Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS (Paperback)

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

使用SAS代碼和示例發現現實世界數據研究的最佳實踐

現實世界的醫療數據是常見且越來越廣泛地使用，來源包括觀察性研究、患者登記、電子病歷庫、保險醫療索賠數據庫以及實用試驗的數據。這些數據為現實世界證據在醫學決策中的日益使用提供了基礎。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。《使用SAS進行現實世界醫療數據分析：因果方法和實施》將基於現實世界數據的因果比較效果分析的最佳實踐集結在一個地方，並提供SAS代碼和示例，使分析相對容易和高效。

該書重點介紹了針對時間獨立混淆因素進行調整的分析方法，這在比較不同潛在干預對某個感興趣的結果的影響時非常有用，而沒有隨機分組。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法


• 比較兩種干預措施以及三種或更多干預措施之間的比較方法


• 個性化醫學算法


• 未測量混淆因素的敏感性分析

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.

道格拉斯·法里斯（Douglas Faries）於1990年在俄克拉荷馬州立大學獲得統計學博士學位，同年加入了愛蓮·莉莉（Eli Lilly）公司。在過去的17年中，道格一直專注於實際數據的統計方法學研究，包括因果推斷、比較效果、未測量的混雜因素以及實際數據在個性化醫學中的應用。目前，道格是愛蓮·莉莉公司的高級研究員，領導著實際世界分析能力團隊。他已經撰寫或合著了150多篇同行評審的論文，包括2010年編輯的教科書《使用SAS分析觀察性醫療數據》。他在統計界非常活躍，擔任出版物審查人員、演講者、研討會組織者，並在全國會議上教授因果推斷的短期課程。他自1988年以來一直是SAS用戶。