**************** **************** ** logistic ** logistic regression **************** **************** Overview: The "logistic" command estimates a logistic regression for a binary outcome variable. The default summary is in terms of exp(beta_j), which is an odds ratio when beta_j is the coefficient of a main effect in the model. Usage: "logistic Dvar X1 X2 X3" Where - Dvar is the disease variable (1=disease, 0=control) Xj are predictor variables Summaries: The "logistic" command returns estimates of adjusted odds ratos, exp( beta_j ). After fitting the model using logistic, the command "logit" will display the regression coefficients, beta_j. Options: (1) "logistic y x1 x2 [freq=count]" -- this is used when the data are in a "grouped" format with the number of cases/controls that have a certain covariate combination given by the variable "freq". (2) "xi: logistic y i.x1 x2" -- this is used to create dummy variables for variable x1. **************** **************** ** lrtest ** logistic regression and likelihood ratio tests **************** **************** Overview: The "logistic" command estimates a logistic regression for a binary outcome variable. We can save the maximized log-likelihood for any fitted model using "lrtest". Then we can compare nested models using "lrtest". Usage: "logistic Y X1" "lrtest, saving(1)" "logistic Y X1 X2" "lrtest, saving(2)" "logistic Y X1 X2 X3 X4" "lrtest, saving(3)" "lrtest, using(2) model(1)" "lrtest, using(3) model(2)" Summaries: The "lrtest" command both stores fitted model log-likelihood values using the "saving" option and tests for zero coefficients that correspond to nested models.