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An R-Based Landscape Validation of a Competing Risk Model
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Methods for generating paired competing risks data.

Ruta Brazauskas1, Jennifer Le-Rademacher2

  • 1Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA.

Computer Methods and Programs in Biomedicine
|September 3, 2016
PubMed
Summary
This summary is machine-generated.

This study examines methods for simulating clustered competing risks data, crucial for genetic and medical research. It provides guidance on selecting simulation techniques to control data dependence levels.

Keywords:
Competing risksCross-hazard ratioPaired dataSimulation study

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Area of Science:

  • Biostatistics
  • Statistical Genetics
  • Epidemiology

Background:

  • Clustered competing risks data are prevalent in genetic studies, multicenter investigations, and matched-pairs research.
  • Advances in competing risks theory necessitate robust simulation methods for evaluating new statistical approaches.
  • Existing simulation techniques often lack clarity regarding the dependence strength within clusters.

Purpose of the Study:

  • To examine various techniques for generating bivariate competing risks data.
  • To provide a framework for simulating dependent competing risks data.
  • To guide researchers in selecting appropriate simulation methods and parameters.

Main Methods:

  • Utilized latent failure time approach, multistate models, and shared frailty models for simulation.
  • Outlined implementation steps for each described method.
  • Discussed properties of each technique and provided measures of association.

Main Results:

  • Described diverse techniques for generating dependent competing risks data.
  • Computed cross-hazard ratios across multiple scenarios for each method.
  • Demonstrated how cross-hazard ratios compare dependence levels between methods.

Conclusions:

  • Cross-hazard ratios offer a quantitative comparison of data dependence across simulation methods.
  • This study serves as a guide for researchers to select simulation approaches for desired dependence.
  • Facilitates informed parameter selection for simulation studies involving clustered competing risks data.