Stopping rules for phase II studies

Stopping Rules for Turbo Decoders

Decoders for turbo codes are iterative in nature, i.e., they have to perform a certain number of iterations before reaching a satisfactory degree of confidence regarding a frame to be decoded. Until now standard turbo decoders have used a fixed number of iterations. In this article, we propose some simple “stopping rules” that can be used to reduce the average number of iterations. This techniq...

Efficient Stopping Rules

The importance of sampling methods in machine learning is growing due to an ever-increasing number of datasets containing millions of records of biological, medical, or other types of data. Such datasets are often beyond the reach of many standard machine learning techniques because of high computational or space complexity of the algorithms. When making even a single pass through the data is p...

Stopping rules employing response rates, time to progression, and early progressive disease for phase II oncology trials

BACKGROUND Response rate (RR), the most common early means of assessing oncology drugs, is not suitable as the sole endpoint for phase II trials of drugs which induce disease stability but not regression. Time to progression (TTP) may be more sensitive to such agents, but induces recruitment delays in multistage studies. Early progressive disease (EPD) is the earliest signal of time to progress...

Cluster Stopping Rules For Word Sense Discrimination

As text data becomes plentiful, unsupervised methods for Word Sense Disambiguation (WSD) become more viable. A problem encountered in applying WSD methods is finding the exact number of senses an ambiguity has in a training corpus collected in an automated manner. That number is not known a priori; rather it needs to be determined based on the data itself. We address that problem using cluster ...

Optimal Stopping Rules for Directionally Reinforced Processes