國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

An adaptive permutation test for det...

Huffman, Kristopher Michael.

An adaptive permutation test for detecting treatment effect in multicenter clinical trials.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An adaptive permutation test for detecting treatment effect in multicenter clinical trials.
作者:	Huffman, Kristopher Michael.
面頁冊數:	149 p.
附註:	Source: Masters Abstracts International, Volume: 50-02, page: 1092.
附註:	Adviser: Thomas W. O'Gorman.
Contained By:	Masters Abstracts International50-02.
標題:	Biology, Biostatistics.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1498774
ISBN:	9781124871073

An adaptive permutation test for detecting treatment effect in multicenter clinical trials.
Huffman, Kristopher Michael.

An adaptive permutation test for detecting treatment effect in multicenter clinical trials. - 149 p.

Source: Masters Abstracts International, Volume: 50-02, page: 1092.

Thesis (M.S.)--Northern Illinois University, 2011.

An adaptive permutation test is developed for detecting treatment effect in the context of balanced, complete multicenter clinical trials. The proposed adaptive test computes a statistic for each center that measures the tail length of the response data in a center. Based on this statistic, the adaptive test will either winsorize the response data in a center or leave it unchanged. The traditional F test for performing a test of significance on a subset of regression coefficients in a linear model is then performed on the winsorized data, and a permutation method is used to compute the p-value of the adaptive test. Simulation studies are employed to compare the size and power of the adaptive test and the traditional F test. The results indicate that the adaptive test maintains an actual significance level close to its nominal significance level. Moreover, the adaptive test is seen to have significantly greater power than the traditional F test for long-tailed and highly skewed error distributions, with only slight loss in power for normal and short to moderate-tailed error distributions.

ISBN: 9781124871073Subjects--Topical Terms:

227395
Biology, Biostatistics.

An adaptive permutation test for detecting treatment effect in multicenter clinical trials.
LDR:02153nmm 2200313 4500 001 380713
005 20130530092743.5
008 130708s2011 ||||||||||||||||| ||eng d
020 $a 9781124871073
035 $a (UMI)AAI1498774
035 $a AAI1498774
040 $a UMI $c UMI
100 1 $a Huffman, Kristopher Michael. $3 603349
245 1 3 $a An adaptive permutation test for detecting treatment effect in multicenter clinical trials.
300 $a 149 p.
500 $a Source: Masters Abstracts International, Volume: 50-02, page: 1092.
500 $a Adviser: Thomas W. O'Gorman.
502 $a Thesis (M.S.)--Northern Illinois University, 2011.
520 $a An adaptive permutation test is developed for detecting treatment effect in the context of balanced, complete multicenter clinical trials. The proposed adaptive test computes a statistic for each center that measures the tail length of the response data in a center. Based on this statistic, the adaptive test will either winsorize the response data in a center or leave it unchanged. The traditional F test for performing a test of significance on a subset of regression coefficients in a linear model is then performed on the winsorized data, and a permutation method is used to compute the p-value of the adaptive test. Simulation studies are employed to compare the size and power of the adaptive test and the traditional F test. The results indicate that the adaptive test maintains an actual significance level close to its nominal significance level. Moreover, the adaptive test is seen to have significantly greater power than the traditional F test for long-tailed and highly skewed error distributions, with only slight loss in power for normal and short to moderate-tailed error distributions.
590 $a School code: 0162.
650 4 $a Biology, Biostatistics. $3 227395
650 4 $a Statistics. $3 182057
690 $a 0308
690 $a 0463
710 2 $a Northern Illinois University. $b Statistics. $3 603350
773 0 $t Masters Abstracts International $g 50-02.
790 1 0 $a O'Gorman, Thomas W., $e advisor
790 1 0 $a O'Gorman, Thomas W. $e committee member
790 1 0 $a Polansky, Alan $e committee member
790 1 0 $a Hosmane, Balakrishna $e committee member
790 $a 0162
791 $a M.S.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1498774