{"id":13967,"date":"2018-04-19T08:57:52","date_gmt":"2018-04-19T08:57:52","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=13967"},"modified":"2021-12-03T10:35:29","modified_gmt":"2021-12-03T05:05:29","slug":"stat-longitudinal-data-analysis","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/stat-longitudinal-data-analysis\/","title":{"rendered":"4 Important SAS\/STAT Longitudinal Data Analysis Procedures"},"content":{"rendered":"

1. Objective<\/h2>\n

In our last tutorial, we studied\u00a0SAS\/STAT Exact Inference. Today we will look at SAS\/STAT longitudinal data analysis. Moreover, we will see how can we use longitudinal data analysis in SAS\/STAT<\/strong><\/a>. Our focus here will be to understand different procedures: PROC GEE, PROC GLIMMIX, PROC MIXED, PROC GENMOD that can be used for SAS\/STAT longitudinal data analysis. At last, we will discuss some longitudinal analysis example.
\nSo, let’s start with SAS\/STAT Longitudinal Data Analysis.<\/p>\n

\"STAT<\/a>

5 Procedure for Longitudinal Data Analysis in SAS\/STAT<\/p><\/div>\n

2. SAS\/ STAT Longitudinal Data Analysis<\/h2>\n

Longitudinal data arises when you measure a response variable of interest multiple numbers of times on multiple subjects. Thus, longitudinal data has the characteristics of both cross-sectional data and time-series data. The response variables in studies of longitudinal data can be either continuous or discrete.
\nThe basic motive behind a SAS\/STAT Longitudinal data analysis is usually to model the expected value of the response variable as either a linear or nonlinear function of a set of explanatory variables. Statistical analysis of longitudinal data requires an accounting for possible between-subject heterogeneity and within-subject correlation<\/a><\/strong>.
\nLet’s Revise SAS\/STAT Advantages & Disadvantages<\/a><\/strong>
\nSAS\/STAT software provides two approaches for modeling longitudinal data: marginal models (also known as population-average models) and mixed models (also known as subject-specific models).
\nBelow is a sample plot showing a SAS\/STAT longitudinal data analysis representation.<\/p>\n

\"SAS\/STAT<\/a>

SAS\/STAT Longitudinal Data Analysis Example<\/p><\/div>\n

3. Longitudinal Data Analysis\u00a0Procedures in SAS\/STAT<\/h2>\n

Following procedures use to perform SAS\/STAT longitudinal data analysis of a sample data. Each procedure has a different syntax and is used with different type of data in different contexts. Let us explore each one of these.
\nRead About 8 Procedures for Calculating Analysis of Variance<\/a><\/strong><\/p>\n

a. PROC GEE<\/h3>\n

The PROC GEE procedure in SAS\/STAT is a comprehensive tool for analyzing longitudinal data. For longitudinal studies, missing data are common, and they can be caused by dropouts or skipped visits. If missing responses depend on previous responses, the usual GEE approach can lead to biased estimates. So the GEE procedure also implements the weighted GEE method to handle missing responses that are caused by dropouts in longitudinal studies.
\nPROC GEE Syntax-<\/strong><\/p>\n

