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Summary
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This study introduces new methods for estimating absolute risk in case-cohort studies, accounting for competing events. The proposed variance estimation improves accuracy for absolute risk analysis using sampled cohort data.

Keywords:
Absolute riskCase-cohortCompeting eventsInfluence-based variance estimationTwo-phase sampling

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Case-cohort studies are efficient for disease research but estimating absolute risk is complex.
  • Competing events can lead to overestimation of pure risk, necessitating accurate absolute risk estimation.
  • Existing case-cohort methods often focus on relative hazards, not absolute risk under competing risks.

Purpose of the Study:

  • To develop and validate methods for absolute risk inference in case-cohort studies using the cause-specific hazard Cox model.
  • To propose an influence-based variance estimation formula that accounts for sampling designs.
  • To address the overestimation of absolute risk caused by competing events.

Main Methods:

  • Utilized the cause-specific hazard Cox model for inference.
  • Developed an influence-based variance estimation formula.
  • Evaluated two sampling designs: exhaustive case-cohort and event-stratified sampling.
  • Performed simulations and analyzed data from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial.

Main Results:

  • The proposed variance estimate accurately reflects sampling features for absolute risk analysis.
  • Demonstrated the method's utility in simulation studies and a real-world cancer screening trial.
  • Found that Barlow's "robust" variance may overestimate absolute risk in cohort subsampling designs.

Conclusions:

  • The developed methods provide accurate absolute risk inference in case-cohort studies with competing risks.
  • The proposed variance estimation is suitable for both exhaustive and event-stratified sampling designs.
  • Highlights the importance of appropriate variance estimation for reliable absolute risk assessment in epidemiological research.