Nora Penzel, Pablo Polosecki, Jean Addington, Celso Arango, Ameneh Asgari-Targhi, Tashrif Billah, Sylvain Bouix, Monica E Calkins, Dylan E Campbell, Tyrone D Cannon, Eduardo Castro, Kang Ik K Cho, Michael J Coleman, Cheryl M Corcoran, Dominic Dwyer, Sophia Frangou, Paolo Fusar-Poli, Robert J Glynn, Anastasia Haidar, Michael P Harms, Grace R Jacobs, Joseph Kambeitz, Tina Kapur, Sinead M Kelly, Nikolaos Koutsouleris, K R Abhinandan, Saryet Kucukemiroglu, Jun Soo Kwon, Kathryn E Lewandowski, Qingqin S Li, Valentina Mantua, Daniel H Mathalon, Vijay A Mittal, Spero Nicholas, Gahan J Pandina, Diana O Perkins, Andrew Potter, Abraham Reichenberg, Jenna Reinen, Michael S Sand, Johanna Seitz-Holland, Jai L Shah, Vairavan Srinivasan, Agrima Srivastava, William S Stone, John Torous, Mark G Vangel, Jijun Wang, Phillip Wolff, Beier Yao, Alan Anticevic, Daniel H Wolf, Hao Zhu, Carrie E Bearden, Patrick D McGorry, Barnaby Nelson, John M Kane, Scott W Woods, René S Kahn, Martha E Shenton, Guillermo Cecchi, Ofer Pasternak
{"title":"加速药物伙伴关系®精神分裂症项目的数据分析策略。","authors":"Nora Penzel, Pablo Polosecki, Jean Addington, Celso Arango, Ameneh Asgari-Targhi, Tashrif Billah, Sylvain Bouix, Monica E Calkins, Dylan E Campbell, Tyrone D Cannon, Eduardo Castro, Kang Ik K Cho, Michael J Coleman, Cheryl M Corcoran, Dominic Dwyer, Sophia Frangou, Paolo Fusar-Poli, Robert J Glynn, Anastasia Haidar, Michael P Harms, Grace R Jacobs, Joseph Kambeitz, Tina Kapur, Sinead M Kelly, Nikolaos Koutsouleris, K R Abhinandan, Saryet Kucukemiroglu, Jun Soo Kwon, Kathryn E Lewandowski, Qingqin S Li, Valentina Mantua, Daniel H Mathalon, Vijay A Mittal, Spero Nicholas, Gahan J Pandina, Diana O Perkins, Andrew Potter, Abraham Reichenberg, Jenna Reinen, Michael S Sand, Johanna Seitz-Holland, Jai L Shah, Vairavan Srinivasan, Agrima Srivastava, William S Stone, John Torous, Mark G Vangel, Jijun Wang, Phillip Wolff, Beier Yao, Alan Anticevic, Daniel H Wolf, Hao Zhu, Carrie E Bearden, Patrick D McGorry, Barnaby Nelson, John M Kane, Scott W Woods, René S Kahn, Martha E Shenton, Guillermo Cecchi, Ofer Pasternak","doi":"10.1038/s41537-025-00561-w","DOIUrl":null,"url":null,"abstract":"<p><p>The Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ) project assesses a large sample of individuals at clinical high-risk for developing psychosis (CHR) and community controls. Subjects are enrolled in 43 sites across 5 continents. The assessments include domains similar to those acquired in previous CHR studies along with novel domains that are collected longitudinally across a period of 2 years. In parallel with the data acquisition, multidisciplinary teams of experts have been working to formulate the data analysis strategy for the AMP SCZ project. Here, we describe the key principles for the data analysis. The primary AMP SCZ analysis aim is to use baseline clinical assessments and multimodal biomarkers to predict clinical endpoints of CHR individuals. These endpoints are defined for the AMP SCZ study as transition to psychosis (i.e., conversion), remission from CHR syndrome, and persistent CHR syndrome (non-conversion/non-remission) obtained at one year and two years after baseline assessment. The secondary aim is to use longitudinal clinical assessments and multimodal biomarkers from all time points to identify clinical trajectories that differentiate subgroups of CHR individuals. The design of the analysis plan is informed by reviewing legacy data and the analytic approaches from similar international CHR studies. In addition, we consider properties of the newly acquired data that are distinct from the available legacy data. Legacy data are used to assist analysis pipeline building, perform benchmark experiments, quantify clinical concepts and to make design decisions meant to overcome the challenges encountered in previous studies. We present the analytic design of the AMP SCZ project, mitigation strategies to address challenges related to the analysis plan, provide rationales for key decisions, and present examples of how the legacy data have been used to support design decisions for the analysis of the multimodal and longitudinal data. Watch Prof. Ofer Pasternak discuss his work and this article: https://vimeo.com/1023394132?share=copy#t=0 .</p>","PeriodicalId":74758,"journal":{"name":"Schizophrenia (Heidelberg, Germany)","volume":"11 1","pages":"53"},"PeriodicalIF":3.0000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11968818/pdf/","citationCount":"0","resultStr":"{\"title\":\"Data analysis strategies for the Accelerating Medicines Partnership® Schizophrenia Program.