When are GEE appropriate for analyzing correlated binary data?

In a logit model, however, the effect of X on Y is a main effect. If you are analyzing a set of categorical variables, and one of them is clearly a “response” ... Jan 22, 2025 ... This systematic review highlights important gaps in the use of logistic regression with complex survey data, such as overlooking data dependencies, survey ... The short answer is: Logistic regression is considered a generalized linear model because the outcome always depends on the sum of the inputs and parameters. Or ... Jul 15, 2022 ... The logistic regression model is an analysis technique that helps predict the probability of an event happening in the future. This article ... A more universally useful logistic regression model to distinguish between a malignant and benign adnexal tumor before surgery. Jun 20, 2012 ... The c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components. Sep 28, 2021 ... To date, our hospital is the only institution in the country that has adopted the ultrasound-based protocol IOTA logistic regression model LR2 ... 1 Introduction. In logistic regression models, if the sample size is small or if a predictor is strongly associated with one of the possible outcomes (say, ... Mar 26, 2020 ... While a simple logistic regression model has a binary outcome and one predictor, a multiple or multivariable logistic regression model finds ... Dec 19, 2024 ... Logistic regression is a classification algorithm. It is used to predict a binary outcome based on a set of independent variables.