Sample size considerations and predictive performance of multinomial logistic prediction models.

ABSTRACT: Multinomial Logistic Regression (MLR) has been advocated for developing clinical prediction models that distinguish between three or more unordered outcomes. We present a full-factorial simulation study to examine the predictive performance of MLR models in relation to the relative size of outcome categories, number of predictors and the number of events per variable. It is shown that MLR estimated by Maximum Likelihood yields overfitted prediction models in small to medium sized data. In most cases, the calibration and overall predictive performance of the multinomial prediction model is improved by using penalized MLR. Our simulation study also highlights the importance of events per variable in the multinomial context as well as the total sample size. As expected, our study demonstrates

SUBMITTER: de Jong VMT 

PROVIDER: S-EPMC6590172 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature