Statistical Joint Modeling for Predicting the Association of CD4 Measurement and Time to Death of People Living with HIV Who Enrolled in ART, Southwest Ethiopia.

Abstract

BackgroundIn much epidemiological HIV research, patients are often followed over a period of time to predict their survival on the basis of repeatedly measured CD4 status. To predict survival, statistical models of the association between mortality and longitudinal CD4 measurement have been conducted widely using time-varying Cox models. However, in the presence of repeated measure, this approach leads to biased estimates. In view of the limitation of time-varying Cox models, in the present study, we considered joint modeling to predict the association of longitudinal CD4 measurement and time to death among patients initiated on ART.MethodsA retrospective cohort study was employed for five years from 2009 to 2014 on a randomly selected 358 samples. Data were collected from patients’ ART and pre-ART follow-up registration book, database and other clinical records. Data were analyzed using joint latent class modeling of repeated CD4 measurement and time-to-event (HIV death).ResultsWe have studied a total of 358 HIV-positive patients. The median and interquartile ranges of the age of patients were 30.31 years and 13.82, respectively. Males constitute the larger proportion, 51.68%. The square root of CD4 count has declined on average over time. This has been indicated with the negative sign of the coefficient for the time effect. The deterioration of health of individuals is severe in class 1, it has been observed with a worse decline in CD4 cell counts over time in this class than other classes (β= −0.488). Women had a larger risk rate than men (β=−2.475, p-value=0.013). Besides, the CD4 counts measurement of patients has been revealed to decrease as age increases (β= −0.016, p=0.008).ConclusionThe finding indicated that the square root CD4 cell measurement dropped over time in the three classes. This clearly suggested deterioration in the health of individuals. Women were found to have a higher hazard rate than men.