An entropy-based nonparametric test for the validation of surrogate endpoints
We present a nonparametric test to validate surrogate endpoints based on measure of divergence and random permutation. This test is a proposal to directly verify the Prentice statistical definition of surrogacy. The test does not impose distributional assumptions on the endpoints, and it is robust t...
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| Veröffentlicht in: | Statistics in medicine 2012-06, Vol.31 (14), p.1517-1530 |
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| container_title | Statistics in medicine |
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| creator | Miao, Xiaopeng Wang, Yong-Cheng Gangopadhyay, Ashis |
| description | We present a nonparametric test to validate surrogate endpoints based on measure of divergence and random permutation. This test is a proposal to directly verify the Prentice statistical definition of surrogacy. The test does not impose distributional assumptions on the endpoints, and it is robust to model misspecification. Our simulation study shows that the proposed nonparametric test outperforms the practical test of the Prentice criterion in terms of both robustness of size and power. We also evaluate the performance of three leading methods that attempt to quantify the effect of surrogate endpoints. The proposed method is applied to validate magnetic resonance imaging lesions as the surrogate endpoint for clinical relapses in a multiple sclerosis trial. Copyright © 2012 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/sim.4500 |
| format | Article |
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Med</addtitle><description>We present a nonparametric test to validate surrogate endpoints based on measure of divergence and random permutation. This test is a proposal to directly verify the Prentice statistical definition of surrogacy. The test does not impose distributional assumptions on the endpoints, and it is robust to model misspecification. Our simulation study shows that the proposed nonparametric test outperforms the practical test of the Prentice criterion in terms of both robustness of size and power. We also evaluate the performance of three leading methods that attempt to quantify the effect of surrogate endpoints. The proposed method is applied to validate magnetic resonance imaging lesions as the surrogate endpoint for clinical relapses in a multiple sclerosis trial. Copyright © 2012 John Wiley & Sons, Ltd.</description><subject>Adjuvants, Immunologic - therapeutic use</subject><subject>Biomarkers - analysis</subject><subject>Biomarkers - metabolism</subject><subject>Computer Simulation - statistics & numerical data</subject><subject>Endpoint Determination - statistics & numerical data</subject><subject>Entropy</subject><subject>Humans</subject><subject>Interferon beta-1a</subject><subject>Interferon-beta - therapeutic use</subject><subject>Kullback-Leibler divergence</subject><subject>Magnetic Resonance Imaging</subject><subject>Medical statistics</subject><subject>Models, Statistical</subject><subject>Multiple sclerosis</subject><subject>Multiple Sclerosis - diagnosis</subject><subject>Multiple Sclerosis - drug therapy</subject><subject>Multiple Sclerosis - physiopathology</subject><subject>NMR</subject><subject>nonparametric test</subject><subject>Nuclear magnetic resonance</subject><subject>Poisson Distribution</subject><subject>Prentice criteria</subject><subject>Randomized Controlled Trials as Topic - statistics & numerical data</subject><subject>Secondary Prevention</subject><subject>Simulation</subject><subject>Statistics, Nonparametric</subject><subject>surrogate endpoints</subject><subject>Treatment Outcome</subject><subject>Validation studies</subject><issn>0277-6715</issn><issn>1097-0258</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10E9r1UAUh-FBFHutgp9AAm7cpJ75n1mWotfW3rpQEdwMk-REpyaZODNR77d3Sq8VBFdn8_By-BHylMIJBWAvk59OhAS4RzYUjK6ByeY-2QDTulaayiPyKKVrAEol0w_JEWNciIaZDdmdzhXOOYZlX7cuYV_NYV5cdBPm6LsqY8rVEGKVv2L1w42-d9mHuQpDldYYwxeXsQT6Jfg5p8fkweDGhE8O95h8fP3qw9mb-vLd9vzs9LLuRKOh7hpBTWN653gzaNSKt5obpbFhQkgOgG03cFTaoBhUC205koqGCmYG6Ht-TF7cdpcYvq_lRTv51OE4uhnDmiwFahSVWupCn_9Dr8Ma5_JdUQzAGKX432AXQ0oRB7tEP7m4L8jeTGzLxPZm4kKfHYJrO2F_B_9sWkB9C376Eff_Ddn357tD8OB9yvjrzrv4zSrNtbSfrrb28_aCXrw1O3vFfwN6FZMn</recordid><startdate>20120630</startdate><enddate>20120630</enddate><creator>Miao, Xiaopeng</creator><creator>Wang, Yong-Cheng</creator><creator>Gangopadhyay, Ashis</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope></search><sort><creationdate>20120630</creationdate><title>An