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.1,2
Norton EC, Dowd BE, Maciejewski ML. Marginal Effects—Quantifying the Effect of Changes in Risk Factors in Logistic Regression Models. JAMA. 2019;321(13):1304–1305. doi:10.1001/jama.2019.1954
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