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Related Concept Videos

Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Kaplan-Meier Approach01:24

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Odds Ratio

The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...

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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

Population-based absolute risk estimation with survey data.

Stephanie A Kovalchik1, Ruth M Pfeiffer

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD, 20892-9702, USA, kovalchiksa@mail.nih.gov.

Lifetime Data Analysis
|May 21, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces methods to estimate absolute risk for specific causes of death, even with competing risks. These population-based models, using survey data, aid in predicting health outcomes and prevention program impact.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Last Updated: May 11, 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

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

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Estimating absolute risk is crucial for public health and clinical decision-making.
  • Competing risks, where one event prevents another, complicate absolute risk assessment.
  • Population-based risk models require methods robust to complex survey designs.

Purpose of the Study:

  • To develop and validate methods for estimating population-based absolute risk in the presence of multiple competing risks.
  • To introduce novel measures for guiding the modeling of competing event components.
  • To apply these methods to predict cardiovascular and cancer mortality using national survey data.

Main Methods:

  • Utilized a complex survey cohort design.
  • Modeled cause-specific hazard functions using individualized relative risks and flexible baseline hazard functions (nonparametric or piecewise exponential).
  • Employed an influence method for Taylor-linearized variance estimation and developed cause-specific influence measures.

Main Results:

  • Developed and validated cause-specific absolute risk models for cardiovascular and cancer deaths.
  • Demonstrated the utility of survey-based risk prediction models.
  • Quantified the potential impact of disease prevention programs at the population level.

Conclusions:

  • The presented methodology effectively estimates population-based absolute risk with competing risks.
  • These models are valuable tools for predicting health outcomes and informing public health interventions.
  • Survey data can be leveraged to build robust risk prediction models for diverse populations.