PROC GEE \u00a0DATASET\r\nCLASS <variable>;\r\nMODEL response= effects <options>;\r\nREPEATED subject=subject effects\/<options>;<\/pre>\n
The PROC GEE, MODEL, and REPEATED statements are required. All other statements can appear only once.
\nPROC GEE\u00a0Example-<\/strong><\/p>\n
data iris;\r\nset sashelp.iris;\r\nrun;\u00a0\r\nproc gee data=iris;\r\nclass species;\r\nmodel sepallength=sepalwidth;\r\nrepeated subject=species;\r\nrun;<\/pre>\n
\"SAS\/STAT<\/a>
SAS\/STAT Longitudinal Data Analysis –\u00a0PROC GEE<\/p><\/div>\n
\"SAS\/STAT<\/a>
Longitudinal Data Analysis in\u00a0SAS\/STAT –\u00a0\u00a0PROC GEE<\/p><\/div>\n
Let’s Discuss 6 SAS\/STAT Bayesian Analysis Procedures<\/a><\/strong><\/p>\n
b. PROC GLIMMIX<\/h3>\n
The PROC GLIMMIX procedure in SAS<\/a>\/STAT performs longitudinal data analysis through which it fits statistical models to data with correlations or nonconstant variability and where the response is not necessarily normally distributed. These models are known as generalized linear mixed models (GLMM). GLMMs, like linear mixed models, assume normal (Gaussian) random effects.
\nPROC GLIMMIX\u00a0Syntax-<\/strong><\/p>\n
PROC GLIMMIX dataset <OPTIONS>;\r\nCLASS <VARIABLES>;\r\nMODEL response= effects <options>;<\/pre>\n
The PROC GLIMMIX and MODEL statements are required. All other statements can appear only once.
\nPROC GLIMMIX Example-<\/strong><\/p>\n
proc glimmix data=sashelp.class;\r\nclass name sex;\r\nmodel age\/height=weight\/solution;\r\nrandom intercept\/subject=weight;\r\nrun;<\/pre>\n
The\u00a0CLASS statement instructs the procedure to treat the variables age and sex as classification variables. The\u00a0MODEL statement specifies the response variable as a sample proportion by using the\u00a0events\/trials<\/em>\u00a0syntax.
\nThe\u00a0SOLUTION\u00a0option in the\u00a0MODEL statement requests a listing of the solutions for the fixed-effects parameter estimates.
\nThe\u00a0RANDOM statement specifies that a random intercept is drawn separately and independently for each center in the study.\u00a0<\/strong>
\nLet’s Learn 7 Simple SAS\/STAT Cluster Analysis Procedures <\/a><\/strong><\/p>\n
\"SAS\/STAT<\/a>
STAT Longitudinal Data Analysis –\u00a0\u00a0PROC GLIMMIX<\/p><\/div>\n
\"SAS\/STAT<\/a>
Longitudinal Data Analysis in SAS\/STAT-\u00a0\u00a0PROC GLIMMIX<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Longitudinal Data Analysis –\u00a0\u00a0PROC GLIMMIX<\/p><\/div>\n
Read About SAS\/STAT Group Sequential Design and Analysis\u00a0<\/a><\/strong><\/p>\n
c. PROC MIXED<\/h3>\n
The PROC MIXED procedure in SAS\/STAT fits different mixed models. Mixed models allow for different sources of variation in data, allows for different variances for groups and takes into account correlation structure of repeated measurements. PROC MIXED fits the structure you select to the data by using the method of\u00a0restricted maximum likelihood (REML)<\/em>,<\/em>\u00a0also known as\u00a0residual maximum likelihood.<\/em>
\nPROC MIXED Syntax-<\/strong><\/p>\n
PROC MIXED dataset OPTIONS;\r\nCLASS <VARIABLES>;\r\n\u00a0\u00a0\u00a0\u00a0 MODEL dependent\u00a0=\u00a0<fixed-effects> <\/ options>;<\/pre>\n
The\u00a0PROC MIXED and\u00a0MODEL statements are required, and the\u00a0MODEL\u00a0statement must appear after the\u00a0CLASS statement if a\u00a0CLASS statement is included.
\nPROC MIXED Example-<\/strong><\/p>\n
ods graphics on;\r\nproc mixed data=SASHELP.CARS\u00a0 plots=all ;\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 class Origin;\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 model MPG_Highway= \/;\r\nrun;<\/pre>\n
The\u00a0MODEL statement first specifies the response (dependent) variable MPG_highway. The explanatory (independent) variables are then listed after the equal (=) sign. Here, no explanatory variables are used.<\/p>\n
\"Longitudinal<\/a>
Longitudinal Data Analysis in SAS\/STAT – PROC MIXED<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS\/STAT Longitudinal Data Analysis –\u00a0PROC MIXED<\/p><\/div>\n
\"Longitudinal<\/a>
Longitudinal Data Analysis in SAS\/STAT –\u00a0PROC MIXED<\/p><\/div>\n
\"SAS\/STAT<\/a>
SAS Longitudinal Data Analysis –\u00a0PROC MIXED<\/p><\/div>\n
d. PROC GENMOD<\/h3>\n
We have already discussed this procedure in detail. You can refer to the following link for the complete tutorial<\/a><\/strong>.
\nSo, This was all about SAS\/STAT Longitudinal Data Analysis\u00a0Tutorial. Hope you like our explanation<\/b>.<\/p>\n
4. Conclusion<\/h2>\n
Hence, this was a complete description and a comprehensive understanding of all the procedures offered by SAS\/STAT longitudinal data analysis. We looked at each one of Procedures: PROC GEE, PROC GLIMMIX, PROC MIXED, and PROC GENMOD with syntax, and how they can use. Hope you all enjoyed it. Stay tuned for more interesting topics in SAS\/STAT, and for any doubts, post it in the comments section below.<\/p>\n
Related Topic-\u00a0SAS\/STAT Categorical Data Analysis Procedure <\/a><\/strong><\/p>\n
1. Objective In our last tutorial, we studied\u00a0SAS\/STAT Exact Inference. Today we will look at SAS\/STAT longitudinal data analysis. Moreover, we will see how can we use longitudinal data analysis in SAS\/STAT. Our focus...<\/p>\n","protected":false},"author":6,"featured_media":13983,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56],"tags":[8398,8399,8401,8402,8403,8404,8405,10020,10021,10022],"class_list":["post-13967","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sas-stat","tag-longitudinal-analysis","tag-longitudinal-data-analysis-in-r","tag-longitudinal-data-analysis-pdf","tag-longitudinal-data-analysis-ppt","tag-longitudinal-data-analysis-sas","tag-longitudinal-data-analysis-spss","tag-longitudinal-data-analysis-stata","tag-proc-gee","tag-proc-gee-example","tag-proc-gee-syntax"],"yoast_head":"\n4 Important SAS\/STAT Longitudinal Data Analysis Procedures - DataFlair<\/title>\n<meta name=\"description\" content=\"What is SAS\/STAT Longitudinal Data Analysis- Procedures for Performing Longitudinal Data Analysis in SAS\/STAT, ROC GEE, PROC GLIMMIX, PROC MIXED,PROC GENMOD\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/stat-longitudinal-data-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"4 Important SAS\/STAT Longitudinal Data Analysis Procedures - 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