Minimum Sample Size for Developing a Multivariable Prediction Model using Multinomial Logistic Regression

Alexander Pate, Richard D. Riley, Gary S. Collins, Maarten Van Smeden, Ben Van Calster, Joie Ensor, Glen Martin

Research output: Contribution to journal β€Ί Article β€Ί peer-review


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 (πΈπ‘˜) and the number of predictor parameters (π‘π‘˜) for each category π‘˜. We propose three criteria to determine the minimum 𝑛 required in light of existing criteria developed for binary outcomes. The first criteria 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 Cox-snell 𝑅2 of distinct β€œone-to-one” logistic regression models corresponding to the sub-models of the multinomial logistic regression, rather than on the overall Cox-snell 𝑅2 of the multinomial logistic regression. We tested the performance of the proposed criteria (i) through a simulation study, and found that it resulted in the desired level of overfitting. Criterion (ii) and (iii) are natural extensions from previously proposed criteria for binary outcomes. We illustrate how to implement the sample size criteria through a worked example considering the development of a multinomial risk prediction model for tumour type when presented with an ovarian mass. Code is provided for the simulation and worked example. We will embed our proposed criteria within the pmsampsize R library and Stata modules.
Original languageEnglish
JournalStatistical Methods in Medical Research
Publication statusPublished - 2022


Dive into the research topics of 'Minimum Sample Size for Developing a Multivariable Prediction Model using Multinomial Logistic Regression'. Together they form a unique fingerprint.

Cite this