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An R-Based Landscape Validation of a Competing Risk Model
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Published on: September 16, 2022

On estimation of linear transformation models with nested case-control sampling.

Wenbin Lu1, Mengling Liu

  • 1Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. lu@stat.ncsu.edu

Lifetime Data Analysis
|September 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for analyzing nested case-control (NCC) data using linear transformation models, offering a cost-effective alternative to traditional Cox models for epidemiological research.

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Nested case-control (NCC) sampling is a cost-effective method for large cohort studies.
  • Current data analysis for NCC primarily uses the Cox proportional hazards model.

Purpose of the Study:

  • To propose and evaluate a family of linear transformation models for analyzing NCC data.
  • To develop an inverse selection probability weighted estimating equation method for statistical inference.

Main Methods:

  • Utilized linear transformation models for NCC data analysis.
  • Developed an inverse selection probability weighted estimating equation approach.
  • Established consistency and asymptotic normality of estimators.

Main Results:

  • The proposed method provides consistent and asymptotically normal estimators for regression coefficients.
  • The asymptotic variance of the estimators has a closed analytic form and is easily estimated.
  • Numerical studies and an application to the Wilms' Tumor Study support the methodology.

Conclusions:

  • Linear transformation models offer a viable alternative for analyzing NCC data.
  • The proposed weighted estimating equation method is theoretically sound and practically applicable.
  • This approach enhances the analytical toolkit for large-scale epidemiological studies.