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Presentation 1
Time: 11am – 11:30am
Speaker: Sherry Gao, UBC Statistics MSc student
Title: Nonlinear mixed-effects models for HIV viral load trajectories before and after antiretroviral therapy interruption, incorporating left censoring
Abstract: In an HIV study, the viral decay during an anti-HIV treatment and the viral rebound after the treatment is interrupted can be viewed as two longitudinal processes, and they may be related to each other. Our goal is to investigate if key features of HIV viral decay and CD4 trajectories during antiretroviral therapy (ART) are associated with characteristics of HIV viral rebound following ART interruption. Nonlinear mixed-effects (NLME) models are used to model viral load trajectories before and following ART interruption, incorporating left censoring due to lower detection limits of viral load assays. A stochastic approximation EM (SAEM) algorithm is used for parameter estimation and inference. To circumvent the computational intensity associated with maximizing the joint likelihood, we propose an easy-to-implement three-step method. We evaluate the performance of this method through simulation studies and apply it to data from the Zurich Primary HIV Infection Study. We find that some key features of viral load and CD4 trajectories during ART (e.g., viral decay rate) are significantly associated with important characteristics of viral rebound following ART interruption (e.g., viral set point).
Presentation 2
Time: 11:30am – 12pm
Speaker: Ian Murphy, UBC Statistics MSc student
Title: Modelling dive phase definitions for Northern Resident Killer Whales
Abstract: Northern Resident Killer Whales (NRKWs), in contrast with the endangered Southern Resident Killer Whales (SRKWs), have been thriving in their habitats. A key component to understanding whale survival is to identify prey capture events, but they are difficult to directly observe. Instead, kinematic variables during the bottom phase of a dive are used to predict prey captures. However, universal definitions of the bottom phase have not been established, despite the fact that modifying the bottom phase greatly impacts existing methods to predict prey capture events. To investigate bottom phase variability, we asked several whale researchers to identify the bottom phase of various dives. The diving data used were collected from 3 NRKWs by a UBC whale researcher. Linear mixed-effects models show that there exists substantial variation in bottom phase definitions across different researchers and across different dive types. We then propose several statistical models for the bottom phase of a dive, including functional linear regression models. Identification of the bottom phase using these models improves the prediction of prey capture dives compared to the currently used bottom phase definitions. Finally, we formulate methods to determine an adequate sample size for fitting these statistical models, and then apply the methods to the data.