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Inverse probability weighted Cox regression for doubly truncated data.

Micha Mandel1, Jacobo de Uña-Álvarez2, David K Simon3

  • 1Department of Statistics, The Hebrew University of Jerusalem, Jerusalem, Israel.

Biometrics
|September 9, 2017
PubMed
Summary

This study introduces a new Cox regression method for doubly truncated data, enabling analysis with multiple covariates. The method was applied to investigate Parkinson's disease onset and single nucleotide polymorphisms.

Keywords:
Biased dataInverse weightingRight truncationU statistic

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Genetics

Background:

  • Doubly truncated data present challenges for survival analysis.
  • Existing regression methods for such data are limited in scope and number of covariates.

Purpose of the Study:

  • To develop a Cox regression method for doubly truncated data with multiple covariates.
  • To enable the study of complex associations, such as genetic factors and disease onset.

Main Methods:

  • Developed a Cox regression modeling approach for doubly truncated data.
  • Incorporated multiple discrete and continuous covariates.
  • Demonstrated implementation using existing statistical software.

Main Results:

  • Successfully fitted the Cox regression model to doubly truncated data.
  • Applied the method to analyze the association between single nucleotide polymorphisms and Parkinson's disease age of onset.

Conclusions:

  • The proposed method extends regression analysis to doubly truncated data with multiple covariates.
  • This approach facilitates the investigation of genetic associations with disease onset in complex datasets.