Literature DB >> 9147587

The behavior of the P-value when the alternative hypothesis is true.

H M Hung1, R T O'Neill, P Bauer, K Köhne.   

Abstract

The P-value is a random variable derived from the distribution of the test statistic used to analyze a data set and to test a null hypothesis. Under the null hypothesis, the P-value based on a continuous test statistic has a uniform distribution over the interval [0, 1], regardless of the sample size of the experiment. In contrast, the distribution of the P-value under the alternative hypothesis is a function of both sample size and the true value or range of true values of the tested parameter. The characteristics, such as mean and percentiles, of the P-value distribution can give valuable insight into how the P-value behaves for a variety of parameter values and sample sizes. Potential applications of the P-value distribution under the alternative hypothesis to the design, analysis, and interpretation of results of clinical trials are considered.

Mesh:

Year:  1997        PMID: 9147587

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  19 in total

1.  Practical guidelines for assessing power and false discovery rate for a fixed sample size in microarray experiments.

Authors:  Tiejun Tong; Hongyu Zhao
Journal:  Stat Med       Date:  2008-05-20       Impact factor: 2.373

2.  Is the tail-strength measure more powerful in tests of genetic association?

Authors:  Shizhong Han; Li Ma; Dawei Li; Bao-Zhu Yang
Journal:  Am J Hum Genet       Date:  2009-02       Impact factor: 11.025

3.  Bias and variance reduction in estimating the proportion of true-null hypotheses.

Authors:  Yebin Cheng; Dexiang Gao; Tiejun Tong
Journal:  Biostatistics       Date:  2014-06-23       Impact factor: 5.899

4.  A parametric meta-analysis.

Authors:  Chang Yu; Daniel Zelterman
Journal:  Stat Med       Date:  2019-06-17       Impact factor: 2.373

5.  Illustrations on Using the Distribution of a P-value in High Dimensional Data Analyses.

Authors:  Xiaojun Hu; Gary L Gadbury; Qinfang Xiang; David B Allison
Journal:  Adv Appl Stat Sci       Date:  2010-02

6.  CALCULATING AVERAGE POWER FOR THE BENJAMINI-HOCHBERG PROCEDURE.

Authors:  William J Feser; Tasha E Fingerlin; Matthew J Strand; Deborah H Glueck
Journal:  J Stat Theory Appl       Date:  2009

7.  P-Value Precision and Reproducibility.

Authors:  Dennis D Boos; Leonard A Stefanski
Journal:  Am Stat       Date:  2012-01-24       Impact factor: 8.710

8.  Bias-corrected estimators for proportion of true null hypotheses: application of adaptive FDR-controlling in segmented failure data.

Authors:  Aniket Biswas; Gaurangadeb Chattopadhyay; Aditya Chatterjee
Journal:  J Appl Stat       Date:  2021-07-27       Impact factor: 1.416

9.  SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery.

Authors:  Jie Wang; Min Wu; Xuhui Huang; Li Wang; Sophia Zhang; Hui Liu; Jie Zheng
Journal:  Database (Oxford)       Date:  2022-05-13       Impact factor: 4.462

10.  NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks.

Authors:  Matthew A Reyna; Uthsav Chitra; Rebecca Elyanow; Benjamin J Raphael
Journal:  J Comput Biol       Date:  2021-01-05       Impact factor: 1.479

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.