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<meta name="DC.Title" content="Modeling MR Imaging Enhancing-Lesion Volumes in Multiple Sclerosis: Application in Clinical Trials" />
<meta name="DC.Identifier" content="10.3174/ajnr.A2691" />
<meta name="DC.Date" content="2011-12-01" />
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<meta name="DC.Description" content="BACKGROUND AND PURPOSE: Although the number of enhancing lesions is the typical outcome measure of choice in clinical trials in MS, a potentially more sensitive and statistically more powerful outcome measure is the volume of enhancing lesions. In this study, we assessed the distribution and statistical power of the volume of enhancing brain lesions as an outcome measure by means of their required sample size, and we compared the results with the number of enhancing lesions.

MATERIAL AND METHODS: First, a literature search was performed to compare the effects of treatment on the number and volume of enhancing lesions. Then, a statistical model was proposed to describe the distribution of the volume of enhancing lesions in 2 datasets of patients with RRMS, and sample sizes for enhancing-lesion volume as primary outcome measure were calculated.

RESULTS: A mixture of the binomial and Weibull distribution was determined to model enhancing-lesion volumes in patients. Sample size calculations for enhancing-lesion volumes showed that approximately 94 patients per arm would be required to detect a combination of 20% decrease in lesion volume and 20% increase in patients without enhancing lesions, whereas calculations for enhancing-lesion counts showed that approximately 129 patients would be required to detect a 50% decrease.

CONCLUSIONS: The mixture of the binomial and Weibull distribution is a suitable approach in modeling new enhancing-lesion volumes in MS and yielded feasible sample size estimates for clinical trials, showing lesion volumes to be an advantageous outcome measure compared with lesion counts in terms of statistical power.

Gd
:   gadolinium
MS
:   multiple sclerosis
NB
:   negative binomial
RRMS
:   relapsing-remitting multiple sclerosis
SPMS
:   secondary-progressive multiple sclerosis" />
<meta name="DC.Contributor" content="I.J. van den Elskamp" />
<meta name="DC.Contributor" content="D.L. Knol" />
<meta name="DC.Contributor" content="B.M.J. Uitdehaag" />
<meta name="DC.Contributor" content="F. Barkhof" />
<meta name="article:published_time" content="2011-12-01" />
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<meta name="citation_title" content="Modeling MR Imaging Enhancing-Lesion Volumes in Multiple Sclerosis: Application in Clinical Trials" />
<meta name="citation_abstract" lang="en" content="&lt;h3&gt;BACKGROUND AND PURPOSE:&lt;/h3&gt;
&lt;p&gt;Although the number of enhancing lesions is the typical outcome measure of choice in clinical trials in MS, a potentially more sensitive and statistically more powerful outcome measure is the volume of enhancing lesions. In this study, we assessed the distribution and statistical power of the volume of enhancing brain lesions as an outcome measure by means of their required sample size, and we compared the results with the number of enhancing lesions.&lt;/p&gt;&lt;h3&gt;MATERIAL AND METHODS:&lt;/h3&gt;
&lt;p&gt;First, a literature search was performed to compare the effects of treatment on the number and volume of enhancing lesions. Then, a statistical model was proposed to describe the distribution of the volume of enhancing lesions in 2 datasets of patients with RRMS, and sample sizes for enhancing-lesion volume as primary outcome measure were calculated.&lt;/p&gt;&lt;h3&gt;RESULTS:&lt;/h3&gt;
&lt;p&gt;A mixture of the binomial and Weibull distribution was determined to model enhancing-lesion volumes in patients. Sample size calculations for enhancing-lesion volumes showed that approximately 94 patients per arm would be required to detect a combination of 20% decrease in lesion volume and 20% increase in patients without enhancing lesions, whereas calculations for enhancing-lesion counts showed that approximately 129 patients would be required to detect a 50% decrease.&lt;/p&gt;&lt;h3&gt;CONCLUSIONS:&lt;/h3&gt;
&lt;p&gt;The mixture of the binomial and Weibull distribution is a suitable approach in modeling new enhancing-lesion volumes in MS and yielded feasible sample size estimates for clinical trials, showing lesion volumes to be an advantageous outcome measure compared with lesion counts in terms of statistical power.&lt;/p&gt;" />
<meta name="citation_journal_title" content="American Journal of Neuroradiology" />
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<meta name="citation_publication_date" content="2011/12/01" />
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<meta name="twitter:title" content="Modeling MR Imaging Enhancing-Lesion Volumes in Multiple Sclerosis: Application in Clinical Trials" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:image" content="http://ajnr-stage2.stage.highwire.org/sites/default/files/highwire/ajnr/32/11.cover-source.jpg" />
<meta name="twitter:description" content="BACKGROUND AND PURPOSE: Although the number of enhancing lesions is the typical outcome measure of choice in clinical trials in MS, a potentially more sensitive and statistically more powerful outcome measure is the volume of enhancing lesions. In this study, we assessed the distribution and statistical power of the volume of enhancing brain lesions as an outcome measure by means of their required sample size, and we compared the results with the number of enhancing lesions.

