Testing the heritability and parent‐of‐origin hypotheses for ages at onset of psoriatic arthritis under biased sampling

Lakhal‐Chaieb, Lajmi; Cook, Richard J.; Zhong, Yujie

doi:10.1111/biom.13138

Cited by 3 publications

(6 citation statements)

References 21 publications

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“…We explore use of piecewise constant baseline hazards in the application to increase robustness against misspecification of the parametric form of the baseline hazard. The dependence structure we form is based on scientific knowledge regarding shared genetic information based on the kinship of family members, and models such as ours have been adopted in other settings involving family studies 15 . The Gaussian copula is the most natural copula to adopt which accommodates the flexible dependence structures needed.…”

Section: Discussionmentioning

confidence: 99%

“…Third, within the copula framework methods based on composite likelihood 13 and conditional second‐order estimating equations 14 have been developed for modeling the dependence structure within families. Moreover Lakhal‐Chaieb et al 15 developed score tests for the effects of rare variants in family studies where the within family dependence was also modeled by a Gaussian copula while exploiting the kinship structure.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of secondary failure time responses in studies with response‐dependent sampling schemes

Zhong,

Cook,

2023

Statistics in Medicine

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Response‐dependent sampling is routinely used as an enrichment strategy in the design of family studies investigating the heritable nature of disease. In addition to the response of primary interest, investigators often wish to investigate the association between biomarkers and secondary responses related to possible comorbidities. Statistical analysis regarding genetic biomarkers and their association with the secondary outcome must address the biased sampling scheme involving the primary response. In this article, we develop composite likelihoods and two‐stage estimation procedures for such secondary analyses in which the within‐family dependence structure for the primary and secondary outcomes is modeled via a Gaussian copula. The dependence among responses within family members is modeled based on kinship coefficients. Auxiliary data from independent individuals are exploited by augmenting the composite likelihoods to increase precision of marginal parameter estimates and enhance the efficiency of estimators of the dependence parameters. Simulation studies are carried out to evaluate the finite sample performance of the proposed method, and an application to a motivating family study in psoriatic arthritis is given for illustration.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of secondary failure time responses in studies with response‐dependent sampling schemes

Zhong,

Cook,

2023

Statistics in Medicine

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“…If case-control probands are available, it would be useful to compare the robustness to misspecification of model assumption (Chatterjee et al, 2006) and compare the efficiency with the case-only probands family studies. It is natural to extend our model to allow for different dependence structures in families using a more flexible Gaussian copula (Zhong and Cook, 2016;Lakhal-Chaieb et al, 2018). As we have shown in the simulation studies in Sections 4 and 5.2, the use of auxiliary data improves efficiency in estimating the marginal parameters related to disease onset and the dependence parameter, so further exploration of the relative value of different types of auxiliary data would be of interest as this would have bearing on the power of the design.…”

Section: Discussionmentioning

confidence: 99%

The illness-death model for family studies

Lee

Cook

2019

Biostatistics

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Summary Family studies involve the selection of affected individuals from a disease registry who provide right-truncated ages of disease onset. Coarsened disease histories are then obtained from consenting family members, either through examining medical records, retrospective reporting, or clinical examination. Methods for dealing with such biased sampling schemes are available for continuous, binary, and failure time responses, but methods for more complex life history processes are less developed. We consider a simple joint model for clustered illness-death processes which we formulate to study covariate effects on the marginal intensity for disease onset and to study the within-family dependence in disease onset times. We construct likelihoods and composite likelihoods for family data obtained from biased sampling schemes. In settings where the disease is rare and data are insufficient to fit the model of interest, we show how auxiliary data can augment the composite likelihood to facilitate estimation. We apply the proposed methods to analyze data from a family study of psoriatic arthritis carried out at the University of Toronto Psoriatic Arthritis Registry.

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“…When the within‐family association is driven primarily by genetic factors, a more general dependence structure may be appealing in which separate dependence parameters accommodate an association that depends on the kinship of pairs of family members. Zhong and Cook 6 used a three‐parameter Gaussian copula to accommodates this kind of association, while Lakhal‐Chaieb et al 9 used a Gaussian copula with a single dependence parameter which was scaled according to the kinship of different pairs of family members. The Gaussian copula with a general correlation matrix can be written as

𝒞 (u_{i 0}, u_{i 1}, \dots, u_{i m_{i}}, ϕ) = Φ_{m_{i} + 1} false(Φ^{- 1} false(u_{i 0} false), Φ^{- 1} false(u_{i 1} false), \dots, Φ^{- 1} false(u_{i m_{i}} false); ϕ false),

where

Φ^{- 1} (\cdot)

is the inverse cumulative distribution function of a standard normal random variable (r.v.…”

Section: Some Extensionsmentioning

confidence: 99%

“…5 Zhong and Cook 6 consider the use of composite likelihood and Zhong and Cook 7 develop a class of second-order estimating functions for the study of the dependence structure within families. Lakhal-Chaieb et al 8 uses copula functions for the development of score tests for the effects of rare variants in family studies; see also Lakhal-Chaieb et al 9 In these latter papers, the dependence between onset times within families was modeled using copula functions rather than the more common approach based on frailty models. 5 We consider the setting in which there is a large registry of affected individuals from which probands may be selected for further study.…”

Section: Introductionmentioning

confidence: 99%

Selection models for efficient two‐phase design of family studies

Zhong

Cook

2020

Statistics in Medicine

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Family studies routinely employ biased sampling schemes in which individuals are randomly chosen from a disease registry and genetic and phenotypic data are obtained from their consenting relatives. We view this as a two-phase study and propose the use of an efficient selection model for the recruitment of families to form a phase II sample subject to budgetary constraints. Simple random sampling, balanced sampling and use of an approximately optimal selection model are considered where the latter is chosen to minimize the variance of parameters of interest. We consider the setting where family members provide current status data with respect to the disease and use copula models to address within-family dependence. The efficiency gains from the use of an optimal selection model over simple random sampling and balanced sampling schemes are investigated as is the robustness of optimal sampling to model misspecification. An application to a family study on psoriatic arthritis is given for illustration.

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Testing the heritability and parent‐of‐origin hypotheses for ages at onset of psoriatic arthritis under biased sampling

Cited by 3 publications

References 21 publications

Analysis of secondary failure time responses in studies with response‐dependent sampling schemes

Analysis of secondary failure time responses in studies with response‐dependent sampling schemes

The illness-death model for family studies

Selection models for efficient two‐phase design of family studies

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