.