MATERIAL AND METHODS: First, a literature search was performed to compare the effects of treatment on the number and volume of enhancing lesions. Then, a statistical model was proposed to describe the distribution of the volume of enhancing lesions in 2 datasets of patients with RRMS, and sample sizes for enhancing-lesion volume as primary outcome measure were calculated.

RESULTS: A mixture of the binomial and Weibull distribution was determined to model enhancing-lesion volumes in patients. Sample size calculations for enhancing-lesion volumes showed that approximately 94 patients per arm would be required to detect a combination of 20% decrease in lesion volume and 20% increase in patients without enhancing lesions, whereas calculations for enhancing-lesion counts showed that approximately 129 patients would be required to detect a 50% decrease.

CONCLUSIONS: The mixture of the binomial and Weibull distribution is a suitable approach in modeling new enhancing-lesion volumes in MS and yielded feasible sample size estimates for clinical trials, showing lesion volumes to be an advantageous outcome measure compared with lesion counts in terms of statistical power.

Gd
:   gadolinium
MS
:   multiple sclerosis
NB
:   negative binomial
RRMS
:   relapsing-remitting multiple sclerosis
SPMS
:   secondary-progressive multiple sclerosis" />
<meta name="og-title" property="og:title" content="Modeling MR Imaging Enhancing-Lesion Volumes in Multiple Sclerosis: Application in Clinical Trials" />
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<meta name="og-site-name" property="og:site_name" content="American Journal of Neuroradiology" />
<meta name="og-description" property="og:description" content="BACKGROUND AND PURPOSE: Although the number of enhancing lesions is the typical outcome measure of choice in clinical trials in MS, a potentially more sensitive and statistically more powerful outcome measure is the volume of enhancing lesions. In this study, we assessed the distribution and statistical power of the volume of enhancing brain lesions as an outcome measure by means of their required sample size, and we compared the results with the number of enhancing lesions.

MATERIAL AND METHODS: First, a literature search was performed to compare the effects of treatment on the number and volume of enhancing lesions. Then, a statistical model was proposed to describe the distribution of the volume of enhancing lesions in 2 datasets of patients with RRMS, and sample sizes for enhancing-lesion volume as primary outcome measure were calculated.

RESULTS: A mixture of the binomial and Weibull distribution was determined to model enhancing-lesion volumes in patients. Sample size calculations for enhancing-lesion volumes showed that approximately 94 patients per arm would be required to detect a combination of 20% decrease in lesion volume and 20% increase in patients without enhancing lesions, whereas calculations for enhancing-lesion counts showed that approximately 129 patients would be required to detect a 50% decrease.

CONCLUSIONS: The mixture of the binomial and Weibull distribution is a suitable approach in modeling new enhancing-lesion volumes in MS and yielded feasible sample size estimates for clinical trials, showing lesion volumes to be an advantageous outcome measure compared with lesion counts in terms of statistical power.

