Validation of Malaria Predictive Model with Missing Covariates

Abstract

This study develops a statistical methodology for validating a predictive clinical malaria model when data has missing values in predictor variables. Using the logistic regression framework, multiple imputation techniques for missing values and a penalized likelihood approach to avoid overfitting of the data were adopted. Models with different functional forms were built using known predictors (age, sickle cell, blood group, parasite density, and mosquito bed net use) and some malaria antibody specific antigens and FCGR3B polymorphism. Models were assessed through visualization and differences between the Area Under the Receiver Operating Characteristic Curve (AUROC) and Brier Score (BS) estimated by suitable internal cross-validation designs. The contributions of this research are in three folds: (i) addresses the statistical question on how to build and validate a risk prediction model in the presence of missing explanatory variables (ii) improves the general statistical approach for malaria epidemiology (iii) identifies potential malaria antibodies and FCGR3B polymorphism which should be the research focus in the search for potential malaria vaccine candidate