The Effect of a Missing at Random Missing Data Mechanism on a Single Layer Artificial Neural Network with a Sigmoidal Activation Function and the Use of Multiple Imputation as a Correction

Abstract

Missing data is a common problem encountered in statistical analysis. However, little is known about how bias inducing missing at random missing data mechanisms affect predictive model performance measures such as sensitivity, specificity, error rate, ROC curves, and AUC. I investigate the effect of missing at random missing data mechanisms on a single layer artificial neural network with a sigmoidal activation function, equivalent to a binary logistic regression. Binary logistic regression is frequently used in health research and so it is a logical starting point to understand the effects of missing data on statistical learning models that could be used in health research. I then examine whether multiple imputation is a useful analytic correction for improving the predictive model performance measures relative to performing a complete case analysis.