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Edited by Michelle Cristina de Sousa Baltazar
baposter Landscape Poster
This template has been downloaded from:
http://www.LaTeXTemplates.com
Created using the baposter Landscape Poster LaTeX Template created by Brian Amberg
This template has been downloaded from:
http://www.LaTeXTemplates.com
Edited by Michelle Cristina de Sousa Baltazar
This is the LaTeX version of the Overleaf Campus Challenge 2016 poster. If you'd like to translate the poster into your own language, you can start by creating a project from this template!
To translate this poster, change the language
option for both the babel and translator packages.
Then add your own OverleafPoster-<lang>.dict file
and provide your translations, based on the
default OverleafPoster-English.dict.
Information before unblinding regarding the success of confirmatory clinical trials is highly uncertain. Estimates of expected future power which purport to use this information for purposes of sample size adjustment after given interim points need to reflect this uncertainty. Estimates of future power at later interim points need to track the evolution of the clinical trial. We employ sequential models to describe this evolution. We show that current techniques using point estimates of auxiliary parameters for estimating expected power: (i) fail to describe the range of likely power obtained after the anticipated data are observed, (ii) fail to adjust to different kinds of thresholds, and (iii) fail to adjust to the changing patient population. Our algorithms address each of these shortcomings. We show that the uncertainty arising from clinical trials is characterized by filtering later auxiliary parameters through their earlier counterparts and employing the resulting posterior distribution to estimate power. We devise MCMC-based algorithms to implement sample size adjustments after the first interim point. Bayesian models are designed to implement these adjustments in settings where both hard and soft thresholds for distinguishing the presence of treatment effects are present. Sequential MCMC-based algorithms are devised to implement accurate sample size adjustments for multiple interim points. We apply these suggested algorithms to a depression trial for purposes of illustration.
This poster theme takes queues from NUST branding and using beamer as LaTeX template. The name namkeen is inspired from the famous Peshawari recipe of Mutton in the Northern Region of Pakistan. Namkeen gosht recipe is famous for its simplicity yet deliciousness.
Source https://github.com/hasanalikhattak/namkeen