Analysis of Repeated Measurements of HIV Viral Load as a Continuous Variable while Accounting for Missing Values

Abstract

Background Recent HIV research predominantly uses Single Measure Frameworks (SMF), focusing solely on the latest viral load data and overlooking missing values. Methods This study explored repeated measures frameworks to assess factors affecting viral load copies while accounting for missing data. The analysis involved 1670 records of HIV patients,using the generalized linear mixed models (GLMM). All variables, except for treatment regimen changes and adherence rating, were recorded at patients’ treatment enrollment. A GLMM was applied to data before and after imputation accounting for the repeated nature of the HIV viral load copies over time. Results The best-fitting model, selected for discussion, was the GLMM fitted to multiply imputed data. Gender and adherence rating did not significantly affect viral load copies. The analysis included other variables such as patient age, marital status, treatment duration, WHO clinical stages, and facility ownership. Results show that viral load copies were higher among currently or formerly married individuals but lower among patients that were accessing treatment from a private facility and spent longer time on treatment. Conclusion and Recomendation The study highlights the significance of recognizing repeated data patterns in longitudinal settings and addressing missing values in health research. It proposes a similar investigation in controlled environments to evaluate SMF and RMF in presence of missing values.