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


Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS®: Causal Methods and Implementation Using SAS®
By 作者: Douglas Faries - Xiang Zhang - Zbigniew Kadziola - Uwe Siebert - Felicitas Kuehne - Robert Obenchain - Josep Maria Haro
ISBN-10 书号: 1642957984
ISBN-13 书号: 9781642957983
Release Finelybook 出版日期: 2019-01-15
pages 页数: (436 )

Book Description to Finelybook sorting

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

Contents


About the Book
About the Authors
Chapter 1: Introduction to Observational and Real World Evidence Research
Chapter 2: Causal Inference and Comparative Effectiveness:A Foundation
Chapter 3: Data Examples and Simulations
Chapter 4: The Propensity Score
Chapter 5: Before You Analyze-Feasibility Assessment
Chapter 6. Matching Methods for Estimating Causal Treatment Effects
Chapter 7: Stratification for Estimating Causal Treatment Effects
Chapter 8: Inverse Weighting and Balancing Algorithms for Estimating Causal Treatment Effects
Chapter 9. Putting It All Together: Model Averaging
Chapter 10: Generalized Propensity Score Analyses(>2 Treatments)
Chapter 11: Marginal Structural Models with Inverse Probability Weighting
Chapter 12:A Target Trial Approach with Dynamic Treatment Regimes and Replicates Analyses
Chapter 13: Evaluating the Impact of Unmeasured Confounding in Observational Research
Chapter 14: Using Real World Data to Examine the Generalizability of Randomized Trials
Chapter 15: Personalized Medicine, Machine Learning, and Real World Data
Index

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