Estimation of Disease Prevalence in the Presence of Missing Data

Abstract

Missing data are commonly encountered in most medical research. Unfortunately, they are often neglected or not properly handled during analytic procedures, and this may substantially bias the results of the study, reduce the study power, and lead to invalid conclusions. In this study, we introduce key concepts regarding missing data in survey data analysis, provide a conceptual framework on how to approach missing data in this setting, describe typical mechanisms of missing data, and use a theoretical model for handling such data. We consider a case where the variable of interest (response variable) is binary and some of the observations are missing and assume that all the covariates are fully observed. In most cases, the statistic of interest, when faced with binary data is the prevalence. We develop a two stage approach to improve the prevalence estimates: in the rst stage, we use a logistic regression model to predict the missing binary observations and then in the second stage we recalculate the prevalence using the observed binary data and the imputed missing data. Finally we study the asymptotic properties of the prevalence estimator. Such a model would be of great interest in research studies involving HIV in which people usually refuse to donate blood for testing yet they are willing to provide other covariates. The prevalence estimation method is illustrated using simulated data and applied to HIV/AIDS data from the Kenya AIDS Indicator Survey, 2007.