There are several procedures in the SAS System for statistical modeling. Most statisticians who use the SAS system are familiar with procedures such as PROC REG and PROC GLM for fitting general linear models. However PROC GENMOD can handle these general linear models as well as more complex ones such as logistic models, loglinear models or models for count data. In addition, the main advantage of PROC GENMOD is that it can accommodate the analysis of correlated data. In this paper, we will discuss the use of PROC GENMOD to analyze simple as well as more complex statistical models. When other procedures are available to perform the same analysis, we will highlight the options from these procedures that may be missing in PROC GENMOD but might...
Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers....
Generalized linear models are highly useful statistical tools in a broad array of business applicati...
Analysts who work frequently with linear models using the SAS System ® often use the LSMEANS stateme...
A user-friendly SAS macro application to perform all possible model selection of fixed effects inclu...
Generalized linear models provide a framework for relating response and predictor variables by exten...
Many procedures in SAs/STAT ~ can be used to perform 10' gistic regression analysis: CATMOD, GE...
Often, when a response percent change from baseline in a clinical parameter is not normally distribu...
Traditional epidemiologic research using cross sectional, retrospective, and prospective designs oft...
This paper presents the advantages of using PROC MIXED versus PROC GLM as a solution for hierarchica...
PROC LOGISTIC has many useful features for model selection and the understanding of fitted models. T...
Cox proportional hazards model is a commonly used model in providing hazard ratio to compare surviva...
This document outlines the use of two procedures capable of modeling repeated respiratory symptom da...
The SAS system is known not to support any more or less developed Bayesian method. At the same time ...
The study presents useful examples of fitting hierarchical linear models using the PROC MIXED statis...
Abstract niet beschikbaarThe SAS procedure GLM (General Linear Model) uses the method of least squar...
Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers....
Generalized linear models are highly useful statistical tools in a broad array of business applicati...
Analysts who work frequently with linear models using the SAS System ® often use the LSMEANS stateme...
A user-friendly SAS macro application to perform all possible model selection of fixed effects inclu...
Generalized linear models provide a framework for relating response and predictor variables by exten...
Many procedures in SAs/STAT ~ can be used to perform 10' gistic regression analysis: CATMOD, GE...
Often, when a response percent change from baseline in a clinical parameter is not normally distribu...
Traditional epidemiologic research using cross sectional, retrospective, and prospective designs oft...
This paper presents the advantages of using PROC MIXED versus PROC GLM as a solution for hierarchica...
PROC LOGISTIC has many useful features for model selection and the understanding of fitted models. T...
Cox proportional hazards model is a commonly used model in providing hazard ratio to compare surviva...
This document outlines the use of two procedures capable of modeling repeated respiratory symptom da...
The SAS system is known not to support any more or less developed Bayesian method. At the same time ...
The study presents useful examples of fitting hierarchical linear models using the PROC MIXED statis...
Abstract niet beschikbaarThe SAS procedure GLM (General Linear Model) uses the method of least squar...
Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers....
Generalized linear models are highly useful statistical tools in a broad array of business applicati...
Analysts who work frequently with linear models using the SAS System ® often use the LSMEANS stateme...