Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS (Paperback)
暫譯: 實際醫療數據分析：使用SAS的因果方法與實作

Faries, Douglas, Zhang, Xiang, Kadziola, Zbigniew

Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS (Paperback)
  • 出版商: SAS Institute
  • 出版日期: 2020-01-15
  • 售價: $3,150
  • 貴賓價: 9.5 折 $2,993
  • 語言: 英文
  • 頁數: 436
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1642957984
  • ISBN-13: 9781642957983
  • 相關分類: Data Science

    • 立即出貨 (庫存=1)

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商品描述

Discover best practices for real world data research with SAS code and examples

Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient.

The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include:

 

  • propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods
  • methods for comparing two interventions as well as comparisons between three or more interventions
  • algorithms for personalized medicine
  • sensitivity analyses for unmeasured confounding

商品描述(中文翻譯)

探索使用 SAS 代碼和範例的實務最佳做法以進行現實世界數據研究

現實世界的醫療保健數據普遍存在且使用日益增長，來源包括觀察性研究、病人登記、電子病歷數據庫、保險醫療索賠數據庫，以及來自實用試驗的數據。這些數據作為在醫療決策中日益增長的現實世界證據的基礎。然而，數據本身並不是證據。必須使用分析方法將現實世界數據轉化為有效且有意義的證據。《現實世界醫療保健數據分析：使用 SAS 的因果方法與實施》將基於現實世界數據的因果比較效果分析的最佳實務集中於一處，並提供 SAS 代碼和範例，使分析相對簡單且高效。

本書專注於調整時間獨立混淆的分析方法，這些方法在比較不同潛在干預對某些感興趣結果的影響時非常有用，尤其是在沒有隨機化的情況下。這些方法包括：

- 傾向得分匹配、分層方法、加權方法、回歸方法，以及結合和平均這些方法的方式
- 比較兩種干預的方法，以及三種或更多干預之間的比較
- 個性化醫療的算法
- 對未測量混淆的敏感性分析

作者簡介

Douglas Faries graduated from Oklahoma State University with a PhD in Statistics in 1990 and joined Eli Lilly and Company later that year. Over the past 17 years, Doug has focused his research interests on statistical methodology for real world data including causal inference, comparative effectiveness, unmeasured confounding, and the use of real world data for personalized medicine. Currently, Doug is a Sr. Research Fellow at Eli Lilly, leading the Real-World Analytics Capabilities team. He has authored or co-authored over 150 peer-reviewed manuscripts including editing the textbook Analysis of Observational Healthcare Data Using SAS in 2010. He is active in the statistical community as a publication reviewer, speaker, workshop organizer, and teaches short courses in causal inference at national meetings. He has been a SAS user since 1988.

作者簡介(中文翻譯)

道格拉斯·法里斯（Douglas Faries）於1990年從俄克拉荷馬州立大學獲得統計學博士學位，並在同年加入艾利藥品公司（Eli Lilly and Company）。在過去的17年中，道格專注於現實世界數據的統計方法研究，包括因果推斷、比較效果、未測量的混雜因素，以及使用現實世界數據進行個人化醫療。目前，道格是艾利藥品公司的高級研究員，負責現實世界分析能力團隊。他已發表或共同發表超過150篇經過同行評審的手稿，包括在2010年編輯的教科書《使用SAS分析觀察性醫療數據》。他在統計界活躍，擔任出版物審稿人、演講者、工作坊組織者，並在全國會議上教授因果推斷的短期課程。自1988年以來，他一直是SAS的使用者。

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