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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.

Lecturas de la clase de Elemento Finito impartida en la Carrera de Matemática de la Universidad Nacional Autónoma de Honduras

This article aims to be a model LaTeX document while teaching you the basics of what it is and how to use it. It contains all of the basic constructs you are likely to encounter as you write your first papers and articles. This article will not go into detail about how to get started with a local installation of LaTeX.

This is a template for junior tests at St Paul's School, London.

Template de Trabalhos do PEE

Этот шаблон документа разработан в 2014 году Данилом Фёдоровых (danil@fedorovykh.ru) для использования в курсе «Документы и презентации в \LaTeX», записанном НИУ ВШЭ для Coursera.org: http://coursera.org/course/latex. Исходная версия шаблона — https://www.writelatex.com/coursera/latex/4.1

This report gives an overview of various Machine Learning algorithms and compare their efficiencies. Also, it gives an insight into various approaches which can be adopted to handle missing data values and confers the impact of discretisation on various machine learning techiques.

Этот шаблон документа разработан в 2014 году Данилом Фёдоровых (danil@fedorovykh.ru) для использования в курсе «Документы и презентации в \LaTeX», записанном НИУ ВШЭ для Coursera.org: http://coursera.org/course/latex.

Rapportmall i LaTeX - första utkastet Av Magnus Håkansson, et08mh1@student.lth.se Laborationshandledare, FAFA01, labb: Svängningar 2 april 2011 Överförd till writeLaTeX av Magnus Dagbro, elt12mda@student.lu.se Laborationshandledare, FAFA01, labb: Svängningar 12 maj 2014