Sample Size Bayesian Estimation for Logistic Regression

The problem of sample size estimation is important in the medical applications, especially in the cases of expensive measurements of immune biomarkers. The papers describes the problem of logistic regression analysis including model feature selection and includes the sample size determination algorithms, namely methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regression model parameters as a multivariate variable, propose to estimate the sample size using the distance between parameter distribution functions on cross-validated data sets.