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万物可“视”:临床研发可视化应用十大场景

作者：李高扬，廖珊妹，李蹊主编

出版社：科学技术文献出版社出版时间：2024-05-01

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万物可“视”:临床研发可视化应用十大场景版权信息

ISBN：9787523512265
条形码：9787523512265 ; 978-7-5235-1226-5
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药学

万物可“视”:临床研发可视化应用十大场景本书特色

近年来临床试验数据量呈指数级增加，临床相关人员在数据审阅和分析过程中遇到了新的挑战。一方面要求将分析结果精准地传达给决策者以辅助试验的策略制定；另一方面要求加速有效信息获取来帮助改善患者的治疗效果。使用合理的可视化工具，同时基于丰富的可视化模型进行数据审核和分析，能轻松地揭示数据关系，实时显示数据状态，容易呈现出数据模式和数据趋势。为了好地提高数据的利用率，本书从临床上的十大场景出发，对数据可视化的技术进行分析、探讨和实践分享，从可视化需求、技术现状及可及性、可视化的应用场景、通用原则等维度进行理论和实践案例的阐述和分享。

万物可“视”:临床研发可视化应用十大场景内容简介

近年来临床试验数据量呈指数级增加，临床相关人员在数据审阅和分析过程中遇到了新的挑战。一方面要求将分析结果精准地传达给决策者以辅助试验的策略制定；另一方面要求加速有效信息获取来帮助改善患者的治疗效果。使用合理的可视化工具，同时基于丰富的可视化模型进行数据审核和分析，能更轻松地揭示数据关系，实时显示数据状态，更容易呈现出数据模式和数据趋势。为了更好地提高数据的利用率，本书从临床上的十大场景出发，对数据可视化的技术进行分析、探讨和实践分享，从可视化需求、技术现状及可及性、可视化的应用场景、通用原则等维度进行理论和实践案例的阐述和分享。

万物可“视”:临床研发可视化应用十大场景目录

第 1章临床运营可视化 ·············································································1 1.1 项目管理的工作职责和内容 ············································································· 2 1.1.1 项目管理的定义·················································································2 1.1.2 项目管理的工作内容 ··········································································2 1.2 项目管理可视化应用的场景 ············································································· 3 1.2.1 项目进度管理····················································································3 1.2.2 项目成本管理·················································································· 11 1.2.3 质量管理 ······················································································· 17 1.2.4 人员管理 ······················································································· 21 1.2.5 项目文档管理·················································································· 24 1.2.6 项目中的监查和远程智能监查的可视化管理 ············································ 26 1.3 项目管理中的自定义报表分析 ·········································································30 1.4 可视化工具在项目管理中的挑战 ······································································31 第 2章 RBQM数据可视化 ······································································· 33 2.1 概述 ·········································································································34 2.1.1 基于风险的质量管理的基本介绍 ··························································· 34 2.1.2 数据可视化对基于质量的风险管理的作用 ··············································· 35 2.2 风险评估与分类（RAC） ··············································································36 2.2.1 应用场景 ······················································································· 36 2.2.2 数据源 ·························································································· 37 2.2.3 可视化应用 ····················································································· 37 万物可“视”临床研发可视化应用十大场景 2.3 质量风险容忍度（QTL） ·············································································· 39 2.3.1 应用场景 ······················································································· 39 2.3.2 数据源 ·························································································· 39 2.3.3 可视化应用 ····················································································· 39 2.4 关键风险指标（KRI） ·················································································· 40 2.4.1 应用场景 ······················································································· 40 2.4.2 数据源 ·························································································· 40 2.4.3 可视化应用 ····················································································· 40 2.4.4 案例 ····························································································· 42 2.4.5 统计学考量 ····················································································· 43 2.5 中心化统计监查（CSM） ·············································································· 44 2.5.1 应用场景 ······················································································· 44 2.5.2 数据源 ·························································································· 44 2.5.3 可视化应用 ····················································································· 44 2.5.4 案例 ····························································································· 47 2.6 基于风险的质量管理可视化的展望 ··································································· 47 第 3章临床数据管理可视化 ····································································· 49 3.1 临床数据管理工作主要内容介绍 ······································································ 50 3.2 CRF采集数据库设计和测试场景 ····································································· 51 3.2.1 从方案到 Mock CRF流程自动化 ··························································· 51 3.2.2 自动化数据核查计划（DVP）设计 ························································ 52 3.2.3 用户接受测试（UAT）自动化 ······························································ 52 3.2.4 相关数据统计报告 ············································································ 52 3.2.5 自动化工具 ····················································································· 53 3.3 数据录入可视化应用的场景 ············································································ 54 3.3.1 中心层面的数据质量监测 ··································································· 55 3.3.2 受试者层面的数据质量监测 ································································ 57 3.3.3 基于数据质量的动态数据清理 ····························································· 59 3.4 数据审核可视化应用的场景 ············································································ 60 3.4.1 数据逻辑核查·················································································· 60 3.4.2 数据一致性核查··············································································· 64 3.5 数据管理可视化挑战与展望 ············································································ 65 第 4章医学监查可视化 ··········································································· 67 4.1 医学监查的工作职责和目的 ············································································68 4.2 入组审核可视化场景·····················································································68 4.3 医学数据核查可视化场景 ···············································································71 4.3.1 医学重点关注的录入逻辑核查 ····························································· 71 4.3.2 不良事件的医学核查 ········································································ 72 4.3.3 实验室数据的核查 ············································································ 75 4.3.4 禁用药物数据核查 ············································································ 76 4.3.5 合并治疗的核查··············································································· 77 4.4 关键临床终点的可视化监查 ············································································78 4.5 方案偏离审核的可视化··················································································80 第 5章统计分析可视化 ··········································································· 83 5.1 统计分析可视化适用的场景 ············································································84 5.2 临床试验设计的信息处理 ···············································································85 5.2.1 分布的判断及研究参数的拟合 ····························································· 86 5.2.2“优势”的判断·················································································· 88 5.2.3 早期临床试验阶段的研究路径 ····························································· 89 5.2.4 荟萃分析 ······················································································· 90 5.2.5 决策树 ·························································································· 91 5.3 试验设计的要素选择·····················································································92 5.3.1 研究终点及分层因素 ········································································· 92 5.3.2 伴发事件及处理策略 ········································································· 93 5.3.3 样本量计算的可视化决策 ··································································· 95 5.4 试验过程中的数据评估和数据追踪 ··································································95 5.4.1 支持临床运营和医学监查 ··································································· 95 5.4.2 支持（盲态）数据审核会 ··································································· 96 5.5 支持SRC、DMC、CSR的试验数据统计分析图表 ·············································98 第 6章注册申报可视化管理 ····································································101 6.1 可视化在注册项目多项目管理中的应用 ··························································· 102 6.2 可视化在注册项目中多团队协调中的应用························································ 104 万物可“视”临床研发可视化应用十大场景 6.3 可视化在辅助注册策略制定中的应用 ······························································ 105 6.4 可视化在注册外包商管理中的应用 ································································· 109 6.5 如何实现注册信息可视化 ············································································· 110 6.6 注册申报可视化的挑战与展望总结 ··································································111 第 7章临床药理和定量药理可视化 ···························································113 7.1 临床药理在临床试验中的应用 ······································································· 114 7.2 可视化在临床药理相关研究设计中的应用 ························································· 115 7.2.1 单剂剂量递增（SAD）/多剂剂量递增（MAD）研究 ·································115 7.2.2 生物等效性（BE）研究 ····································································116 7.2.3 肝功能不全患者的 PK研究 ································································117 7.3 可视化在临床药理和定量药理的分析方法中的应用 ············································· 118 7.3.1 PK非房室模型分析（Non-Compartment Analysis，NCA） ···························118 7.3.2 群体药代动力学（Population PK，PopPK）分析 ·······································118 7.3.3 E-R分析 ······················································································121 7.4 定量系统药理学 ························································································ 122 第 8章药物经济学可视化 ·······································································125 8.1 什么是药物经济学 ····················································································· 126 8.2 药物经济学的研究内容 ················································································ 126 8.3 药物经济学在医学决策中的应用及其可视化展示 ················································ 126 8.3.1 医药 ····························································································126 8.3.2 医疗 ····························································································131 8.3.3 医保 ····························································································132 第 9章安全风险信号监测可视化 ······························································137 9.1 安全风险信号相关概念 ················································································ 138 9.2 信号的来源 ······························································································ 138 9.3 信号监测流程 ··························································································· 138 9.3.1 信号监测策略制定考量因素 ·······························································140 9.3.2 信号监测频率制定 ···········································································140 9.3.3 重点关注信号 ·················································································140 9.3.4 信号优先级判定可考虑因素 ·······························································141 9.3.5 信号监测执行 ·················································································141 9.4 信号验证流程 ··························································································· 149 第 10章真实世界数据及真实世界研究可视化 ·············································153 10.1 真实世界研究概述 ···················································································· 154 10.1.1

