Marginal effects—quantifying the effect of changes in risk factors in logistic regression models


8 March 2019 - Marginal effects can be used to express how the predicted probability of a binary outcome changes with a change in a risk factor. 

For example, how does 1-year mortality risk change with a 1-year increase in age or for a patient with diabetes compared with a patient without diabetes? This approach can make the results more easily understood. Marginal effects often are reported with logistic regression analyses to communicate and quantify the incremental risk associated with each factor.

In a 2013 article in JAMA Psychiatry, Cummings et al3 studied factors that predicted access to outpatient mental health facilities that accept Medicaid. Their main outcome had 3 categories, which were labeled “no access,” “some access,” and “good access.” An ordered logistic regression model was developed and results were presented as the change in the probability of each outcome for a change in certain demographic factors.

Read JAMA Guide to Statistics and Methods article

Michael Wonder

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Michael Wonder