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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

692
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...
692
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

544
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
544
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Introduction to Test of Independence01:21

Introduction to Test of Independence

3.1K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
3.1K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

568
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
568
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

475
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.
475

You might also read

Related Articles

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

Sort by
Same author

A nonparametric dependent competing risk method for net survival analysis.

The international journal of biostatistics·2026
Same author

Prediction of transition probabilities in multi-state models with nested case-control data.

Biometrics·2025
Same author

Dynamic prediction by landmarking with data from cohort subsampling designs.

Statistical methods in medical research·2025
Same author

Information Revolutions and Information Transitions: Counting, Sealing, Writing in Iran 10,000-300 BC.

Journal of ancient Near Eastern history·2025
Same author

Can Contemporary Large Language Models Provide the Domain Knowledge Needed for Causal Inference? Evaluating Automated Causal Graph Discovery Through an ASCVD Case Study.

Clinical epidemiology·2025
Same author

Racial and Ethnic Disparities in the Incidence and Prevalence of Low Back Pain in the United States: A Systematic Review.

Arthritis care & research·2025
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Mar 18, 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

2.7K

Nonparametric binary instrumental variable analysis of competing risks data.

Amy Richardson1, Michael G Hudgens2, Jason P Fine2

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA amyrichardson@google.com.

Biostatistics (Oxford, England)
|June 30, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces new instrumental variable (IV) methods to estimate causal treatment effects in complex health studies, addressing unmeasured confounding in survival data with competing risks. These methods accurately assess treatment impacts, even with patient noncompliance.

Keywords:
Competing risksComplianceIdentifiabilityInstrumental variablesRight censoringSurvival analysis

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

11.0K
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

15.4K

Related Experiment Videos

Last Updated: Mar 18, 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

2.7K
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

11.0K
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

15.4K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Causal Inference

Background:

  • Unmeasured confounding can bias treatment effect estimates in observational studies and randomized trials with noncompliance.
  • Instrumental variables (IV) offer a robust approach to address confounding and consistently estimate causal effects.
  • Censored time-to-event data with competing risks present unique challenges in estimating treatment effects.

Purpose of the Study:

  • To propose novel nonparametric instrumental variable (IV) estimators for censored time-to-event data, specifically designed to handle competing risks.
  • To develop an integrated weighted difference statistic for an overall test of treatment effect, applicable with or without competing risks.
  • To evaluate the performance of these new IV methods using simulation studies and apply them to a real-world HIV transmission study.

Main Methods:

  • Development of nonparametric IV estimators utilizing nonparametric cumulative incidence function estimators.
  • Derivation of confidence intervals using established asymptotic theory.
  • Introduction of an integrated weighted difference statistic for treatment effect testing.

Main Results:

  • Simulation studies indicate that the proposed nonparametric IV methods perform well with realistic sample sizes.
  • The methods are successfully applied to analyze the effect of antiretroviral therapy on mother-to-child HIV transmission in a Malawian trial.
  • The study demonstrates the utility of IV methods in addressing noncompliance and confounding in complex clinical trial data.

Conclusions:

  • The proposed nonparametric IV estimators provide a valuable tool for causal inference in the presence of unmeasured confounding and competing risks in survival analysis.
  • These methods enhance the ability to obtain reliable treatment effect estimates from observational and interventional studies with noncompliance.
  • The application to HIV transmission highlights the practical relevance of these statistical techniques in public health research.