Marginal Modeling of Nonnested Multilevel Data using Standard Software
نویسندگان
چکیده
منابع مشابه
Marginal modeling of nonnested multilevel data using standard software.
Epidemiologic data are often clustered within multiple levels that may not be nested within each other. Generalized estimating equations are commonly used to adjust for correlation among observations within clusters when fitting regression models; however, standard software does not currently accommodate nonnested clusters. This paper introduces a simple generalized estimating equation strategy...
متن کاملOriginal Contribution Marginal Modeling of Nonnested Multilevel Data using Standard Software
Epidemiologic data are often clustered within multiple levels that may not be nested within each other. Generalized estimating equations are commonly used to adjust for correlation among observations within clusters when fitting regression models; however, standard software does not currently accommodate nonnested clusters. This paper introduces a simple generalized estimating equation strategy...
متن کاملMarginal modeling of multilevel binary data with time-varying covariates.
We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies on a working independence assumption coupled with a three-step method for obtaining empirical standard errors, and a likelihood-based method implemented using Bayesian computational techniques. Implications of time-varying endogenous covariat...
متن کاملBiometrical modeling of twin and family data using standard mixed model software.
Biometrical genetic modeling of twin or other family data can be used to decompose the variance of an observed response or 'phenotype' into genetic and environmental components. Convenient parameterizations requiring few random effects are proposed, which allow such models to be estimated using widely available software for linear mixed models (continuous phenotypes) or generalized linear mixed...
متن کاملmodeling loss data by phase-type distribution
بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...
ذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: American Journal of Epidemiology
سال: 2006
ISSN: 0002-9262,1476-6256
DOI: 10.1093/aje/kwk020