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pro vyhledávání: '"HUDGENS, Michael G."'
Inferring treatment effects on a survival time outcome based on data from an observational study is challenging due to the presence of censoring and possible confounding. An additional challenge occurs when a unit's treatment affects the outcome of o
Externí odkaz:
http://arxiv.org/abs/2409.13190
Confounding and exposure measurement error can introduce bias when drawing inference about the marginal effect of an exposure on an outcome of interest. While there are broad methodologies for addressing each source of bias individually, confounding
Externí odkaz:
http://arxiv.org/abs/2407.09443
Observational data are often used to estimate real-world effectiveness and durability of coronavirus disease 2019 (COVID-19) vaccines. A sequence of nested trials can be emulated to draw inference from such data while minimizing selection bias, immor
Externí odkaz:
http://arxiv.org/abs/2403.18115
Patient care may be improved by recommending treatments based on patient characteristics when there is treatment effect heterogeneity. Recently, there has been a great deal of attention focused on the estimation of optimal treatment rules that maximi
Externí odkaz:
http://arxiv.org/abs/2401.03084
Autor:
Shook-Sa, Bonnie E., Zivich, Paul N., Rosin, Samuel P., Edwards, Jessie K., Adimora, Adaora A., Hudgens, Michael G., Cole, Stephen R.
While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from trial data. RCTs are limited to a small number of comparator arms and of
Externí odkaz:
http://arxiv.org/abs/2305.00845
Autor:
Lotspeich, Sarah C., Richardson, Brian D., Baldoni, Pedro L., Enders, Kimberly P., Hudgens, Michael G.
People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but "latent" HIV still persists in viral reservoirs. Eliminating these reservoirs is the goal of HIV cure research. The quantitative viral outgr
Externí odkaz:
http://arxiv.org/abs/2302.00516
Interference occurs when a unit's treatment (or exposure) affects another unit's outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the
Externí odkaz:
http://arxiv.org/abs/2212.10959
Autor:
Zivich, Paul N, Cole, Stephen R, Edwards, Jessie K, Shook-Sa, Bonnie E, Breskin, Alexander, Hudgens, Michael G
Comparisons of treatments, interventions, or exposures are of central interest in epidemiology, but direct comparisons are not always possible due to practical or ethical reasons. Here, we detail a fusion approach to compare treatments across studies
Externí odkaz:
http://arxiv.org/abs/2206.04445
Autor:
Shook-Sa, Bonnie E., Hudgens, Michael G., Knittel, Andrea K., Edmonds, Andrew, Ramirez, Catalina, Cole, Stephen R., Cohen, Mardge, Adedimeji, Adebola, Taylor, Tonya, Michel, Katherine G., Kovacs, Andrea, Cohen, Jennifer, Donohue, Jessica, Foster, Antonina, Fischl, Margaret A., Long, Dustin, Adimora, Adaora A.
Causal inference methods can be applied to estimate the effect of a point exposure or treatment on an outcome of interest using data from observational studies. For example, in the Women's Interagency HIV Study, it is of interest to understand the ef
Externí odkaz:
http://arxiv.org/abs/2202.01650
Autor:
Berkowitz, Seth A., Ochoa, Aileen, Donovan, Jenna M., Dankovchik, Jenine, LaPoint, Myklynn, Kuhn, Marlena L., Morrissey, Suzanne, Gao, Mufeng, Hudgens, Michael G., Basu, Sanjay, Gold, Rachel
Publikováno v:
In SSM - Population Health September 2024 27