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

Estimation with interval censored data and covariates

M J Van der Laan1, A Hubbard

  • 1Division of Biostatistics, University of California, Berkeley, USA.

Lifetime Data Analysis
|January 1, 1997
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

Formal responsibility and task execution: the importance of modelling task delegation in FRAM to optimise the identification of potential improvement strategies.

Applied ergonomics·2026
Same author

Uncovering gaps in workforce well-being: a national look at survey practice in Dutch university medical centres - an exploratory quantitative study.

BMJ open·2025
Same author

Overview of the early campaign diagnostics for the SPARC tokamak (invited).

The Review of scientific instruments·2024
Same author

The extreme yet transient nature of glacial erosion.

Nature communications·2022
Same author

Dedicated teams to optimize quality and safety of surgery: A systematic review.

International journal for quality in health care : journal of the International Society for Quality in Health Care·2022
Same author

Nutritional status and out-of-hospital mortality in vascular surgery patients.

PloS one·2022
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 new statistical method for estimating event time distributions from censored data. The proposed estimator is efficient and robust, even with complex covariate data, and performs well in simulations.

Area of Science:

  • Biostatistics and Survival Analysis
  • Statistical Estimation Methods

Background:

  • Accurate estimation of time-to-event distributions is crucial in biostatistics.
  • Current methods for interval-censored data with covariates can be inefficient or inconsistent.
  • Existing nonparametric maximum likelihood estimators struggle with continuous covariates.

Purpose of the Study:

  • To develop a locally efficient and robust estimator for time-to-event distributions.
  • To extend existing methods to handle interval-censored data in the presence of covariates.
  • To provide an estimator with a known influence curve for direct confidence interval calculation.

Main Methods:

  • Utilizes a one-step estimator approach, building on van der Laan and Robins (1996).
  • Assumes a model for censoring but not for the conditional distribution of the event time.

Related Experiment Videos

  • Applies the estimator to interval-censored data, including cases without covariates.

    • Main Results:

    • The proposed estimator is shown to be locally optimal for distribution function estimation.
    • Demonstrates effective estimation for interval-censored data with covariates.
    • Achieves high relative efficiency (0.994) compared to the optimal bound for a specific case.

    Conclusions:

    • The new estimator offers a consistent, asymptotically normal, and efficient solution for interval-censored data.
    • It provides a practical and robust alternative to existing methods, especially with covariates.
    • The estimator's performance is validated through theoretical analysis and simulation studies.