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linear and logistic marginal models (generalized estimating equations)  (SAS institute)


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    SAS institute linear and logistic marginal models (generalized estimating equations)
    Linear And Logistic Marginal Models (Generalized Estimating Equations), supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Average 90 stars, based on 1 article reviews
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    MRI metrics analyzed with respect to functional disability score for individuals with PLP1 mutations. A generalized linear model (GLM) analyses based on the GEE (generalized estimating equation) methodology was performed with FDS subgroups as the main effect term and age as a covariate to test FDS subgroup differences (JMP; SAS Institute Inc.). The total brain volume (TBV) was used as an additional covariate when comparing white matter volume and the corpus callosum area. The statistical results of the GLM analyses were based on the “Chi-Square” statistics and associated p-values. P-values of 0.05 or less were considered significant. Since all three measurements were not performed on all subjects, a median split for each measure was conducted.

    Journal: Journal of the neurological sciences

    Article Title: Neuroradiologic correlates of clinical disability and progression in the X-Linked leukodystrophy Pelizaeus–Merzbacher disease

    doi: 10.1016/j.jns.2013.08.030

    Figure Lengend Snippet: MRI metrics analyzed with respect to functional disability score for individuals with PLP1 mutations. A generalized linear model (GLM) analyses based on the GEE (generalized estimating equation) methodology was performed with FDS subgroups as the main effect term and age as a covariate to test FDS subgroup differences (JMP; SAS Institute Inc.). The total brain volume (TBV) was used as an additional covariate when comparing white matter volume and the corpus callosum area. The statistical results of the GLM analyses were based on the “Chi-Square” statistics and associated p-values. P-values of 0.05 or less were considered significant. Since all three measurements were not performed on all subjects, a median split for each measure was conducted.

    Article Snippet: A generalized linear model (GLM) analyses based on the GEE (generalized estimating equation) methodology was performed with FDS subgroups as the main effect term and age as a covariate to test FDS subgroup differences (JMP; SAS Institute Inc.).

    Techniques: Functional Assay

    MRI metrics analyzed with respect to functional disability score for individuals with PLP1 mutations. A generalized linear model (GLM) analyses based on the GEE (generalized estimating equation) methodology was performed with FDS subgroups as the main effect term and age as a covariate to test FDS subgroup differences (JMP; SAS Institute Inc.). The total brain volume (TBV) was used as an additional covariate when comparing white matter volume and the corpus callosum area. The statistical results of the GLM analyses were based on the “Chi-Square” statistics and associated p-values. P-values of 0.05 or less were considered significant. Since all three measurements were not performed on all subjects, a median split for each measure was conducted.

    Journal: Journal of the neurological sciences

    Article Title: Neuroradiologic correlates of clinical disability and progression in the X-Linked leukodystrophy Pelizaeus–Merzbacher disease

    doi: 10.1016/j.jns.2013.08.030

    Figure Lengend Snippet: MRI metrics analyzed with respect to functional disability score for individuals with PLP1 mutations. A generalized linear model (GLM) analyses based on the GEE (generalized estimating equation) methodology was performed with FDS subgroups as the main effect term and age as a covariate to test FDS subgroup differences (JMP; SAS Institute Inc.). The total brain volume (TBV) was used as an additional covariate when comparing white matter volume and the corpus callosum area. The statistical results of the GLM analyses were based on the “Chi-Square” statistics and associated p-values. P-values of 0.05 or less were considered significant. Since all three measurements were not performed on all subjects, a median split for each measure was conducted.

    Article Snippet: FDS subgroup differences were tested using a generalized linear model (GLM) analysis based on the GEE (generalized estimating equation) methodology with FDS subgroup as the main effect term and age as a covariate (JMP; SAS Institute Inc.).

    Techniques: Functional Assay