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Related Concept Videos

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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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Related Experiment Video

Updated: Jan 26, 2026

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression
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Estimation of Relative and Absolute Risks in a Competing-Risks Setting Using a Nested Case-Control Study Design:

Renata Zelic1, Daniela Zugna2,3, Matteo Bottai4

  • 1Clinical Epidemiology Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.

American Journal of Epidemiology
|April 13, 2019
PubMed
Summary
This summary is machine-generated.

The Prognostic Factors for Mortality in Prostate Cancer (ProMort) study shows that bias in estimating prostate cancer death risks can occur in nested case-control studies. Augmenting cases and controls reduces this bias.

Keywords:
absolute riskcompeting riskscumulative incidence functionflexible parametric survival modelinverse probability weightingnested case-control studiesweighted likelihood

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Oncology

Background:

  • Nested case-control studies are efficient for rare outcomes but can introduce bias in competing-risks settings.
  • Accurate estimation of prostate cancer mortality risks is crucial for patient management and public health.
  • The National Prostate Cancer Register (NPCR) of Sweden provides a valuable resource for epidemiological research.

Purpose of the Study:

  • To describe the Prognostic Factors for Mortality in Prostate Cancer (ProMort) study.
  • To demonstrate the application of the weighted likelihood method in nested case-control studies for competing-risks analysis.
  • To evaluate bias in hazard ratio and cumulative incidence function (CIF) estimates within nested case-control designs.

Main Methods:

  • Utilized the ProMort study, a nested case-control study within the NPCR, with 1,710 prostate cancer deaths and 1,710 matched controls.
  • Employed weighted flexible parametric models to estimate cause-specific hazard ratios and CIFs for prostate cancer death.
  • Quantified bias by comparing ProMort estimates with NPCR cohort data and analyzing random subsamples, including augmentation strategies.

Main Results:

  • Hazard ratios for prostate cancer death in ProMort were comparable to those from the NPCR cohort.
  • Biased hazard ratios for non-prostate cancer deaths were observed, impacting CIF estimates in the competing-risks setting.
  • Augmenting both competing-risks cases and controls in the analysis reduced the observed bias.

Conclusions:

  • The weighted likelihood method is a viable approach for estimating risks in nested case-control studies with competing risks.
  • Careful consideration of competing risks and potential biases is essential when analyzing nested case-control data.
  • Augmentation strategies can improve the accuracy of risk estimates in such designs, particularly when non-study outcomes are present.