Analyzing Longitudinally Collected Viral Load Measurements in Youth With Perinatally Acquired HIV Infection: Problems and Possible Remedies. Issue 10 (22nd July 2022)

• Record Type: Journal Article
• Authors: Brummel, Sean S; Van Dyke, Russell B; Patel, Kunjal; Purswani, Murli; Seage, George R; Yao, Tzy-Jyun; Hazra, Rohan; Karalius, Brad; Williams, Paige L
• Is Part Of: American journal of epidemiology. Volume 191:Issue 10(2022)
• Page Start: 1820
• Page End: 1830
• Publication Date: 2022-07-22
• Subjects: bias -- HIV -- imputation -- missing data -- viral load -- viremia copy-years -- youth; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwac125
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Human immunodeficiency virus (HIV) viral load (VL) is an important quantitative marker of disease progression and treatment response in people living with HIV infection, including children with perinatally acquired HIV. Measures of VL are often used to predict different outcomes of interest in this population, such as HIV-associated neurocognitive disorder. One popular approach to summarizing historical viral burden is the area under a time-VL curve (AUC). However, alternative historical VL summaries (HVS) may better answer the research question of interest. In this article, we discuss and contrast the AUC with alternative HVS, including the time-averaged AUC, duration of viremia, percentage of time with suppressed VL, peak VL, and age at peak VL. Using data on youth with perinatally acquired HIV infection from the Pediatric HIV/AIDS Cohort Study Adolescent Master Protocol, we show that HVS and their associations with full-scale intelligence quotient depend on when the VLs were measured. When VL measurements are incomplete, as can be the case in observational studies, analysis results may be subject to selection bias. To alleviate bias, we detail an imputation strategy, and we present a simulation study demonstrating that unbiased estimation of a historical VL summary is possible with a correctly specified imputation model.