{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T13:45:08Z","timestamp":1790171108958,"version":"4.1.0"},"reference-count":38,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2011,2,1]],"date-time":"2011-02-01T00:00:00Z","timestamp":1296518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Clinical Trials"],"published-print":{"date-parts":[[2011,2]]},"abstract":"<jats:p>Background Therapeutic trials of disease-modifying agents on Alzheimer\u2019s disease (AD) require novel designs and analyses involving switch of treatments for at least a portion of subjects enrolled. Randomized start and randomized withdrawal designs are two examples of such designs. Crucial design parameters such as sample size and the time of treatment switch are important to understand in designing such clinical trials.<\/jats:p>\n                  <jats:p>Purpose The purpose of this article is to provide methods to determine sample sizes and time of treatment switch as well as optimum statistical tests of treatment efficacy for clinical trials of disease-modifying agents on AD.<\/jats:p>\n                  <jats:p>Methods A general linear mixed effects model is proposed to test the disease-modifying efficacy of novel therapeutic agents on AD. This model links the longitudinal growth from both the placebo arm and the treatment arm at the time of treatment switch for these in the delayed treatment arm or early withdrawal arm and incorporates the potential correlation on the rate of cognitive change before and after the treatment switch. Sample sizes and the optimum time for treatment switch of such trials as well as optimum test statistic for the treatment efficacy are determined according to the model.<\/jats:p>\n                  <jats:p>Results Assuming an evenly spaced longitudinal design over a fixed duration, the optimum treatment switching time in a randomized start or a randomized withdrawal trial is half way through the trial. With the optimum test statistic for the treatment efficacy and over a wide spectrum of model parameters, the optimum sample size allocations are fairly close to the simplest design with a sample size ratio of 1:1:1 among the treatment arm, the delayed treatment or early withdrawal arm, and the placebo arm. The application of the proposed methodology to AD provides evidence that much larger sample sizes are required to adequately power disease-modifying trials when compared with those for symptomatic agents, even when the treatment switch time and efficacy test are optimally chosen.<\/jats:p>\n                  <jats:p>Limitations The proposed method assumes that the only and immediate effect of treatment switch is on the rate of cognitive change.<\/jats:p>\n                  <jats:p>Conclusions Crucial design parameters for the clinical trials of disease-modifying agents on AD can be optimally chosen. Government and industry officials as well as academia researchers should consider the optimum use of the clinical trials design for disease-modifying agents on AD in their effort to search for the treatments with the potential to modify the underlying pathophysiology of AD.<\/jats:p>","DOI":"10.1177\/1740774510392391","type":"journal-article","created":{"date-parts":[[2011,2,18]],"date-time":"2011-02-18T18:58:13Z","timestamp":1298055493000},"page":"15-26","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Designing clinical trials to test disease-modifying agents: application to the treatment trials of Alzheimer\u2019s disease"],"prefix":"10.1177","volume":"8","author":[{"given":"Chengjie","family":"Xiong","sequence":"first","affiliation":[{"name":"Division of Biostatistics, School of Medicine, Washington University, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerald","family":"van Belle","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA"},{"name":"Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J Philip","family":"Miller","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, School of Medicine, Washington University, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John C","family":"Morris","sequence":"additional","affiliation":[{"name":"Department of Neurology, Pathology and Immunology, Washington University Alzheimer\u2019s Disease Research Center, St. Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2011,2,18]]},"reference":[{"key":"bibr1-1740774510392391","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2006.07.001"},{"key":"bibr2-1740774510392391","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2006.08.004"},{"key":"bibr3-1740774510392391","doi-asserted-by":"publisher","DOI":"10.1038\/nrn1495"},{"key":"bibr4-1740774510392391","doi-asserted-by":"publisher","DOI":"10.1002\/sim.1718"},{"key":"bibr5-1740774510392391","first-page":"S10","volume":"11","author":"Leber P","year":"1997","journal-title":"Alzheimer Dis Assoc Disord"},{"key":"bibr6-1740774510392391","first-page":"113","volume":"10","author":"Sampaio C","year":"2006","journal-title":"J Nutr Health 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