Journal ArticleDOI
Approximate inference in generalized linear mixed models
Norman E. Breslow,D. G. Clayton +1 more
TLDR
In this paper, generalized linear mixed models (GLMM) are used to estimate the marginal quasi-likelihood for the mean parameters and the conditional variance for the variances, and the dispersion matrix is specified in terms of a rank deficient inverse covariance matrix.Abstract:
Statistical approaches to overdispersion, correlated errors, shrinkage estimation, and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed model (GLMM). Given an unobserved vector of random effects, observations are assumed to be conditionally independent with means that depend on the linear predictor through a specified link function and conditional variances that are specified by a variance function, known prior weights and a scale factor. The random effects are assumed to be normally distributed with mean zero and dispersion matrix depending on unknown variance components. For problems involving time series, spatial aggregation and smoothing, the dispersion may be specified in terms of a rank deficient inverse covariance matrix. Approximation of the marginal quasi-likelihood using Laplace's method leads eventually to estimating equations based on penalized quasilikelihood or PQL for the mean parameters and pseudo-likelihood for the variances. Im...read more
Citations
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A Flexible Two-Part Random Effects Model for Correlated Medical Costs
TL;DR: A flexible "two-part" random effects model is proposed for correlated medical cost data that is used to analyze pharmacy cost data on 56,245 adult patients clustered within 239 physicians in a mid-western U.S. managed care organization.
Journal ArticleDOI
Fitting Multilevel Models With Ordinal Outcomes: Performance of Alternative Specifications and Methods of Estimation
Daniel J. Bauer,Sonya K. Sterba +1 more
TL;DR: Whether and when fitting multilevel linear models to ordinal outcome data is justified and which estimator to employ when instead fitting multilesvel cumulative logit models to Ordinal data, maximum likelihood (ML), or penalized quasi-likelihood (PQL) is evaluated.
Non-centred parameterisations for hierarchical models and data augmentation.
TL;DR: This paper will investigate the construction of non-centered methods by the use of state space expansion techniques, and will introduce methods for devising partially non- centered parameterisations, many of which are data-dependent.
Journal ArticleDOI
Need-service matching in substance abuse treatment: racial/ethnic differences.
TL;DR: Receipt of access services was related to reduced post-treatment substance use for Latinos and substance abuse counseling and matching services to needs is an effective strategy both for retaining clients in treatment and for reducing post- treatment substance use.
Journal ArticleDOI
Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale
Xihao Li,Zilin Li,Hufeng Zhou,Sheila M. Gaynor,Yaowu Liu,Han Chen,Ryan Sun,Rounak Dey,Donna K. Arnett,Stella Aslibekyan,Christie M. Ballantyne,Lawrence F. Bielak,John Blangero,Eric Boerwinkle,Eric Boerwinkle,Donald W. Bowden,Jai G. Broome,Matthew P. Conomos,Adolfo Correa,L. Adrienne Cupples,Joanne E. Curran,Barry I. Freedman,Xiuqing Guo,George Hindy,Marguerite R. Irvin,Sharon L.R. Kardia,Sekar Kathiresan,Alyna T. Khan,Charles Kooperberg,Cathy C. Laurie,X. Shirley Liu,Michael C. Mahaney,Ani Manichaikul,Lisa W. Martin,Rasika A. Mathias,Stephen T. McGarvey,Braxton D. Mitchell,Braxton D. Mitchell,May E. Montasser,Jill Moore,Alanna C. Morrison,Jeffrey R. O'Connell,Nicholette D. Palmer,Akhil Pampana,Akhil Pampana,Juan M. Peralta,Patricia A. Peyser,Bruce M. Psaty,Bruce M. Psaty,Susan Redline,Susan Redline,Susan Redline,Kenneth Rice,Stephen S. Rich,Jennifer A. Smith,Hemant K. Tiwari,Michael Y. Tsai,Ramachandran S. Vasan,Fei Fei Wang,Daniel E. Weeks,Zhiping Weng,James G. Wilson,James G. Wilson,Lisa R. Yanek,Benjamin M. Neale,Benjamin M. Neale,Shamil R. Sunyaev,Shamil R. Sunyaev,Shamil R. Sunyaev,Gonçalo R. Abecasis,Gonçalo R. Abecasis,Jerome I. Rotter,Cristen J. Willer,Gina M. Peloso,Pradeep Natarajan,Pradeep Natarajan,Xihong Lin,Xihong Lin +77 more
TL;DR: STAAR is a powerful rare variant association test that incorporates variant functional categories and complementary functional annotations using a dynamic weighting scheme based on annotation principal components and is scalable for analyzing large whole-genome sequencing studies.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Journal ArticleDOI
Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.