Literature DB >> 17401640

Genetic mixed linear models for twin survival data.

Il Do Ha1, Youngjo Lee, Yudi Pawitan.   

Abstract

Twin studies are useful for assessing the relative importance of genetic or heritable component from the environmental component. In this paper we develop a methodology to study the heritability of age-at-onset or lifespan traits, with application to analysis of twin survival data. Due to limited period of observation, the data can be left truncated and right censored (LTRC). Under the LTRC setting we propose a genetic mixed linear model, which allows general fixed predictors and random components to capture genetic and environmental effects. Inferences are based upon the hierarchical-likelihood (h-likelihood), which provides a statistically efficient and unified framework for various mixed-effect models. We also propose a simple and fast computation method for dealing with large data sets. The method is illustrated by the survival data from the Swedish Twin Registry. Finally, a simulation study is carried out to evaluate its performance.

Mesh:

Year:  2007        PMID: 17401640     DOI: 10.1007/s10519-007-9150-7

Source DB:  PubMed          Journal:  Behav Genet        ISSN: 0001-8244            Impact factor:   2.805


  3 in total

1.  Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data.

Authors:  Jihyoun Jeon; Li Hsu; Malka Gorfine
Journal:  Biostatistics       Date:  2011-11-15       Impact factor: 5.899

2.  Frailty modelling approaches for semi-competing risks data.

Authors:  Il Do Ha; Liming Xiang; Mengjiao Peng; Jong-Hyeon Jeong; Youngjo Lee
Journal:  Lifetime Data Anal       Date:  2019-02-07       Impact factor: 1.588

3.  Statistical inference in mixed models and analysis of twin and family data.

Authors:  Xueqin Wang; Xiaobo Guo; Mingguang He; Heping Zhang
Journal:  Biometrics       Date:  2011-02-09       Impact factor: 2.571

  3 in total

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