AIDS and Behavior 2023;27:2915-2931. [doi: 10.1007/s10461-023-04015-1]
Liu, Jingxin | Pan, Yue | Nelson, Mindy C. | Gooden, Lauren K. | Metsch, Lisa R. | Rodriguez, Allan E. | Tross, Susan | del Rio, Carlos | Mandler, Raul N. | Feaster, Daniel J.
The HIV/AIDS epidemic remains a major public health concern since the 1980s; untreated HIV infection has numerous consequences on quality of life. To optimize patients’ health outcomes and to reduce HIV transmission, this study, using data from CTN-0049 and CTN-0064, focused on vulnerable populations of people living with HIV (PLWH) and compared different predictive strategies for viral suppression using longitudinal or repeated measures.
The four methods of predicting viral suppression are (1) including the repeated measures of each feature as predictors, (2) utilizing only the initial (baseline) value of the feature as predictor, (3) using the last observed value as the predictors and (4) using a growth curve estimated from the features to create individual-specific prediction of growth curves as features. These models were compared using Synthetic Random Forests (SRF).
The SRF models predicted HIV viral suppression in CTN-0064 with an accuracy rate as high as 70%. The person-specific trajectories (Model 4) had the best predictive performance of the approaches. Not surprisingly, among the other models, those with characteristics from closer time-points produced better model fit than using baseline aspects only.
Conclusions: The model with person-specific trajectories had the best predictive power as compared to other models. The findings from this study are valuable, since they provide evidence that incorporating not just levels of predictors but also their change over time improves predictive performance of our models. Using person-specific intercepts and slopes provides a novel and useful approach to creating predictive models using repeated measurements. It also suggests the possibility of incorporating these types of modeling efforts into ongoing clinical monitoring using medical records.
Keywords: CTN platform/ancillary study | HIV/AIDS | NIDA Data Share | Statistical models | AIDS and Behavior (journal)
Document No: 1554 ; PMID: 36739589
Submitted by: CTN Dissemination Librarian (2/13/2023)
