Literature DB >> 11550928

A bivariate cure-mixture approach for modeling familial association in diseases.

N Chatterjee1, J Shih.   

Abstract

For modeling correlation in familial diseases with variable ages at onset, we propose a bivariate model that incorporates two types of pairwise association, one between the lifetime risk or the overall susceptibility of two individuals and one between the ages at onset between two susceptible individuals. For estimation, we consider a two-stage estimation procedure similar to that of Shih (1998, Biometrics 54, 1115-1128). We evaluate the properties of the estimators through simulations and compare the performance with that from a bivariate survival model that allows correlation between ages at onset only. We apply the methodology to breast cancer using the kinship data from the Washington Ashkenazi Study. We also discuss potential applications of the proposed method in the area of cure modeling.

Entities:  

Mesh:

Year:  2001        PMID: 11550928     DOI: 10.1111/j.0006-341x.2001.00779.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  5 in total

1.  A marginal regression model for multivariate failure time data with a surviving fraction.

Authors:  Yingwei Peng; Jeremy M G Taylor; Binbing Yu
Journal:  Lifetime Data Anal       Date:  2007-07-20       Impact factor: 1.588

2.  Association measures for bivariate failure times in the presence of a cure fraction.

Authors:  Lajmi Lakhal-Chaieb; Thierry Duchesne
Journal:  Lifetime Data Anal       Date:  2016-06-23       Impact factor: 1.588

3.  Mixture cure model with random effects for the analysis of a multi-center tonsil cancer study.

Authors:  Yingwei Peng; Jeremy M G Taylor
Journal:  Stat Med       Date:  2010-11-05       Impact factor: 2.373

4.  A bivariate survival model with compound Poisson frailty.

Authors:  A Wienke; S Ripatti; J Palmgren; A Yashin
Journal:  Stat Med       Date:  2010-01-30       Impact factor: 2.373

5.  Analysis of Smoking Cessation Patterns Using a Stochastic Mixed-Effects Model With a Latent Cured State.

Authors:  Sheng Luo; Ciprian M Crainiceanu; Thomas A Louis; Nilanjan Chatterjee
Journal:  J Am Stat Assoc       Date:  2008-09-01       Impact factor: 5.033

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.