We consider the case when a covariate variable is missing at random such that the selection probability of the validation set depends only on observed data. Breslow and Cain (1988) proposed a ...

With many nuisance parameters to eliminate and missing covariates, many investigators exclude any subject with missing covariates and then use conditional logistic regression, often called a ...

In addition to predicting the value of a variable (e.g., a patient will survive), logistic regression can also predict the associated probability (e.g., the patient has a 75% chance of survival).

The risk model was developed using conditional logistic regression. The multivariate model was built up in two phases.

What are the advantages of logistic regression over decision trees? This question was originally answered on Quora by Claudia Perlich.