Douglas Faries, Xiang Zhang, Zbigniew Kadziola, Uwe Siebert, Felicitas Kuehne, Robert L. Obenchain, Josep Maria Haro
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; and also data from pragmatic trials. This data serves as the basis for the growing use of real-world evidence in medical decision-making. Though; 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 (PDF) 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 comparatively easy and efficient.
The ebook focuses on analytic methods adjusted for time-independent confounding; which are helpful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include:
- algorithms for personalized medicine
- sensitivity analyses for unmeasured confounding
- methods for comparing two interventions as well as comparisons between three or more interventions
- propensity score matching; stratification methods; weighting methods; regression methods; and approaches that combine and average across these methods.
“This ebook is packed full of helpful and insightful information. The table of contents may seem appalling to novice SAS users/healthcare analysts; but given the topics covered; I would recommend a brief run-through at least for anyone new to the field. For more intermediate / advanced users; this book will become a mainstay in your resource list.” — Chris Battiston; Research Data Analyst “Women’s College Hospital”
NOTE: The product includes the ebook; Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS in PDF. No access codes are included.