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Related Experiment Video

Updated: May 24, 2026

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

Comparison of estimators in nested case-control studies with multiple outcomes.

Nathalie C Støer¹, Sven Ove Samuelsen

¹Department of Mathematics, University of Oslo, Oslo, Norway. nathalcs@math.uio.no

Lifetime Data Analysis

|March 3, 2012

Summary

Nested case-control (NCC) studies can now reuse controls, overcoming previous matching limitations. New methods, including weighted partial likelihood and full likelihood approaches, enable flexible analysis of matched data.

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Last Updated: May 24, 2026

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

Area of Science:

Epidemiology
Biostatistics

Background:

Traditionally, controls in nested case-control (NCC) studies are matched to cases, preventing control reuse.
Recent methodological advancements allow for breaking this matching, enabling controls to be used across multiple cases.

Purpose of the Study:

To explore and present novel statistical methods for reusing controls in NCC studies.
To evaluate the performance of these methods under various conditions, including left truncation and competing risks.

Main Methods:

Discusses weighted partial likelihood (WPL) methods with four distinct weight estimation procedures.
Presents a full maximum likelihood approach, including an aggregation technique for computational efficiency.
Generalizes calibration methods from case-cohort designs to NCC studies.

Main Results:

Compares WPL, full likelihood, and calibration methods through simulations.
Analyzes a real-world dataset to demonstrate practical application and effectiveness.
Addresses necessary modifications for handling left-truncated data within these frameworks.

Conclusions:

The developed methods offer feasible solutions for reusing controls in NCC studies, enhancing statistical efficiency.
These approaches provide robust analytical options for complex epidemiological data, including competing risks scenarios.
The study validates the utility of control reuse through simulations and real data analysis.