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Updated: Apr 30, 2026

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Attributable risk from distributed lag models.

Antonio Gasparrini1, Michela Leone

  • 1Department Medical Statistics, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK. antonio.gasparrini@lshtm.ac.uk.

BMC Medical Research Methodology
|April 25, 2014
PubMed
Summary

This study introduces new definitions for attributable risk, incorporating time delays in exposure-response relationships. These advanced measures improve public health assessments by considering temporal patterns in risk factors like temperature.

Related Experiment Videos

Last Updated: Apr 30, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

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Area of Science:

  • Epidemiology
  • Environmental Health
  • Biostatistics

Background:

  • Attributable risk measures are crucial for public health planning but lack temporal considerations.
  • Existing definitions do not account for time lags between exposure and health outcomes.
  • This limits their accuracy in evaluating interventions with delayed effects.

Purpose of the Study:

  • To extend definitions of attributable risk to include temporal relationships.
  • To incorporate distributed lag non-linear models for delayed exposure-response associations.
  • To provide more accurate risk assessment tools for public health.

Main Methods:

  • Classified attributable number and fraction using forward (future burden) and backward (past burden) perspectives.
  • Utilized distributed lag non-linear models (DLNMs) to capture delayed effects.
  • Separated attributable components for sub-ranges of exposure.

Main Results:

  • Applied extended definitions to estimate mortality risk attributable to outdoor temperature in London and Rome.
  • Provided estimates for overall mortality burden due to temperature.
  • Quantified components attributable to cold, heat, mild, and extreme temperatures.

Conclusions:

  • Extended attributable risk definitions effectively incorporate temporal dimensions.
  • These new measures offer more appropriate assessments for complex exposure-response patterns.
  • The approach enhances epidemiological analysis for public health interventions.