Imputation of Missing Data in Chronic Hepatitis C Patient Utility Data

Abstract

A longitudinal study was conducted to measure the impact of treatment with direct-acting antiviral agents on the Health-Related Quality of Life (HR-QoL) among patients diagnosed with Chronic Hepatitis C. EQ-5D measurements were recorded before treatment, mid-treatment and at two timepoints following treatment. This thesis provides recommendations for dealing with missing EQ-5D measurements in the data and proposes a strategy for selecting an imputation method for item non-response and unit non-response missingness. A simulation study is conducted on a nearly complete subset of the data to compare the performance of several imputation methods based on prediction accuracy. Results show that fully conditional specification (FCS) with predictive mean matching and FCS with a linear mixed effects model (FCS-LMM) were the most suitable imputation methods for item non-response and unit non-response missingness, respectively. The FCS-LMM method was selected to impute the missing values in the original longitudinal dataset.