Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Confounding in Epidemiological Studies

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...

Bias in Epidemiological Studies

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:

Introduction to Epidemiology

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...

Causality in Epidemiology

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

You might also read

Related Articles

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

Sort by

Same author

Adjuvant Chemoradiotherapy Versus Radiotherapy Alone in Women With High-Risk Endometrial Cancer (PORTEC-3): 10-Year Clinical Outcomes and Post Hoc Analysis by Molecular Classification From a Randomized Phase 3 Trial.

Obstetrical & gynecological survey·2025

Same author

Neoadjuvant endocrine therapy often leads to breast and axilla-conserving surgery in stages II and III hormone receptor-positive breast cancer patients: A registry study.

International journal of cancer·2025

Same author

Rupture Risk of Large Abdominal Aortic Aneurysms: A Scoping Review.

Annals of vascular surgery·2025

Same author

Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data.

Biometrical journal. Biometrische Zeitschrift·2025

Same author

Factors influencing locoregional recurrence rates in locally advanced rectal cancer after total neoadjuvant treatment versus chemoradiotherapy in the RAPIDO trial.

The British journal of surgery·2025

Same author

Adjuvant chemoradiotherapy versus radiotherapy alone in women with high-risk endometrial cancer (PORTEC-3): 10-year clinical outcomes and post-hoc analysis by molecular classification from a randomised phase 3 trial.

The Lancet. Oncology·2025

Search research articles

Related Experiment Video

Updated: May 25, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Competing risks in epidemiology: possibilities and pitfalls.

Per Kragh Andersen¹, Ronald B Geskus, Theo de Witte

¹Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark.

International Journal of Epidemiology

|January 19, 2012

Summary

In competing risks studies, the direct relationship between rate and risk is lost, unlike in all-cause mortality. This impacts statistical modeling and interpretation of results, requiring careful consideration in analysis.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model

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

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

Last Updated: May 25, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

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

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

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
Survival Analysis

Background:

In all-cause mortality, rate and risk are linked one-to-one, enabling direct covariate interpretation in regression models.
This fundamental relationship influences how covariates affect survival functions.

Purpose of the Study:

To review the concepts of rate and risk in all-cause mortality.
To introduce analogous concepts of rate and risk within competing risks.
To examine the cause-specific hazard and cumulative incidence function.

Main Methods:

Review of epidemiological concepts.
Introduction of competing risks framework.
Illustrative example using stem cell transplantation data.

Main Results:

The one-to-one correspondence between cause-specific hazard and cumulative incidence is lost in competing risks.
Naïve Kaplan-Meier estimators are biased when treating competing events as censored.
Covariate associations with cause-specific hazards may differ from associations with cumulative incidence.

Conclusions:

The loss of one-to-one correspondence in competing risks has significant implications for statistical inference.
Researchers must be aware of these implications when analyzing data with competing risks.
Careful consideration is needed for model selection and interpretation in competing risks scenarios.