加速药物合作组织精神分裂症项目的数据分析策略

Data analysis strategies for the Accelerating Medicines Partnership® Schizophrenia Program.

作者信息

Penzel Nora, Polosecki Pablo, Addington Jean, Arango Celso, Asgari-Targhi Ameneh, Billah Tashrif, Bouix Sylvain, Calkins Monica E, Campbell Dylan E, Cannon Tyrone D, Castro Eduardo, Cho Kang Ik K, Coleman Michael J, Corcoran Cheryl M, Dwyer Dominic, Frangou Sophia, Fusar-Poli Paolo, Glynn Robert J, Haidar Anastasia, Harms Michael P, Jacobs Grace R, Kambeitz Joseph, Kapur Tina, Kelly Sinead M, Koutsouleris Nikolaos, Abhinandan K R, Kucukemiroglu Saryet, Kwon Jun Soo, Lewandowski Kathryn E, Li Qingqin S, Mantua Valentina, Mathalon Daniel H, Mittal Vijay A, Nicholas Spero, Pandina Gahan J, Perkins Diana O, Potter Andrew, Reichenberg Abraham, Reinen Jenna, Sand Michael S, Seitz-Holland Johanna, Shah Jai L, Srinivasan Vairavan, Srivastava Agrima, Stone William S, Torous John, Vangel Mark G, Wang Jijun, Wolff Phillip, Yao Beier, Anticevic Alan, Wolf Daniel H, Zhu Hao, Bearden Carrie E, McGorry Patrick D, Nelson Barnaby, Kane John M, Woods Scott W, Kahn René S, Shenton Martha E, Cecchi Guillermo, Pasternak Ofer

机构信息

Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Department of Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

出版信息

Schizophrenia (Heidelb). 2025 Apr 3;11(1):53. doi: 10.1038/s41537-025-00561-w.

DOI:10.1038/s41537-025-00561-w

PMID:40180950

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11968818/

Abstract

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 .

摘要

加速药物合作组织精神分裂症项目（AMP® SCZ）对大量有精神病临床高危风险（CHR）的个体以及社区对照人群进行评估。研究对象来自五大洲的43个研究点。评估内容包括与以往CHR研究中所涉及的类似领域，以及在2年时间内纵向收集的新领域。在数据采集的同时，多学科专家团队一直在努力制定AMP SCZ项目的数据分析策略。在此，我们描述数据分析的关键原则。AMP SCZ分析的主要目标是利用基线临床评估和多模态生物标志物来预测CHR个体的临床终点。这些终点在AMP SCZ研究中被定义为向精神病的转变（即转化）、CHR综合征的缓解，以及在基线评估后1年和2年时获得的持续性CHR综合征（未转化/未缓解）。次要目标是利用所有时间点的纵向临床评估和多模态生物标志物来确定区分CHR个体亚组的临床轨迹。分析计划的设计参考了以往的数据以及类似国际CHR研究的分析方法。此外，我们还考虑了新获取数据与现有以往数据不同的特性。以往数据用于辅助分析流程构建、进行基准实验、量化临床概念以及做出旨在克服以往研究中所遇到挑战的设计决策。我们展示了AMP SCZ项目的分析设计、应对与分析计划相关挑战的缓解策略，为关键决策提供理由，并举例说明以往数据如何被用于支持对多模态和纵向数据进行分析的设计决策。观看奥弗·帕斯特纳克教授讨论他的工作及本文：https://vimeo.com/1023394132?share=copy#t=0 。