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

Bayesian survival analysis using a MARS model.

B K Mallick1, D G Denison, A F Smith

  • 1Department of Statistics, Texas A&M University, College Station 77843-3143, USA. bmallick@stat.tamu.edu

Biometrics
|April 21, 2001
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

Choice of airway device and the incidence and severity of postoperative pulmonary complications in older patients.

Anaesthesia·2023
Same author

How to conduct a Delphi consensus process.

Anaesthesia·2022
Same author

The thirst for improvement: ensuring shorter and safer pre-operative fasting times in children.

Anaesthesia·2022
Same author

What can anaesthetists do to help combat the global climate emergency?

Anaesthesia·2022
Same author

The use of intravenous lidocaine for postoperative pain and recovery: a reply.

Anaesthesia·2021
Same author

'Never Events': will they always be with us?

Anaesthesia·2021
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 introduces a Bayesian adaptive regression spline method for analyzing survival data, even with missing information. The approach models survival curves and hazard functions, automatically identifying deviations from standard models.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Survival Analysis

Background:

  • Survival data analysis is crucial in many fields, but standard models like the proportional hazards model have limitations.
  • Handling censored data and detecting departures from model assumptions are key challenges.

Purpose of the Study:

  • To develop a flexible Bayesian approach for modeling univariate and multivariate survival data with censoring.
  • To automatically detect deviations from the proportional hazards model.
  • To provide reliable estimates for hazard and survival functions.

Main Methods:

  • Utilizing a Bayesian multivariate adaptive regression spline (MARS) fitting approach.
  • Employing a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm for model estimation.

Related Experiment Videos

  • The MARS models encompass the proportional hazards model as a special case.

    • Main Results:

    • The proposed Bayesian MARS method effectively models complex survival data patterns.
    • The approach successfully identifies departures from proportional hazards assumptions.
    • Accurate estimation of hazard functions and survival curves was achieved.

    Conclusions:

    • The Bayesian MARS approach offers a powerful and flexible alternative for survival data analysis.
    • This method enhances the ability to model and interpret survival data, especially when proportional hazards assumptions are violated.
    • The RJMCMC algorithm provides a robust framework for estimating model parameters.