SMHS BigDataBigSci GEE - Revision history

Pineaumi: /* See also */

2016-05-23T19:52:39Z

‎See also

Pineaumi: /* Overview */

2016-05-23T19:50:18Z

‎Overview

Pineaumi: /* Questions */

2016-05-23T19:49:33Z

‎Questions

Pineaumi: /* Model-based Analytics - Generalized Estimating Equation (GEE) Modeling */

2016-05-23T19:49:27Z

‎Model-based Analytics - Generalized Estimating Equation (GEE) Modeling

Pineaumi: /* Footnotes */

2016-05-23T19:47:11Z

‎Footnotes

Pineaumi: /* Overview */

2016-05-23T19:19:44Z

‎Overview

Imoubara at 13:44, 23 May 2016

2016-05-23T13:44:20Z

Imoubara: /* Model-based Analytics - Generalized Estimating Equation (GEE) Modeling */

2016-05-18T19:55:31Z

‎Model-based Analytics - Generalized Estimating Equation (GEE) Modeling

Imoubara at 18:27, 16 May 2016

2016-05-16T18:27:53Z

Imoubara at 18:27, 16 May 2016

2016-05-16T18:27:04Z

← Older revision		Revision as of 19:52, 23 May 2016
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−	~~==See also==~~
−	*[[SMHS_BigDataBigSci\| Back to Model-based Analytics]]
−	*[[SMHS_BigDataBigSci_GCM\| Back to Growth Curve Modeling ]]
−	*[[SMHS_BigDataBigSci_SEM\| Back to Structural Equation Modeling (SEM)]]

−	~~<hr>~~
−	* SOCR Home page: http://www.socr.umich.edu
−
−	~~{{translate\|pageName=http://wiki.socr.umich.edu/index.php?title=SMHS_BigDataBigSci_GEE}}~~

← Older revision		Revision as of 19:50, 23 May 2016
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−	~~==Overview==~~
−
−	GEE is a marginal longitudinal method that directly assesses the mean relations of interest (i.e., how the mean dependent variable changes over time), accounting for covariances among the observations within subjects, and getting a better estimate and valid significance tests of the relations. Thus, GEE estimates two different equations, (1) for the mean relations, and (2) for the covariance structure. An advantage of GEE over random-effect models is that it does not require the dependent variable to be normally distributed. However, a disadvantage of GEE is that it is less flexible and versatile – commonly employed algorithms for it require a small-to-moderate number of time points evenly (or approximately evenly) spaced, and similarly spaced across subjects. Nevertheless, it is a little more flexible than repeated-measure ANOVA because it permits some missing values and has an easy way to test for and model away the specific form of autocorrelation within subjects.
−
−	GEE is mostly used when the study is focused on uncovering the population average effect of a covariate vs. the individual specific effect. These two things are only equivalent for linear models, but not in non-linear models.
−
	==See also==		==See also==
	*[[SMHS_BigDataBigSci\| Back to Model-based Analytics]]		*[[SMHS_BigDataBigSci\| Back to Model-based Analytics]]

← Older revision		Revision as of 19:49, 23 May 2016
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−	~~==Questions==~~
−
−	*How to represent dependencies in linear models and examine causal effects?
−	*Is there a way to study population average effects of a covariate against specific individual effects?
−
	==Overview==		==Overview==

← Older revision		Revision as of 19:49, 23 May 2016
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−	~~==[[SMHS_BigDataBigSci\| Model-based Analytics]] - Generalized Estimating Equation (GEE) Modeling ==~~
−
	==Questions==		==Questions==

← Older revision		Revision as of 19:47, 23 May 2016
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	GEE is mostly used when the study is focused on uncovering the population average effect of a covariate vs. the individual specific effect. These two things are only equivalent for linear models, but not in non-linear models.		GEE is mostly used when the study is focused on uncovering the population average effect of a covariate vs. the individual specific effect. These two things are only equivalent for linear models, but not in non-linear models.
−
−	~~===Footnotes===~~
−
−	*<sup>3</sup> http://www.jstatsoft.org/v15/i02/
−
−	*<sup>4</sup> https://books.google.com/books?id=mdEqBgAAQBAJ

	==See also==		==See also==

← Older revision		Revision as of 19:19, 23 May 2016
Line 11:		Line 11:

	GEE is mostly used when the study is focused on uncovering the population average effect of a covariate vs. the individual specific effect. These two things are only equivalent for linear models, but not in non-linear models.		GEE is mostly used when the study is focused on uncovering the population average effect of a covariate vs. the individual specific effect. These two things are only equivalent for linear models, but not in non-linear models.
		+
		+	===Footnotes===
		+
		+	*<sup>3</sup> http://www.jstatsoft.org/v15/i02/
		+
		+	*<sup>4</sup> https://books.google.com/books?id=mdEqBgAAQBAJ

	==See also==		==See also==

@@ Line 3: / Line 3: @@
 ==Questions==
-<li>How to represent dependencies in linear models and examine causal effects?</li>
+*How to represent dependencies in linear models and examine causal effects?
-<li>Is there a way to study population average effects of a covariate against specific individual effects?</li>
+*Is there a way to study population average effects of a covariate against specific individual effects?
 ==Overview==

@@ Line 1: / Line 1: @@
 ==[[SMHS_BigDataBigSci| Model-based Analytics]] - Generalized Estimating Equation (GEE) Modeling ==
-<b>Questions</b>
+==Questions==
 <li>How to represent dependencies in linear models and examine causal effects?</li>
 <li>Is there a way to study population average effects of a covariate against specific individual effects?</li>
-<b>Overview</b>
+==Overview==
 GEE is a marginal longitudinal method that directly assesses the mean relations of interest (i.e., how the mean dependent variable changes over time), accounting for covariances among the observations within subjects, and getting a better estimate and valid significance tests of the relations. Thus, GEE estimates two different equations, (1) for the mean relations, and (2) for the covariance structure. An advantage of GEE over random-effect models is that it does not require the dependent variable to be normally distributed. However, a disadvantage of GEE is that it is less flexible and versatile – commonly employed algorithms for it require a small-to-moderate number of time points evenly (or approximately evenly) spaced, and similarly spaced across subjects. Nevertheless, it is a little more flexible than repeated-measure ANOVA because it permits some missing values and has an easy way to test for and model away the specific form of autocorrelation within subjects.
 GEE is mostly used when the study is focused on uncovering the population average effect of a covariate vs. the individual specific effect. These two things are only equivalent for linear models, but not in non-linear models.
 ==See also==

@@ Line 2: / Line 2: @@
 <b>Questions</b>
-* How to represent dependencies in linear models and examine causal effects?
+<li>How to represent dependencies in linear models and examine causal effects?</li>
-* Is there a way to study population average effects of a covariate against specific individual effects?
+<li>Is there a way to study population average effects of a covariate against specific individual effects?</li>
 <b>Overview</b>
@@ Line 14: / Line 14: @@
 ==See also==
-<li>[[SMHS_BigDataBigSci| Back to Model-based Analytics]] </li>
+*[[SMHS_BigDataBigSci| Back to Model-based Analytics]]
-<li> [[SMHS_BigDataBigSci_GCM| Back to Growth Curve Modeling ]]</li>
+*[[SMHS_BigDataBigSci_GCM| Back to Growth Curve Modeling ]]
-<li>[[SMHS_BigDataBigSci_SEM| Back to Structural Equation Modeling (SEM)]]</li>
+*[[SMHS_BigDataBigSci_SEM| Back to Structural Equation Modeling (SEM)]]
 <hr>