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 Concept Videos

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Provider Mental Health and Telehealth Utilization During COVID-19: A Cross-sectional Survey of Providers at Clinics Treating Urban American Indian and Alaska Native Peoples.

Journal of racial and ethnic health disparities·2026
Same author

Paralysis in public health: evidence as an alibi? - Authors' reply.

The Lancet. Public health·2026
Same author

The adaptation and evaluation of a culturally grounded lifestyle intervention to mitigate the risk of Alzheimer's disease and related dementias in Native Hawaiians and Pacific Islanders: A study protocol.

SSM. Mental health·2026
Same author

Combined Oral Contraceptives and Stroke Risk in Individuals With Migraine With Aura.

Obstetrics and gynecology·2026
Same author

Hepatitis B and C Prevention, Screening and Diagnostic Services at HIV Treatment Sites: International epidemiology Databases to Evaluate AIDS.

Journal of acquired immune deficiency syndromes (1999)·2026
Same author

One- and Two-Year Outcomes of Switching to a Dolutegravir-Based ART Regimen: An Observational Emulation of Sequential Target Trials.

Open forum infectious diseases·2026
Same journal

RE: advancing observational research on arts and health: theory-informed approaches using the RADIANCE framework.

American journal of epidemiology·2026
Same journal

Maternal Cesarean Section and Offspring ASD or ADHD Risk: A Nurses' Health Study II Analysis.

American journal of epidemiology·2026
Same journal

Immigration and epigenetic age acceleration in the health and retirement study: differences Between Hispanics and Non-Hispanics.

American journal of epidemiology·2026
Same journal

Age-period-cohort analysis of low birth weight, early term delivery, induction and csection, US births 1990-2019.

American journal of epidemiology·2026
Same journal

A measurement comparability study to support instrument harmonization in the Canadian longitudinal study on aging.

American journal of epidemiology·2026
Same journal

Attending an Historically Black College or University and all-cause mortality in US Black adults.

American journal of epidemiology·2026
See all related articles
  1. Home
  3. Quantitative Bias Analysis For Time To Event Data Needs Good Validation Data.
  1. Home
  3. Quantitative Bias Analysis For Time To Event Data Needs Good Validation Data.

Related Experiment Video

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Quantitative bias analysis for time to event data needs good validation data.

Matthew P Fox1, Richard MacLehose2

  • 1Departments of Epidemiology and of Global Health, Boston University School of Public Health, Boston University, Boston, MA 02118, United States.

American Journal of Epidemiology
|May 12, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Quantitative bias analysis methods for time-to-event outcomes are underdeveloped. New approaches are needed to address misclassification errors in both events and person-time, improving epidemiologic research validity.

Keywords:
measurement errormisclassificationquantitative bias analysisvalidation

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Quantitative bias analysis (QBA) methods primarily address binary outcomes, leaving time-to-event outcomes underdeveloped.
  • Misclassification in time-to-event analyses is complex, involving errors in both events and person-time, necessitating specific bias parameters.

Purpose of the Study:

  • To highlight the underdeveloped nature of QBA for time-to-event outcomes.
  • To discuss current limitations and propose future directions for improving QBA in this area.

Main Methods:

  • Review of existing QBA methodologies and their limitations for time-to-event data.
  • Discussion of recent work utilizing expert-informed ranges and simulation for bias parameter estimation.
  • Emphasis on the need for validation studies and methodological innovation.

Main Results:

  • Current QBA methods for time-to-event outcomes are limited by a lack of validation data, especially for person-time measurement error.
  • Existing approaches, while better than qualitative assessments, require further development for broader application.

Conclusions:

  • Improving QBA for time-to-event outcomes requires focused efforts on designing and publishing validation studies.
  • Methodological innovation, accessible software, and extensions of regression calibration and risk-based adjustment are crucial.
  • Expanding QBA methods will enhance the validity, transparency, and interpretability of epidemiologic research.