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

Semi-parametric estimation in failure time mixture models

J M Taylor1

  • 1Department of Biostatistics, University of California, Los Angeles 90024-1772, USA.

Biometrics
|September 1, 1995
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

A Case of Cut Throat.

Atlanta medical and surgical journal (1884)·2022
Same author

The Bone Sialoprotein RGD Domain Modulates and Maintains Periodontal Development.

Journal of dental research·2022
Same author

Magnetism and magnetoresistance in the critical region of a dilute ferromagnet.

Scientific reports·2021
Same author

A coherent spin-photon interface in silicon.

Nature·2018
Same author

Efimov States of Strongly Interacting Photons.

Physical review letters·2017
Same author

Resonantly driven CNOT gate for electron spins.

Science (New York, N.Y.)·2017
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 semi-parametric mixture model for failure time data, offering a flexible approach for analyzing two distinct subject groups. The model shows nearly equivalent efficiency to parametric methods for incidence estimation but is less efficient for latency distribution.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Failure time data analysis often involves populations with distinct groups, necessitating specialized models.
  • Traditional mixture models provide a framework for such data, but semi-parametric generalizations offer enhanced flexibility.
  • Existing methods may not fully capture the complexities of latency and incidence within these groups.

Purpose of the Study:

  • To propose and evaluate a semi-parametric mixture model for failure time data.
  • To generalize Farewell's (1982) mixture model using logistic regression for incidence and a Kaplan-Meier approach for latency.
  • To assess the model's performance via application to radiation biology data and a Monte Carlo simulation.

Main Methods:

  • A semi-parametric mixture model combining logistic regression (incidence) and Kaplan-Meier estimation (latency).

Related Experiment Videos

  • Utilized the Expectation-Maximization (EM) algorithm for model fitting, drawing from Larson and Dinse (1985).
  • Applied the model to experimental radiation biology data and conducted a Monte Carlo simulation study.

    • Main Results:

    • The semi-parametric model demonstrated strong performance in analyzing failure time data with two distinct subject groups.
    • Monte Carlo simulations indicated the procedure's efficiency is comparable to fully parametric methods for estimating incidence regression coefficients.
    • The model was less efficient than parametric approaches for estimating the latency distribution.

    Conclusions:

    • The proposed semi-parametric mixture model is a viable and flexible tool for analyzing complex failure time data.
    • It offers a robust alternative to fully parametric models, particularly when assumptions about latency distributions are uncertain.
    • Further research could explore extensions to improve latency estimation efficiency.