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We furthermore investigated by a logistic regression analysis with a robust variance estimate (generalized estimation equation approach) the influence of the RNFL on the likelihood for a P100 below 118 ms and for a MD variation ≥ ±2.5 decibel.
The possible influence of demographic factors on changes in response rates across time was analysed, using a repeated measures logistic regression, estimated using Generalized Estimation Equations.
OR: Odds ratio; RR: Relative risk; MLE: Maximum likelihood estimator; GEE: Generalized estimation equation; SE: Standard error; MSE: Mean square error.
Significance was estimated using the generalized estimation equations (PGEE) and multivariate analysis of variation (PMAN); the required sample size by generalized estimation equations (nGEE as number of subjects) and power analysis (nPA).
Time-series data were analyzed using a general linear model extended by generalized estimation equations (GEE).
Time-series data were analyzed by using a general linear model extended by generalized estimation equations (GEEs).
The generalized estimation equation model was used to estimate odds ratios and corresponding 95% confidence intervals.
We will use both the general linear model (i.e. "basic regression") and its extension: Generalized Estimation Equations (GEE) (i.e. "basic regression for repeated measures").
Generalized estimation equations controlling for sociodemographic characteristics and disease status were used to estimate the relationship between caregiving and 4 outcomes: self-rated physical health, mobility limitation, life satisfaction, and depressive symptoms.
Marginal models, such as the Generalized Estimation Equation (GEE) method, adjust for the clustering nature of data and estimate the standard error of the estimated parameters correctly.
Generalized estimation equations were used for statistical analysis.
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