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万物可“视”:临床研发可视化应用十大场景作者简介

李高扬，羚研创新（北京）健康科技创始人，DIA中国数字健康社区负责人。2008年毕业于南京大学生命科学学院，获得硕士学位。从事临床研究15年，超过10年主要聚焦统计分析工作，领导开发了悟空可视化平台，并发表多篇医药研发领域交互式可视化应用的专业文章。主编和参与编写《远程智能临床试验蓝皮书》《远程智能临床试验专家共识》《远程智能临床试验》《中医临床真实世界研究》《智能无人系统产业发展报告》等多部专业著作，参与《中国食品药品监管》“药械监管科学创新技术研究专刊”出版工作。同时，组织临床研发数字化研讨会，协调撰写蓝皮书、共识、文章等，长期协助监管机构起草数字化相关技术指导原则，促进医药研发数字化转型。先后获得DIA数字健康社区（DHC）未来领袖奖（2020年度）和创新领导奖（2024年度）。廖珊妹，美国加州大学戴维斯分校统计博士，拥有近17年药厂生物统计经验。曾任职于BMS美国和辉瑞中国，现为百济神州上市后及真实世界证据统计负责人。曾参与/领导多项新药/生物类似物研发及6项中/美注册申报，负责过几十项Ⅰ期至Ⅳ期临床研究。疾病领域涉及实体瘤血液肿瘤、免疫、神经、病毒、心血管、生物类似物研发等。自2019年担任百济神州上市后统计负责人后，带领团队支持了多项国内外真实世界研究设计、确证性试验监管机构讨论、医保谈判疗效经济模型分析及监管交流、真实世界数据库及PRO分析报告支持上市产品证据链，以及上市后安全数据监测。同时担任中国DIA RWD工作组组长、亦弘商学院统计学讲者、NMPA高研院真实世界数据应用讲者、《中国食品药品监管》杂志特约审稿人等外部职务。李蹊，DIA中国数字健康社区成员，映恩生物数据管理高级总监，具有10年以上临床研究领域从业经验，曾就职于赛诺菲、Parexel、Viedoc、科伦等医药企业，有数据管理、数据编程、EDC建库、数据库系统开发等经验，具有南京大学生物化学和新加坡南洋理工信息技术双硕士学位，发表过5篇SCI论文。

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