Score Test for Linkage in Generalized Linear Models

Lebrec, Jérémie; Houwelingen, Hans C. van

doi:10.1159/000101418

Cited by 7 publications

(20 citation statements)

References 38 publications

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“…Since the breakthrough made by Liang (1987), homogeneity testing has flourished and been applied to generalized linear models (Hamerle, 1990;Jacqmin-Gadda and Commenges, 1995;Lin, 1997;Zhu and Zhang, 2006) and to the proportional hazards model (Commenges and Andersen, 1995); see Commenges and JacqminGadda (1997), Goeman, van de Geer, and van Houwelingen (2006) for general studies. The score test has been used for testing the fit of a model (Le Cessie and Van Houwelingen, 1995), in multiple testing situations (Liquet and Commenges, 2005;Cook, Gold, and Li, 2007;Commenges and Liquet, 2008), in genetic linkage (Commenges, 1994;Dudoit and Speed, 1999;Putter et al, 2002;Lebrec and van Houwelingen, 2007) and in transmission/disequilibrium tests as well as in association studies (Clayton, 1999;Goeman et al, 2004) and familial aggregation studies (HouwingDuistermaat et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

Score Tests for Exploring Complex Models: Application to HIV Dynamics Models

2010

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In biostatistics, more and more complex models are being developed. This is particularly the case in system biology. Fitting complex models can be very time-consuming, since many models often have to be explored. Among the possibilities are the introduction of explanatory variables and the determination of random effects. The particularity of this use of the score test is that the null hypothesis is not itself very simple; typically, some random effects may be present under the null hypothesis. Moreover, the information matrix cannot be computed, but only an approximation based on the score. This article examines this situation with the specific example of HIV dynamics models. We examine the score test statistics for testing the effect of explanatory variables and the variance of random effect in this complex situation. We study type I errors and the statistical powers of this score test statistics and we apply the score test approach to a real data set of HIV-infected patients.

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Section: Introductionmentioning

confidence: 99%

Score Tests for Exploring Complex Models: Application to HIV Dynamics Models

2010

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“…Using this model, Pankratz et al [5] proposed a likelihood-ratio approach for linkage. In the spirit of Lebrec and Houwelingen [7], we derive a robust and simpler score test, using an approximation of the likelihood around the null random effect.…”

Section: Introductionmentioning

confidence: 99%

New score tests for age-at-onset linkage analysis in general pedigrees

Callegaro

Helmer

et al. 2009

BMC Proc

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Our aim is to develop methods for mapping genes related to age at onset in general pedigrees. We propose two score tests, one derived from a gamma frailty model with pairwise likelihood and one derived from a log-normal frailty model with approximated likelihood around the null random effect. The score statistics are weighted nonparametric linkage statistics, with weights depending on the age at onset. These tests are correct under the null hypothesis irrespective of the weight used. They are simple, robust, computationally fast, and can be applied to large, complex pedigrees. We apply these methods to simulated data and to the Genetic Analysis Workshop 16 Framingham Heart Study data set. We investigate the time to the first of three events: hard coronary heart disease, diabetes, or death from any cause. We use a two-step procedure. In the first step, we estimate the population parameters under the null hypothesis of no linkage. In the second step, we apply the score tests, using the population parameters estimated in the first step.

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“…In order to solve this problem, we derive also a score test from a log-normal frailty model which can easily be applied to general pedigrees. In the spirit of the work of Lebrec and van Houwelingen [11] , the score test is derived from a Taylor approximation of the log-likelihood around random effect equal to zero. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Robust Age at Onset Linkage Analysis in Nuclear Families

Callegaro¹,

Houwelingen²,

Houwing-Duistermaat³

2009

Hum Hered

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Objective: Standard methods for linkage analysis ignore the phenotype of the parents when they are not genotyped. However, this information can be useful for gene mapping. In this paper we propose methods for age at onset genetic linkage analysis in sibling pairs, taking into account parental age at onset. Methods:Two new score statistics are derived, one from an additive gamma frailty model and one from a log-normal frailty model. The score statistics are classical non-parametric linkage (NPL) statistics weighted by a function of the age at onset of the four family members. The weight depends on information from registries (age-specific incidences) and family studies (sib-sib and father-mother correlation). Results:In order to investigate how age at onset of sibs and their parents affect the information for linkage analysis the weight functions were studied for rare and common disease models, realistic models for breast cancer and human lifespan. We studied the performance of the weighted NPL methods by simulations. As illustration, the score statistics were applied to the GAW12 data. The results show that it is useful to include parental age at onset information in genetic linkage analysis.

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Score Test for Linkage in Generalized Linear Models

Cited by 7 publications

References 38 publications

Score Tests for Exploring Complex Models: Application to HIV Dynamics Models

Score Tests for Exploring Complex Models: Application to HIV Dynamics Models

New score tests for age-at-onset linkage analysis in general pedigrees

Robust Age at Onset Linkage Analysis in Nuclear Families

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