entropy-based nonparametric test for the validation of surrogate endpoints</title><author>Miao, Xiaopeng ; Wang, Yong-Cheng ; Gangopadhyay, Ashis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4870-c841989daa38f7e763b73967e82445300ebcf3e679e4f6b0be4f51481429f0dd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Adjuvants, Immunologic - therapeutic use</topic><topic>Biomarkers - analysis</topic><topic>Biomarkers - metabolism</topic><topic>Computer Simulation - statistics & numerical data</topic><topic>Endpoint Determination - statistics & numerical data</topic><topic>Entropy</topic><topic>Humans</topic><topic>Interferon beta-1a</topic><topic>Interferon-beta - therapeutic use</topic><topic>Kullback-Leibler divergence</topic><topic>Magnetic Resonance Imaging</topic><topic>Medical statistics</topic><topic>Models, Statistical</topic><topic>Multiple sclerosis</topic><topic>Multiple Sclerosis - diagnosis</topic><topic>Multiple Sclerosis - drug therapy</topic><topic>Multiple Sclerosis - physiopathology</topic><topic>NMR</topic><topic>nonparametric test</topic><topic>Nuclear magnetic resonance</topic><topic>Poisson Distribution</topic><topic>Prentice criteria</topic><topic>Randomized Controlled Trials as Topic - statistics & numerical data</topic><topic>Secondary Prevention</topic><topic>Simulation</topic><topic>Statistics, Nonparametric</topic><topic>surrogate endpoints</topic><topic>Treatment Outcome</topic><topic>Validation studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Miao, Xiaopeng</creatorcontrib><creatorcontrib>Wang, Yong-Cheng</creatorcontrib><creatorcontrib>Gangopadhyay, Ashis</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Statistics in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Miao, Xiaopeng</au><au>Wang, Yong-Cheng</au><au>Gangopadhyay, Ashis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An entropy-based nonparametric test for the validation of surrogate endpoints</atitle><jtitle>Statistics in medicine</jtitle><addtitle>Statist. Med</addtitle><date>2012-06-30</date><risdate>2012</risdate><volume>31</volume><issue>14</issue><spage>1517</spage><epage>1530</epage><pages>1517-1530</pages><issn>0277-6715</issn><eissn>1097-0258</eissn><coden>SMEDDA</coden><abstract>We present a nonparametric test to validate surrogate endpoints based on measure of divergence and random permutation. This test is a proposal to directly verify the Prentice statistical definition of surrogacy. The test does not impose distributional assumptions on the endpoints, and it is robust to model misspecification. Our simulation study shows that the proposed nonparametric test outperforms the practical test of the Prentice criterion in terms of both robustness of size and power. We also evaluate the performance of three leading methods that attempt to quantify the effect of surrogate endpoints. 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| fulltext | fulltext |
| identifier | ISSN: 0277-6715 |
| ispartof | Statistics in medicine, 2012-06, Vol.31 (14), p.1517-1530 |
| issn | 0277-6715 1097-0258 |
| language | eng |
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| source | MEDLINE; Access via Wiley Online Library |
| subjects | Adjuvants, Immunologic - therapeutic use Biomarkers - analysis Biomarkers - metabolism Computer Simulation - statistics & numerical data Endpoint Determination - statistics & numerical data Entropy Humans Interferon beta-1a Interferon-beta - therapeutic use Kullback-Leibler divergence Magnetic Resonance Imaging Medical statistics Models, Statistical Multiple sclerosis Multiple Sclerosis - diagnosis Multiple Sclerosis - drug therapy Multiple Sclerosis - physiopathology NMR nonparametric test Nuclear magnetic resonance Poisson Distribution Prentice criteria Randomized Controlled Trials as Topic - statistics & numerical data Secondary Prevention Simulation Statistics, Nonparametric surrogate endpoints Treatment Outcome Validation studies |
| title | An entropy-based nonparametric test for the validation of surrogate endpoints |
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