|P.Mean: Abstracts for a possible upcoming talk (created 2010-01-20).|
I might be asked to give a talk in February and I wanted to offer two possible choices. Here are the titles and abstracts of those talks.
A Bayesian Approach for Modeling Accrual During a Clinical Trial. Abstract: The most common reason why clinical trials fail is that they fall well below their goals for patient accrual. Accrual rates are often developed in an ad hoc fashion. If they are monitored at all, accrual rates are examined using a purely subjective approach. There are not any good quantitative tools for planning and monitoring accrual rates. With good planning tools, researchers would be able to construct realistic targets for their sample sizes rather than promising a sample size that could not be delivered in a reasonable time frame and within a limited research budget. With good monitoring tools, researchers would get an early warning when accrual rates are suffering. This would allow them to take appropriate corrective action before too much harm was done. In this talk, I will outline a simple Bayesian model for accrual using exponential waiting times between successive patients. I will also discuss extensions to multicenter trials and to trials that account for exclusion and refusal rates. A copy of a poster presentation of this talk is available at http://www.pmean.com/10/images/JSM2007.pdf.
The surprisal matrix and its role in identifying anomalous observations. Abstract: The surprisal, defined as the negative of the base 2 logarithm of a probability, is a fundamental component used in the calculation of entropy. In this talk, I will define a surprisal matrix for a data set consisting of multiple discrete variables, possibly with different supports. The surprisal matrix is useful in identifying areas of high heterogeneity in such a data set, which often corresponds to interesting and unusual patterns among the observations or among the variables. I will illustrate two applications of the surprisal matrix: monitoring data quality in a large stream of fixed format text data, and examining consensus in the evaluation of sperm morphology. A webpage outlining some of the ideas in this talk is available at http://www.pmean.com/09/SurprisalMatrix.html.
This work is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2017-06-15. Need more information? I have a page with general help resources. You can also browse for pages similar to this one at Category: Professional details.