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萬物可“視”:臨床研發可視化應用十大場景 版權信息
- ISBN：9787523512265
- 條形碼：9787523512265 ; 978-7-5235-1226-5
- 裝幀：暫無
- 冊數：暫無
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萬物可“視”:臨床研發可視化應用十大場景 本書特色
近年來臨床試驗數據量呈指數級增加，臨床相關人員在數據審閱和分析過程中遇到了新的挑戰。一方面要求將分析結果精準地傳達給決策者以輔助試驗的策略制定；另一方面要求加速有效信息獲取來幫助改善患者的治療效果。使用合理的可視化工具，同時基于豐富的可視化模型進行數據審核和分析，能 輕松地揭示數據關系，實時顯示數據狀態， 容易呈現出數據模式和數據趨勢。為了 好地提高數據的利用率，本書從臨床上的十大場景出發，對數據可視化的技術進行分析、探討和實踐分享，從可視化需求、技術現狀及可及性、可視化的應用場景、通用原則等維度進行理論和實踐案例的闡述和分享。
萬物可“視”:臨床研發可視化應用十大場景 內容簡介
近年來臨床試驗數據量呈指數級增加，臨床相關人員在數據審閱和分析過程中遇到了新的挑戰。一方面要求將分析結果精準地傳達給決策者以輔助試驗的策略制定；另一方面要求加速有效信息獲取來幫助改善患者的治療效果。使用合理的可視化工具，同時基于豐富的可視化模型進行數據審核和分析，能更輕松地揭示數據關系，實時顯示數據狀態，更容易呈現出數據模式和數據趨勢。 為了更好地提高數據的利用率，本書從臨床上的十大場景出發，對數據可視化的技術進行分析、探討和實踐分享，從可視化需求、技術現狀及可及性、可視化的應用場景、通用原則等維度進行理論和實踐案例的闡述和分享。
萬物可“視”:臨床研發可視化應用十大場景 目錄
第 1章 臨床運營可視化 ·············································································1
1.1 項目管理的工作職責和內容 ············································································· 2
1.1.1 項目管理的定義·················································································2
1.1.2 項目管理的工作內容 ··········································································2
1.2 項目管理可視化應用的場景 ············································································· 3
1.2.1 項目進度管理····················································································3
1.2.2 項目成本管理·················································································· 11
1.2.3 質量管理 ······················································································· 17
1.2.4 人員管理 ······················································································· 21
1.2.5 項目文檔管理·················································································· 24
1.2.6 項目中的監查和遠程智能監查的可視化管理 ············································ 26
1.3 項目管理中的自定義報表分析 ·········································································30
1.4 可視化工具在項目管理中的挑戰 ······································································31
第 2章 RBQM數據可視化 ······································································· 33
2.1 概述 ·········································································································34
2.1.1 基于風險的質量管理的基本介紹 ··························································· 34
2.1.2 數據可視化對基于質量的風險管理的作用 ··············································· 35
2.2 風險評估與分類（RAC） ··············································································36
2.2.1 應用場景 ······················································································· 36
2.2.2 數據源 ·························································································· 37
2.2.3 可視化應用 ····················································································· 37
萬物可“視”臨床研發可視化應用十大場景
2.3 質量風險容忍度（QTL） ·············································································· 39
2.3.1 應用場景 ······················································································· 39
2.3.2 數據源 ·························································································· 39
2.3.3 可視化應用 ····················································································· 39
2.4 關鍵風險指標（KRI） ·················································································· 40
2.4.1 應用場景 ······················································································· 40
2.4.2 數據源 ·························································································· 40
2.4.3 可視化應用 ····················································································· 40
2.4.4 案例 ····························································································· 42
2.4.5 統計學考量 ····················································································· 43
2.5 中心化統計監查（CSM） ·············································································· 44
2.5.1 應用場景 ······················································································· 44
2.5.2 數據源 ·························································································· 44
2.5.3 可視化應用 ····················································································· 44
2.5.4 案例 ····························································································· 47
2.6 基于風險的質量管理可視化的展望 ··································································· 47
第 3章 臨床數據管理可視化 ····································································· 49
3.1 臨床數據管理工作主要內容介紹 ······································································ 50
3.2 CRF采集數據庫設計和測試場景 ····································································· 51
3.2.1 從方案到 Mock CRF流程自動化 ··························································· 51
3.2.2 自動化數據核查計劃（DVP）設計 ························································ 52
3.2.3 用戶接受測試（UAT）自動化 ······························································ 52
3.2.4 相關數據統計報告 ············································································ 52
3.2.5 自動化工具 ····················································································· 53
3.3 數據錄入可視化應用的場景 ············································································ 54
3.3.1 中心層面的數據質量監測 ··································································· 55
3.3.2 受試者層面的數據質量監測 ································································ 57
3.3.3 基于數據質量的動態數據清理 ····························································· 59
3.4 數據審核可視化應用的場景 ············································································ 60
3.4.1 數據邏輯核查·················································································· 60
3.4.2 數據一致性核查··············································································· 64
3.5 數據管理可視化挑戰與展望 ············································································ 65
第 4章 醫學監查可視化 ··········································································· 67
4.1 醫學監查的工作職責和目的 ············································································68
4.2 入組審核可視化場景·····················································································68
4.3 醫學數據核查可視化場景 ···············································································71
