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Title: Semi-parametric g-computation to understand the effect of antiretroviral therapy on subsequent weight gain Authors:  Andrew Spieker - Vanderbilt University Medical Center (United States) [presenting]
Abstract: G-computation is a longitudinal generalization of standardization suitable for settings in which there is time-dependent confounding. While highly useful as a tool for estimating longitudinal causal effects, its reliance on parametric models is sometimes criticized. We discuss the utility of cumulative probability models for use in g-computation as a way to relax parametric assumptions. Simulations suggest this approach to be robust and feasible to implement in the real world. We illustrate the utility of this methodology through a study of core and ancillary agents comprising antiretroviral therapies and their effects on weight gain in a large cohort of persons living with HIV. Specifically, we hypothesize that modern integrase strand transfer inhibitors and tenofovir alafenamide are associated with greater mean weight gain as compared to other core and ancillary agents.