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

388
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.
388
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

988
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
988
Censoring Survival Data01:09

Censoring Survival Data

516
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...
516
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.0K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.0K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

549
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,...
549
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

548
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...
548

You might also read

Related Articles

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

Sort by
Same author

Spatial disparities and associated factors of composite index of anthropometric failure for under-five children across three African countries.

Global epidemiology·2026
Same author

Supervised machine learning algorithms for classifications of gender-based violence in Somalia: a comparison of oversampling techniques.

Scientific reports·2026
Same author

Hyperbaric Oxygen Therapy Versus Intravenous Thrombolysis in the Treatment of Central Retinal Artery Occlusion: A Systematic Review and Meta-Analysis.

Journal of clinical medicine·2026
Same author

Survival analysis of time-to-death for under-five children in Somalia: Application of AFT modeling approach.

Public health in practice (Oxford, England)·2026
Same author

A comparative analysis of data-driven models for breast cancer survival prediction.

Scientific reports·2026
Same author

Maternal mortality in Ethiopia (2015-2025): a systematic review of recent evidence and determinants.

BMC public health·2025

Related Experiment Video

Updated: Jan 12, 2026

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

2.5K

Pseudo-Observation Approach for Length-Biased Cox Proportional Hazards Model.

Mahboubeh Akbari1, Najmeh Nakhaei Rad1, Ding-Geng Chen1,2

  • 1Department of Statistics, University of Pretoria, Pretoria, South Africa.

Biometrical Journal. Biometrische Zeitschrift
|October 31, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces pseudo-observations to estimate Cox proportional hazards models with length-biased right-censored data. The proposed methods offer comparable standard errors and improved confidence intervals, especially in large samples.

Keywords:
Cox proportional hazards modelgeneralized estimation equationlength‐biased data‐observation

More Related 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

10.7K
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

15.0K

Related Experiment Videos

Last Updated: Jan 12, 2026

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

2.5K
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

10.7K
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

15.0K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Incomplete survival data due to censoring or truncation is common in biostatistics and epidemiology.
  • Length-biased sampling, a form of left-truncation where truncation follows a uniform distribution, biases observations towards longer durations.
  • Estimating parameters in survival models with such biased data requires specialized methods.

Purpose of the Study:

  • To apply pseudo-observations for estimating regression coefficients in the Cox proportional hazards model under length-biased right-censored (LBRC) data.
  • To compare the accuracy and efficiency of two novel pseudo-observation generation approaches against existing standard methods.
  • To analyze the specific characteristics of length-biased data within the broader context of left-truncated data.

Main Methods:

  • Development and application of two distinct pseudo-observation generation techniques tailored for LBRC data.
  • Comparative analysis of the proposed methods against two established standard methods using simulations.
  • Theoretical assessment including establishing consistency and asymptotic normality for one proposed estimator.

Main Results:

  • The two proposed pseudo-observation methods demonstrate performance comparable to standard methods regarding standard error.
  • The novel methods provide confidence intervals closer to nominal levels in large sample sizes and specific scenarios.
  • Simulation results highlight the necessity of treating length-biased data distinctly from general left-truncated data.

Conclusions:

  • Pseudo-observation methods are effective for analyzing LBRC data, offering advantages in confidence interval accuracy.
  • Leveraging the uniform distribution property of the truncation variable in length-biased data is crucial for accurate estimation.
  • The proposed methods provide a robust alternative for survival data analysis in the presence of length bias and right-censoring.