4.3.1 醫學重點關注的錄入邏輯核查 ····························································· 71
4.3.2 不良事件的醫學核查 ········································································ 72
4.3.3 實驗室數據的核查 ············································································ 75
4.3.4 禁用藥物數據核查 ············································································ 76
4.3.5 合并治療的核查··············································································· 77
4.4 關鍵臨床終點的可視化監查 ············································································78
4.5 方案偏離審核的可視化··················································································80
第 5章 統計分析可視化 ··········································································· 83
5.1 統計分析可視化適用的場景 ············································································84
5.2 臨床試驗設計的信息處理 ···············································································85
5.2.1 分布的判斷及研究參數的擬合 ····························································· 86
5.2.2“優勢”的判斷·················································································· 88
5.2.3 早期臨床試驗階段的研究路徑 ····························································· 89
5.2.4 薈萃分析 ······················································································· 90
5.2.5 決策樹 ·························································································· 91
5.3 試驗設計的要素選擇·····················································································92
5.3.1 研究終點及分層因素 ········································································· 92
5.3.2 伴發事件及處理策略 ········································································· 93
5.3.3 樣本量計算的可視化決策 ··································································· 95
5.4 試驗過程中的數據評估和數據追蹤 ··································································95
5.4.1 支持臨床運營和醫學監查 ··································································· 95
5.4.2 支持（盲態）數據審核會 ··································································· 96
5.5 支持SRC、DMC、CSR的試驗數據統計分析圖表 ·············································98
第 6章 注冊申報可視化管理 ····································································101
6.1 可視化在注冊項目多項目管理中的應用 ··························································· 102
6.2 可視化在注冊項目中多團隊協調中的應用························································ 104
萬物可“視”臨床研發可視化應用十大場景
6.3 可視化在輔助注冊策略制定中的應用 ······························································ 105
6.4 可視化在注冊外包商管理中的應用 ································································· 109
6.5 如何實現注冊信息可視化 ············································································· 110
6.6 注冊申報可視化的挑戰與展望總結 ··································································111
第 7章 臨床藥理和定量藥理可視化 ···························································113
7.1 臨床藥理在臨床試驗中的應用 ······································································· 114
7.2 可視化在臨床藥理相關研究設計中的應用 ························································· 115
7.2.1 單劑劑量遞增（SAD）/多劑劑量遞增（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 可視化在臨床藥理和定量藥理的分析方法中的應用 ············································· 118
7.3.1 PK非房室模型分析（Non-Compartment Analysis，NCA） ···························118
7.3.2 群體藥代動力學（Population PK，PopPK）分析 ·······································118
7.3.3 E-R分析 ······················································································121
7.4 定量系統藥理學 ························································································ 122
第 8章 藥物經濟學可視化 ·······································································125
8.1 什么是藥物經濟學 ····················································································· 126
8.2 藥物經濟學的研究內容 ················································································ 126
8.3 藥物經濟學在醫學決策中的應用及其可視化展示 ················································ 126
8.3.1 醫藥 ····························································································126
8.3.2 醫療 ····························································································131
8.3.3 醫保 ····························································································132
第 9章 安全風險信號監測可視化 ······························································137
9.1 安全風險信號相關概念 ················································································ 138
9.2 信號的來源 ······························································································ 138
9.3 信號監測流程 ··························································································· 138
9.3.1 信號監測策略制定考量因素 ·······························································140
9.3.2 信號監測頻率制定 ···········································································140
9.3.3 重點關注信號 ·················································································140
9.3.4 信號優先級判定可考慮因素 ·······························································141
9.3.5 信號監測執行 ·················································································141
9.4 信號驗證流程 ··························································································· 149
第 10章 真實世界數據及真實世界研究可視化 ·············································153
10.1 真實世界研究概述 ···················································································· 154
10.1.1
展開全部
萬物可“視”:臨床研發可視化應用十大場景 作者簡介
李高揚，羚研創新（北京）健康科技創始人，DIA中國數字健康社區負責人。2008年畢業于南京大學生命科學學院，獲得碩士學位。從事臨床研究15年，超過10年主要聚焦統計分析工作，領導開發了悟空可視化平臺，并發表多篇醫藥研發領域交互式可視化應用的專業文章。主編和參與編寫《遠程智能臨床試驗藍皮書》《遠程智能臨床試驗專家共識》《遠程智能臨床試驗》《中醫臨床真實世界研究》《智能無人系統產業發展報告》等多部專業著作，參與《中國食品藥品監管》“藥械監管科學創新技術研究專刊”出版工作。同時，組織臨床研發數字化研討會，協調撰寫藍皮書、共識、文章等，長期協助監管機構起草數字化相關技術指導原則，促進醫藥研發數字化轉型。先后獲得DIA數字健康社區（DHC）未來領袖獎（2020年度）和創新領導獎（2024年度）。 廖珊妹，美國加州大學戴維斯分校統計博士，擁有近17年藥廠生物統計經驗。曾任職于BMS美國和輝瑞中國，現為百濟神州上市后及真實世界證據統計負責人。曾參與/領導多項新藥/生物類似物研發及6項中/美注冊申報，負責過幾十項Ⅰ期至Ⅳ期臨床研究。疾病領域涉及實體瘤血液腫瘤、免疫、神經、病毒、心血管、生物類似物研發等。自2019年擔任百濟神州上市后統計負責人后，帶領團隊支持了多項國內外真實世界研究設計、確證性試驗監管機構討論、醫保談判療效經濟模型分析及監管交流、真實世界數據庫及PRO分析報告支持上市產品證據鏈，以及上市后安全數據監測。同時擔任中國DIA RWD工作組組長、亦弘商學院統計學講者、NMPA高研院真實世界數據應用講者、《中國食品藥品監管》雜志特約審稿人等外部職務。 李蹊，DIA中國數字健康社區成員，映恩生物數據管理高級總監，具有10年以上臨床研究領域從業經驗，曾就職于賽諾菲、Parexel、Viedoc、科倫等醫藥企業，有數據管理、數據編程、EDC建庫、數據庫系統開發等經驗，具有南京大學生物化學和新加坡南洋理工信息技術雙碩士學位，發表過5篇SCI論文。
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