logistic regression

The most traditional approach for analyzing binary outcome data is logistic regression, where the estimated parameters are interpreted as log odds ratios or, if exponentiated, as odds ratios (ORs). No one other than statisticians (and maybe not even statisticians) finds the odds ratio to be a very intuitive statistic, and many feel that a risk difference or risk ratio/relative risks (RRs) are much more interpretable. Indeed, there seems to be a strong belief that readers will, more often than not, interpret odds ratios as risk ratios. [Read More]

About two years ago, someone inquired whether simstudy had the functionality to generate data from a logistic model with a specific AUC. It did not, but now it does, thanks to a paper by Peter Austin that describes a nice algorithm to accomplish this. The paper actually describes a series of related algorithms for generating coefficients that target specific prevalence rates, risk ratios, and risk differences, in addition the AUC. [Read More]