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A theory for testing hypotheses under covariate-adaptive randomization
The covariate-adaptive randomization method was proposed for clinical trials long ago but little theoretical work has been done for statistical inference associated with it. Practitioners often apply test procedures available for simple randomization, which is controversial since procedures valid un...
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| Published in: | Biometrika 2010-06, Vol.97 (2), p.347-360 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c421t-a5e3824fb72b5257861d6339a43db16bc0aa6bc3901fea717929478d857b38773 |
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| cites | cdi_FETCH-LOGICAL-c421t-a5e3824fb72b5257861d6339a43db16bc0aa6bc3901fea717929478d857b38773 |
| container_end_page | 360 |
| container_issue | 2 |
| container_start_page | 347 |
| container_title | Biometrika |
| container_volume | 97 |
| creator | Shao, Jun Yu, Xinxin Zhong, Bob |
| description | The covariate-adaptive randomization method was proposed for clinical trials long ago but little theoretical work has been done for statistical inference associated with it. Practitioners often apply test procedures available for simple randomization, which is controversial since procedures valid under simple randomization may not be valid under other randomization schemes. In this paper, we provide some theoretical results for testing hypotheses after covariate-adaptive randomization. We show that one way to obtain a valid test procedure is to use a correct model between outcomes and covariates, including those used in randomization. We also show that the simple two sample t-test, without using any covariate, is conservative under covariate-adaptive biased coin randomization in terms of its Type I error, and that a valid bootstrap t-test can be constructed. The powers of several tests are examined theoretically and empirically. Our study provides guidance for applications and sheds light on further research in this area. |
| doi_str_mv | 10.1093/biomet/asq014 |
| format | article |
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Practitioners often apply test procedures available for simple randomization, which is controversial since procedures valid under simple randomization may not be valid under other randomization schemes. In this paper, we provide some theoretical results for testing hypotheses after covariate-adaptive randomization. We show that one way to obtain a valid test procedure is to use a correct model between outcomes and covariates, including those used in randomization. We also show that the simple two sample t-test, without using any covariate, is conservative under covariate-adaptive biased coin randomization in terms of its Type I error, and that a valid bootstrap t-test can be constructed. The powers of several tests are examined theoretically and empirically. 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Yu, Xinxin ; Zhong, Bob</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c421t-a5e3824fb72b5257861d6339a43db16bc0aa6bc3901fea717929478d857b38773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Adaptive allocation</topic><topic>Applications</topic><topic>Biased coin</topic><topic>Biology, psychology, social sciences</topic><topic>Bootstrap method</topic><topic>Clinical trial</topic><topic>Clinical trials</topic><topic>Conditional probabilities</topic><topic>Covariance</topic><topic>Estimation bias</topic><topic>Estimators</topic><topic>Exact sciences and technology</topic><topic>General topics</topic><topic>Hypotheses</topic><topic>Hypothesis testing</topic><topic>Inference</topic><topic>Mathematics</topic><topic>Minimization</topic><topic>Modeling</topic><topic>Nonparametric inference</topic><topic>Parametric inference</topic><topic>Power</topic><topic>Probability and statistics</topic><topic>Random allocation</topic><topic>Random variables</topic><topic>Sampling bias</topic><topic>Sciences and techniques of general use</topic><topic>Significance level</topic><topic>Statistical inference</topic><topic>Statistical variance</topic><topic>Statistics</topic><topic>Studies</topic><topic>Type I error</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shao, Jun</creatorcontrib><creatorcontrib>Yu, Xinxin</creatorcontrib><creatorcontrib>Zhong, Bob</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Biometrika</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shao, Jun</au><au>Yu, Xinxin</au><au>Zhong, Bob</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A theory for testing hypotheses under covariate-adaptive randomization</atitle><jtitle>Biometrika</jtitle><date>2010-06-01</date><risdate>2010</risdate><volume>97</volume><issue>2</issue><spage>347</spage><epage>360</epage><pages>347-360</pages><issn>0006-3444</issn><eissn>1464-3510</eissn><coden>BIOKAX</coden><notes>istex:3285AD5C28A36C33B3B54FA25BFFC0033F446F3A</notes><notes>ArticleID:asq014</notes><notes>ark:/67375/HXZ-N73ZKK7P-1</notes><abstract>The covariate-adaptive randomization method was proposed for clinical trials long ago but little theoretical work has been done for statistical inference associated with it. 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| ispartof | Biometrika, 2010-06, Vol.97 (2), p.347-360 |
| issn | 0006-3444 1464-3510 |
| language | eng |
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| source | Oxford University Press Journals; JSTOR Archival Journals and Primary Sources Collection |
| subjects | Adaptive allocation Applications Biased coin Biology, psychology, social sciences Bootstrap method Clinical trial Clinical trials Conditional probabilities Covariance Estimation bias Estimators Exact sciences and technology General topics Hypotheses Hypothesis testing Inference Mathematics Minimization Modeling Nonparametric inference Parametric inference Power Probability and statistics Random allocation Random variables Sampling bias Sciences and techniques of general use Significance level Statistical inference Statistical variance Statistics Studies Type I error |
| title | A theory for testing hypotheses under covariate-adaptive randomization |
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