88,026 research outputs found
Sequential monitoring of response-adaptive randomized clinical trials
Clinical trials are complex and usually involve multiple objectives such as
controlling type I error rate, increasing power to detect treatment difference,
assigning more patients to better treatment, and more. In literature, both
response-adaptive randomization (RAR) procedures (by changing randomization
procedure sequentially) and sequential monitoring (by changing analysis
procedure sequentially) have been proposed to achieve these objectives to some
degree. In this paper, we propose to sequentially monitor response-adaptive
randomized clinical trial and study it's properties. We prove that the
sequential test statistics of the new procedure converge to a Brownian motion
in distribution. Further, we show that the sequential test statistics
asymptotically satisfy the canonical joint distribution defined in Jennison and
Turnbull (\citeyearJT00). Therefore, type I error and other objectives can be
achieved theoretically by selecting appropriate boundaries. These results open
a door to sequentially monitor response-adaptive randomized clinical trials in
practice. We can also observe from the simulation studies that, the proposed
procedure brings together the advantages of both techniques, in dealing with
power, total sample size and total failure numbers, while keeps the type I
error. In addition, we illustrate the characteristics of the proposed procedure
by redesigning a well-known clinical trial of maternal-infant HIV transmission.Comment: Published in at http://dx.doi.org/10.1214/10-AOS796 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
LCSTS: A Large Scale Chinese Short Text Summarization Dataset
Automatic text summarization is widely regarded as the highly difficult
problem, partially because of the lack of large text summarization data set.
Due to the great challenge of constructing the large scale summaries for full
text, in this paper, we introduce a large corpus of Chinese short text
summarization dataset constructed from the Chinese microblogging website Sina
Weibo, which is released to the public
{http://icrc.hitsz.edu.cn/Article/show/139.html}. This corpus consists of over
2 million real Chinese short texts with short summaries given by the author of
each text. We also manually tagged the relevance of 10,666 short summaries with
their corresponding short texts. Based on the corpus, we introduce recurrent
neural network for the summary generation and achieve promising results, which
not only shows the usefulness of the proposed corpus for short text
summarization research, but also provides a baseline for further research on
this topic.Comment: Recently, we received feedbacks from Yuya Taguchi from NAIST in Japan
and Qian Chen from USTC of China, that the results in the EMNLP2015 version
seem to be underrated. So we carefully checked our results and find out that
we made a mistake while using the standard ROUGE. Then we re-evaluate all
methods in the paper and get corrected results listed in Table 2 of this
versio
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