万物可“视”:临床研发可视化应用十大场景
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- ISBN:9787523512265
- 装帧:暂无
- 册数:暂无
- 重量:暂无
- 开本:29cm
- 页数:183页
- 出版时间:2024-05-01
- 条形码:9787523512265 ; 978-7-5235-1226-5
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。
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。
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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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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