Ascertainment-Adjusted Maximum Likelihood Estimation for the Additive Genetic Gamma Frailty Model

Sun, Wanlong; Li, Hongzhe

doi:10.1023/b:lida.0000036390.15378.5e

Cited by 5 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach cannot yield unbiased population-based parameter estimates. In fact, this is possible only through proper construction of the ascertainment-adjusted likelihood [13,31], but the probability of the sampled families is often unknown and hard to model. Recently, Jonker et al [15] proposed a correlated gamma frailty model for linkage similar to the one described in this paper.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Score test for age at onset genetic linkage analysis in selected sibling pairs

Callegaro

Houwelingen

Houwing-Duistermaat

2009

Statistics in Medicine

View full text Add to dashboard Cite

A new score statistic is derived, which uses information from registries (age-specific incidences) and family studies (sib-sib marginal correlation) to weight affected sibling pairs according to their age at onset. Age at onset of sibling pairs is modelled by a gamma frailty model. From this model we derive a bivariate survival function, which depends on the marginal survival and on the marginal correlation. The score statistic for linkage is a classical nonparametric linkage (NPL) statistic where the identical by descent sharing is weighted by a particular function of the age at onset data. Since the statistic is based on survival models, it can also be applied to discordant and healthy sibling pairs. Simulation studies show that the proposed method is robust and more powerful than standard NPL methods. As illustration we apply the new score statistic to data from a breast cancer study.

show abstract

Section: Discussionmentioning

confidence: 99%

“…For genetic linkage analysis, methods based on frailty models have been proposed for survival data (age at onset) [10][11][12][13][14][15]. Commenges [10] proposed a frailty model where the linkage effect is decomposed into the sum of two random effects representing the two alleles at the locus linked to a disease susceptibility gene.…”

Section: Introductionmentioning

confidence: 99%

Score test for age at onset genetic linkage analysis in selected sibling pairs

Callegaro

Houwelingen

Houwing-Duistermaat

2009

Statistics in Medicine

View full text Add to dashboard Cite

show abstract

“…Due to conditioning, one may expect loss of efficiency in parameter estimates. Sun and Li (2004) recently proposed and evaluated two approaches based on conditional prospective likelihood and conditional ascertainment corrected likelihood for the additive genetic gamma frailty model in order to estimate the baseline hazard function based on the family data collected for linkage analysis. However, such an ascertainment correction procedure requires knowledge of the population distribution of the family structures and family sizes, which can be difficult to obtain.…”

Section: Ascertainment Correctionmentioning

confidence: 99%

Survival Analysis Methods in Genetic Epidemiology

Li¹

2007

Current Topics in Human Genetics

Self Cite

View full text Add to dashboard Cite

Mapping genes for complex human diseases is a challenging problem due to the fact that many such diseases are due to both genetic and enviromental risk factors and many also exhibit phenotypic heterogeneity, such as variable age of onset. Information on variable age of disease onset is often a good indicator for disease heterogeneity and incorporation of such information together with enviromental risk factors into genetic analysis should lead to more powerful tests for genetic analysis. Due to the problem of censoring, survival analysis methods have proved to be very useful for genetic analysis. In this paper, I review some recent methodological developments on integrating modern survival analysis methods and human genetics in order to rigorously incorporate both age of onset and enviromental covariates data into aggregation analysis, segregation analysis, linkage analysis, association analysis and gene risk characterization. I also briefly discuss the issue of ascertainment correction and survival analysis methods for high-dimensional genomic data. Finally, I outline several areas that need further methodological developments. Survival Analysis Methods in Genetic Epidemiology AbstractMapping genes for complex human diseases is a challenging problem due to the fact that many such diseases are due to both genetic and environmental risk factors and many also exhibit phenotypic heterogeneity, such as variable age of onset. Information on variable age of disease onset is often a good indicator for disease heterogeneity and incorporation of such information together with environmental risk factors into genetic analysis should lead to more powerful tests for genetic analysis. Due to the problem of censoring, survival analysis methods have proved to be very useful for genetic analysis. In this paper, I review some recent methodological developments on integrating modern survival analysis methods and human genetics in order to rigorously incorporate both age of onset and environmental covariates data into aggregation analysis, segregation analysis, linkage analysis, association analysis and gene risk characterization. I also briefly discuss the issue of ascertainment correction and survival analysis methods for high-dimensional genomic data. Finally, I outline several areas that need further methodological developments.

show abstract

“…Several papers followed up on this work [8,9]. Sun and Li [10] used a frailty model to analyze clustered survival data, where random effects are used to describe a familial structure. In their work, a family is ascertained when at least two members have experience the failure before a certain age.…”

Section: Introductionmentioning

confidence: 99%

Ascertainment correction in frailty models for recurrent events data

Balan

Jonker

Johannesma

et al. 2016

Statistics in Medicine

View full text Add to dashboard Cite

In retrospective studies involving recurrent events, it is common to select individuals based on their event history up to the time of selection. In this case, the ascertained subjects might not be representative for the target population, and the analysis should take the selection mechanism into account. The purpose of this paper is two-fold. First, to study what happens when the data analysis is not adjusted for the selection and second, to propose a corrected analysis. Under the Andersen-Gill and shared frailty regression models, we show that the estimators of covariate effects, incidence, and frailty variance can be biased if the ascertainment is ignored, and we show that with a simple adjustment of the likelihood, unbiased and consistent estimators are obtained. The proposed method is assessed by a simulation study and is illustrated on a data set comprising recurrent pneumothoraces. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Ascertainment-Adjusted Maximum Likelihood Estimation for the Additive Genetic Gamma Frailty Model

Cited by 5 publications

References 11 publications

Score test for age at onset genetic linkage analysis in selected sibling pairs

Score test for age at onset genetic linkage analysis in selected sibling pairs

Survival Analysis Methods in Genetic Epidemiology

Ascertainment correction in frailty models for recurrent events data

Contact Info

Product

Resources

About