Posters are a great way to showcase your work, whether at conferences, class presentations, or university open days. Formatting a poster correctly can be difficult but these templates and examples make it easy to create beautiful, eye-catching posters with key content clearly laid out. Each template provides placeholders for text, tables, figures and equations. Font size is usually set automatically, and it’s easy to switch between landscape or portrait, A0, A1, A2, A3 and A4 size posters.
A fork of Gemini beamer poster theme with UChicago color scheme v1.1.0 released September 8, 2022. Includes UChicago logo and shield. Also includes an unofficial logo for the CS department.
See https://github.com/anishathalye/gemini for the original.
Please file any issues at https://github.com/k4rtik/uchicago-poster.
This is a template of conference poster by using beamerposter package. This package enables the user to use beamer style operations on a canvas of the sizes provided by a0poster; font scaling is available.
This template use multicols environment to provide multicolumn layout with text flow between columns.
Edit beamerthemesharelatex.sty file to change colors and font sizes of various poster elements.
See also: http://www.ctan.org/pkg/beamerposter.
This template was originally published on ShareLaTeX and subsequently moved to Overleaf in December 2019.
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.