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

Inference for multi-state models from interval-censored data.

D Commenges1

  • 1INSERM U330, 146 rue Leo Saignat, Bordeaux, 33076, France. daniel.commenges@bordeaux.isped.u-bordeaux2.fr

Statistical Methods in Medical Research
|June 4, 2002
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

Dynamical models of biomarkers and clinical progression for personalized medicine: the HIV context.

Advanced drug delivery reviews·2013
Same author

Estimation of the distribution of infection times using longitudinal serological markers of HIV: implications for the estimation of HIV incidence.

Biometrics·2010
Same author

Practical identifiability of HIV dynamics models.

Bulletin of mathematical biology·2007
Same author

Maximum likelihood estimation in dynamical models of HIV.

Biometrics·2007
Same author

Analysis of the effect of aluminum in drinking water and transferrin C2 allele on Alzheimer's disease.

European journal of neurology·2006
Same author

A latent process model for joint modeling of events and marker.

Lifetime data analysis·2004
Same journal

A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme.

Statistical methods in medical research·2026
Same journal

Efficient semi-supervised estimation of optimal individualized treatment regimes with survival outcome.

Statistical methods in medical research·2026
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
See all related articles

This review covers statistical methods for analyzing interval-censored data in multi-state models. It discusses non-parametric approaches for transition intensity estimation in clinical research.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Clinical Data Analysis

Background:

  • Clinical studies often involve discrete observation points, leading to interval-censored data for state transitions.
  • Analyzing these transitions requires specialized statistical methods beyond simple time-to-event data.
  • Homogeneous Markov models offer simpler inference but are less applicable to complex clinical scenarios.

Purpose of the Study:

  • To review statistical methodologies for handling interval-censored observations in multi-state models.
  • To explore non-parametric approaches for estimating transition intensities.
  • To provide an overview of methods applicable to clinical status observations.

Main Methods:

  • Discussion of likelihood formulation using transition probabilities and intensities.

Related Experiment Videos

  • Exploration of non-parametric approaches, including generalizations of the Turnbull method.
  • Review of smooth intensity models and penalized likelihood methods for inference.

    • Main Results:

    • Transition probabilities are directly linked to intensities in homogeneous Markov models.
    • More complex multi-state models require advanced techniques for intensity estimation.
    • Penalized likelihood offers a practical approach for non-homogeneous models.

    Conclusions:

    • Interval-censored data in clinical settings necessitates advanced survival analysis techniques.
    • Non-parametric and smooth intensity models provide viable pathways for inference.
    • This review synthesizes key methods for analyzing complex state transitions.