\",\"authors\":\"Nora Penzel, Pablo Polosecki, Jean Addington, Celso Arango, Ameneh Asgari-Targhi, Tashrif Billah, Sylvain Bouix, Monica E Calkins, Dylan E Campbell, Tyrone D Cannon, Eduardo Castro, Kang Ik K Cho, Michael J Coleman, Cheryl M Corcoran, Dominic Dwyer, Sophia Frangou, Paolo Fusar-Poli, Robert J Glynn, Anastasia Haidar, Michael P Harms, Grace R Jacobs, Joseph Kambeitz, Tina Kapur, Sinead M Kelly, Nikolaos Koutsouleris, K R Abhinandan, Saryet Kucukemiroglu, Jun Soo Kwon, Kathryn E Lewandowski, Qingqin S Li, Valentina Mantua, Daniel H Mathalon, Vijay A Mittal, Spero Nicholas, Gahan J Pandina, Diana O Perkins, Andrew Potter, Abraham Reichenberg, Jenna Reinen, Michael S Sand, Johanna Seitz-Holland, Jai L Shah, Vairavan Srinivasan, Agrima Srivastava, William S Stone, John Torous, Mark G Vangel, Jijun Wang, Phillip Wolff, Beier Yao, Alan Anticevic, Daniel H Wolf, Hao Zhu, Carrie E Bearden, Patrick D McGorry, Barnaby Nelson, John M Kane, Scott W Woods, René S Kahn, Martha E Shenton, Guillermo Cecchi, Ofer Pasternak\",\"doi\":\"10.1038/s41537-025-00561-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ) project assesses a large sample of individuals at clinical high-risk for developing psychosis (CHR) and community controls. Subjects are enrolled in 43 sites across 5 continents. The assessments include domains similar to those acquired in previous CHR studies along with novel domains that are collected longitudinally across a period of 2 years. In parallel with the data acquisition, multidisciplinary teams of experts have been working to formulate the data analysis strategy for the AMP SCZ project. Here, we describe the key principles for the data analysis. The primary AMP SCZ analysis aim is to use baseline clinical assessments and multimodal biomarkers to predict clinical endpoints of CHR individuals. These endpoints are defined for the AMP SCZ study as transition to psychosis (i.e., conversion), remission from CHR syndrome, and persistent CHR syndrome (non-conversion/non-remission) obtained at one year and two years after baseline assessment. The secondary aim is to use longitudinal clinical assessments and multimodal biomarkers from all time points to identify clinical trajectories that differentiate subgroups of CHR individuals. The design of the analysis plan is informed by reviewing legacy data and the analytic approaches from similar international CHR studies. In addition, we consider properties of the newly acquired data that are distinct from the available legacy data. Legacy data are used to assist analysis pipeline building, perform benchmark experiments, quantify clinical concepts and to make design decisions meant to overcome the challenges encountered in previous studies. We present the analytic design of the AMP SCZ project, mitigation strategies to address challenges related to the analysis plan, provide rationales for key decisions, and present examples of how the legacy data have been used to support design decisions for the analysis of the multimodal and longitudinal data. 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Data analysis strategies for the Accelerating Medicines Partnership® Schizophrenia Program.
The Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ) project assesses a large sample of individuals at clinical high-risk for developing psychosis (CHR) and community controls. Subjects are enrolled in 43 sites across 5 continents. The assessments include domains similar to those acquired in previous CHR studies along with novel domains that are collected longitudinally across a period of 2 years. In parallel with the data acquisition, multidisciplinary teams of experts have been working to formulate the data analysis strategy for the AMP SCZ project. Here, we describe the key principles for the data analysis. The primary AMP SCZ analysis aim is to use baseline clinical assessments and multimodal biomarkers to predict clinical endpoints of CHR individuals. These endpoints are defined for the AMP SCZ study as transition to psychosis (i.e., conversion), remission from CHR syndrome, and persistent CHR syndrome (non-conversion/non-remission) obtained at one year and two years after baseline assessment. The secondary aim is to use longitudinal clinical assessments and multimodal biomarkers from all time points to identify clinical trajectories that differentiate subgroups of CHR individuals. The design of the analysis plan is informed by reviewing legacy data and the analytic approaches from similar international CHR studies. In addition, we consider properties of the newly acquired data that are distinct from the available legacy data. Legacy data are used to assist analysis pipeline building, perform benchmark experiments, quantify clinical concepts and to make design decisions meant to overcome the challenges encountered in previous studies. We present the analytic design of the AMP SCZ project, mitigation strategies to address challenges related to the analysis plan, provide rationales for key decisions, and present examples of how the legacy data have been used to support design decisions for the analysis of the multimodal and longitudinal data. Watch Prof. Ofer Pasternak discuss his work and this article: https://vimeo.com/1023394132?share=copy#t=0 .
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