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

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.

Review

“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.