Gd
:   gadolinium
MS
:   multiple sclerosis
NB
:   negative binomial
RRMS
:   relapsing-remitting multiple sclerosis
SPMS
:   secondary-progressive multiple sclerosis" />
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<script>window.dataLayer = window.dataLayer || [];window.dataLayer.push({"page":{"hwTitle":"Modeling MR Imaging Enhancing-Lesion Volumes in Multiple Sclerosis: Application in Clinical Trials","hwUrl":"http://ajnr-stage2.stage.highwire.org/content/32/11/2093","hwPath":"/content/32/11/2093.abstract","hwType":"journal page"},"user":{"hwMemberID":"","hwLoggedIntoInstitutionID":"","hwLoggedIntoInstitution":"","hwAccessUsageTerms":"guest","hwRegisteredUser":"no","hwHasFullTextAccess":"yes","hwEntitled":"no"},"content":{"hwIssueDate":"2011-12-01","hwIssueDateTime":"2011-12-01 00:00:00","hwPublicationDate":"2011-12-13","hwPublicationDateTime":"2011-12-13 08:03:45","hwNlmArticleType":"research-article","hwDOI":"10.3174/ajnr.A2691","hwAuthors":"I.J. van den Elskamp,D.L. Knol,B.M.J. Uitdehaag,F. Barkhof","hwContributors":"","hwIsEarlyRelease":"no","hwEissn":"1936-959X","hwPissn":"0195-6108","hwVolume":"32","hwIssue":"11","hwArticleType":"Brain","hwTabView":"main_tab","hwViewType":"abstract","hwAccessType":"Free","hwOpenAccess":"no","hwFreeAccess":"yes","hwTaxonomy":"","hwCorpusCode":"ajnr","hwType":"article","hwSubtitle":null,"hwOverline":null,"hwPisaId":"ajnr;32/11/2093","hwFirstPage":"2093","hwLastPage":"2097","hwIsLatestVersion":"yes","hwIsCurrentIssue":"no","hwIsOpenIssue":"no","hwHasFullText":"yes","hwHasPDF":"yes","hwParentTitle":"American Journal of Neuroradiology: 32 (11)","hwElectronicPubDate":"2011-12-13","hwElectronicPubDateTime":"2011-12-13 08:03:45"}});
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<div id="hw-article-author-popups-top-node-11688--6670485744" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">I.J. van den Elskamp </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Radiology (I.J.v.d.E., F.B.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=I.J.%2Bvan%2Bden%2BElskamp%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=van%20den%20Elskamp%20I&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AI.J.%2Bvan%2Bden%2BElskamp%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">D.L. Knol </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Epidemiology and Biostatistics (D.L.K., B.M.J.U.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=D.L.%2BKnol%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Knol%20D&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AD.L.%2BKnol%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">B.M.J. Uitdehaag </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>b</span>Epidemiology and Biostatistics (D.L.K., B.M.J.U.)</div><div class='author-affiliation'><span class='nlm-sup'>c</span>Neurology (B.M.J.U.), VU University Medical Center, Amsterdam, the Netherlands.</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=B.M.J.%2BUitdehaag%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Uitdehaag%20B&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AB.M.J.%2BUitdehaag%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">F. Barkhof </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>a</span>From the Departments of Radiology (I.J.v.d.E., F.B.)</div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&amp;gs_type=author&amp;author%5B0%5D=F.%2BBarkhof%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Barkhof%20F&amp;link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AF.%2BBarkhof%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div></div></div>  </div>

