Minimum sample size for developing a multivariable prediction model using multinomial logistic regression.

ABSTRACT:

Aims

Multinomial logistic regression models allow one to predict the risk of a categorical outcome with > 2 categories. When developing such a model, researchers should ensure the number of participants (n) is appropriate relative to the number of events (Ek) and the number of predictor parameters (pk) for each category k. We propose three criteria to determine the minimum n required in light of existing criteria developed for binary outcomes.

Proposed criteria

The first criterion aims to minimise the model overfitting. The second aims to minimise the difference between the observed and adjusted R2 Nagelkerke. The third criterion aims to ensure the overall risk is estimated precisely. For criterion (i), we show the sample size must be based on the anticipated C

SUBMITTER: Pate A 

PROVIDER: S-EPMC10012398 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature