Patricia Krause, Philipp Mahlknecht, Inger Marie Skogseid, Frank Steigerwald, Günther Deuschl, Richard Erasmi, Alfons Schnitzler, Tobias Warnecke, Jörg Müller, Werner Poewe, Gerd-Helge Schneider, Jan Vesper, Nils Warneke, Wilhelm Eisner, Thomas Prokop, Jan-Uwe Müller, Jens Volkmann, Andrea A Kühn
Pubu M. Abeyasinghe PhD, James H. Cole PhD, Adeel Razi PhD, Govinda R. Poudel PhD, Jane S. Paulsen PhD, Sarah J. Tabrizi PhD, Jeffrey D. Long PhD, Nellie Georgiou-Karistianis PhD
Umar Zubair cand. med., Habibah A.P. Agianda MD, Kathryn Yang MBChB, FRCPC, Amy Tam BSc, Joshua Rong BSc, Carolina Gorodetsky MD, MSc, Shekeeb S. Mohammad MBBS, FRACP, PhD, Juan Darío Ortigoza-Escobar MD, PhD, Darius Ebrahimi-Fakhari MD, PhD
{"title":"DBSMatchMaker: Connecting Clinicians Globally for Deep Brain Stimulation in Rare Diseases","authors":"Umar Zubair cand. med., Habibah A.P. Agianda MD, Kathryn Yang MBChB, FRCPC, Amy Tam BSc, Joshua Rong BSc, Carolina Gorodetsky MD, MSc, Shekeeb S. Mohammad MBBS, FRACP, PhD, Juan Darío Ortigoza-Escobar MD, PhD, Darius Ebrahimi-Fakhari MD, PhD","doi":"10.1002/mds.30131","DOIUrl":"10.1002/mds.30131","url":null,"abstract":"","PeriodicalId":213,"journal":{"name":"Movement Disorders","volume":"40 4","pages":"765-767"},"PeriodicalIF":7.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mirella Russo MD, MSc, Tommaso Costa PhD, Dario Calisi MD, MSc, Stefano L. Sensi MD, PhD
{"title":"Prasinezumab: A Bayesian Perspective on Its Efficacy","authors":"Mirella Russo MD, MSc, Tommaso Costa PhD, Dario Calisi MD, MSc, Stefano L. Sensi MD, PhD","doi":"10.1002/mds.30129","DOIUrl":"10.1002/mds.30129","url":null,"abstract":"<p>This study employed a Bayesian approach to examine the impact of prasinezumab on the progression of PD symptoms and signs. We used the BF in hypothesis testing. The BF is inherently comparative: it weighs the support for one model against that of another. Moreover, BFs do so by fully conditioning on the observed data. Otherwise, the <i>P</i> value depends on hypothetical outcomes that are more extreme than those observed in the sample. Such practice violates the likelihood principle and results in inconsistent or paradoxical conclusions. The BF can quantify evidence in favor of the null hypothesis. In the Bayesian framework, no special status is attached to either of the hypotheses under test; the BF assesses each model's predictive performance and expresses a preference for the model that made the most accurate forecasts. The fact that the BF can quantify evidence in favor of the null hypothesis can be of substantive importance. For instance, the hypothesis of interest may predict the absence of an effect across a varying set of conditions. Quantifying the null hypothesis is also important to learn whether the observed data provide evidence of absence or absence.</p><p>Specifically, the possible outcomes of the BF can be assigned to three discrete categories: (1) evidence in favor of <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mn>1</mn>\u0000 </msub>\u0000 </mrow></math> (ie, evidence in favor of the presence of an effect), (2) evidence in favor of <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mn>0</mn>\u0000 </msub>\u0000 </mrow></math> (ie, evidence in favor of the absence of an effect), and (3) evidence that favors neither <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mn>1</mn>\u0000 </msub>\u0000 </mrow></math> nor <span></span><math>\u0000 <mrow>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mn>0</mn>\u0000 </msub>\u0000 </mrow></math>. Instead, the <i>P</i> value cannot provide a measure of evidence in favor of the null hypothesis. Finally, the BF is not affected by the sampling plan, that is, the intention with which the data were collected. This irrelevance follows from the likelihood principle, and it means that BFs may be computed and interpreted even when the intention with which the data are collected is ambiguous, unknown, or absent. All these advantages are not available if a classical analysis is performed as was done for the PASADENA trial data.</p><p>Based on the findings shown in the first table of the source article<span><sup>21</sup></span> (Table 1), a Bayesian analysis of the results obtained in these subpopulations was carried out. The results of the Bayesian analysis are s","PeriodicalId":213,"journal":{"name":"Movement Disorders","volume":"40 4","pages":"619-624"},"PeriodicalIF":7.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mds.30129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}