  
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<p id="p-1"><strong>BACKGROUND AND PURPOSE:</strong> Although the number of enhancing lesions is the typical outcome measure of choice in clinical trials in MS, a potentially more sensitive and statistically more powerful outcome measure is the volume of enhancing lesions. In this study, we assessed the distribution and statistical power of the volume of enhancing brain lesions as an outcome measure by means of their required sample size, and we compared the results with the number of enhancing lesions.</p>
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<p id="p-2"><strong>MATERIAL AND METHODS:</strong> First, a literature search was performed to compare the effects of treatment on the number and volume of enhancing lesions. Then, a statistical model was proposed to describe the distribution of the volume of enhancing lesions in 2 datasets of patients with RRMS, and sample sizes for enhancing-lesion volume as primary outcome measure were calculated.</p>
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<p id="p-3"><strong>RESULTS:</strong> A mixture of the binomial and Weibull distribution was determined to model enhancing-lesion volumes in patients. Sample size calculations for enhancing-lesion volumes showed that approximately 94 patients per arm would be required to detect a combination of 20% decrease in lesion volume and 20% increase in patients without enhancing lesions, whereas calculations for enhancing-lesion counts showed that approximately 129 patients would be required to detect a 50% decrease.</p>
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<p id="p-4"><strong>CONCLUSIONS:</strong> The mixture of the binomial and Weibull distribution is a suitable approach in modeling new enhancing-lesion volumes in MS and yielded feasible sample size estimates for clinical trials, showing lesion volumes to be an advantageous outcome measure compared with lesion counts in terms of statistical power.</p>
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</div><div class="section glossary" id="glossary-1">
<h2>ABBREVIATIONS</h2>
<dl id="def-list-1"><dt id="def-item-1">Gd<span class="def-item-dt-sep"></span></dt><dd>gadolinium<span class="def-item-dd-sep"></span></dd><dt id="def-item-2">MS<span class="def-item-dt-sep"></span></dt><dd>multiple sclerosis<span class="def-item-dd-sep"></span></dd><dt id="def-item-3">NB<span class="def-item-dt-sep"></span></dt><dd>negative binomial<span class="def-item-dd-sep"></span></dd><dt id="def-item-4">RRMS<span class="def-item-dt-sep"></span></dt><dd>relapsing-remitting multiple sclerosis<span class="def-item-dd-sep"></span></dd><dt id="def-item-5">SPMS<span class="def-item-dt-sep"></span></dt><dd>secondary-progressive multiple sclerosis</dd></dl>
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      <div class="highwire-cite-title">Modeling MR Imaging Enhancing-Lesion Volumes in Multiple Sclerosis: Application in Clinical Trials</div>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">I.J.</span> <span class="nlm-surname">van den Elskamp</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">D.L.</span> <span class="nlm-surname">Knol</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">B.M.J.</span> <span class="nlm-surname">Uitdehaag</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">F.</span> <span class="nlm-surname">Barkhof</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Dec 2011, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">32 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2093-2097; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A2691 </span></div>
  
  
  
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      <div class="highwire-cite-title">Modeling MR Imaging Enhancing-Lesion Volumes in Multiple Sclerosis: Application in Clinical Trials</div>  
    	<div class="highwire-cite-authors"><span  class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">I.J.</span> <span class="nlm-surname">van den Elskamp</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">D.L.</span> <span class="nlm-surname">Knol</span></span>, <span class="highwire-citation-author" data-delta="2"><span class="nlm-given-names">B.M.J.</span> <span class="nlm-surname">Uitdehaag</span></span>, <span class="highwire-citation-author" data-delta="3"><span class="nlm-given-names">F.</span> <span class="nlm-surname">Barkhof</span></span></span></div>
  
    	<div class="highwire-cite-metadata"><span  class="highwire-cite-metadata-journal highwire-cite-metadata">American Journal of Neuroradiology </span><span  class="highwire-cite-metadata-date highwire-cite-metadata">Dec 2011, </span><span  class="highwire-cite-metadata-volume highwire-cite-metadata">32 </span><span  class="highwire-cite-metadata-issue highwire-cite-metadata">(11) </span><span  class="highwire-cite-metadata-pages highwire-cite-metadata">2093-2097; </span><span  class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">DOI:</span> 10.3174/ajnr.A2691 </span></div>
  
  
  
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