Observational cohorts are, and will continue to be, indispensable to evaluate clinical strategies for HIV-infected patients. The most clinically relevant strategies are dynamic, that is, strategies that incorporate the patients' time-varying clinical history in the clinical decision. Unfortunately, conventional statistical methods cannot appropriately compare dynamic strategies. Rather methods specifically designed to deal with dynamic strategies and time-varying confounders are needed. We propose to continue to develop analytical methods to enhance the validity of estimates from observational HIV cohorts. We will build upon our previous collaborative efforts to compare the effectiveness of combined antiretroviral therapy (cART) of both static and dynamic strategies. We propose to answer key clinical questions about the management of HIV-infected patients by applying innovative analytic methods to complex observational data. These questions include when to switch cART?, what cART to start? (with an eye towards when to switch? and what to switch to?), and when to monitor immunologic and virologic parameters? for the management of HIV patients. Our primary data source will be the HIV-CAUSAL Collaboration, and we will establish collaborations with other HIV consortia in Europe, the U.S., and Africa. We will generate and maintain user-friendly, publicly-available software and detailed documentation to make these methods available to the HIV research community. Our work will implement and extend cutting-edge approaches to emulate randomized trials using observational data, including methods based on inverse probability of weighting of dynamic marginal structural models and the parametric g-formula. One reason for the lack of widespread use of these methods is their perceived technical complexity, which has made them appear inaccessible to many practitioners. After two decades of methods and software development, our group is now applying innovative methods to large prospective HIV cohorts, and showing that these methods can be added to the standard arsenal of HIV researchers. This proposal will strengthen the international role of the HIV-CAUSAL Collaboration as an incubator for new methodologies in observational HIV research.
We will answer key clinical questions about the management of HIV-infected patients by applying innovative analytic methods to complex observational data. We will develop the methods, implement them, show that they work, and make them available to HIV researchers.
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|Lodi, Sara; Günthard, Huldrych F; Dunn, David et al. (2018) Effect of immediate initiation of antiretroviral treatment on the risk of acquired HIV drug resistance. AIDS 32:327-335|
|Caniglia, Ellen C; Zash, Rebecca; Jacobson, Denise L et al. (2018) Emulating a target trial of antiretroviral therapy regimens started before conception and risk of adverse birth outcomes. AIDS 32:113-120|
|Hernán, Miguel A (2018) How to estimate the effect of treatment duration on survival outcomes using observational data. BMJ 360:k182|
|Caniglia, Ellen C; Cain, Lauren E; Sabin, Caroline A et al. (2017) Comparison of dynamic monitoring strategies based on CD4 cell counts in virally suppressed, HIV-positive individuals on combination antiretroviral therapy in high-income countries: a prospective, observational study. Lancet HIV 4:e251-e259|
|Hernán, Miguel A (2017) Invited Commentary: Selection Bias Without Colliders. Am J Epidemiol 185:1048-1050|
|Lodi, Sara; Costagliola, Dominique; Sabin, Caroline et al. (2017) Effect of Immediate Initiation of Antiretroviral Treatment in HIV-Positive Individuals Aged 50 Years or Older. J Acquir Immune Defic Syndr 76:311-318|
|Mansournia, Mohammad Ali; Hernán, Miguel A (2017) The Authors Respond. Epidemiology 28:e41|
|Swanson, Sonja A; Tiemeier, Henning; Ikram, M Arfan et al. (2017) Nature as a Trialist?: Deconstructing the Analogy Between Mendelian Randomization and Randomized Trials. Epidemiology 28:653-659|
|Mansournia, Mohammad Ali; Higgins, Julian P T; Sterne, Jonathan A C et al. (2017) Biases in Randomized Trials: A Conversation Between Trialists and Epidemiologists. Epidemiology 28:54-59|
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