Real World Health Care Data Analysis

Additional Book Details

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

Book Review

"This book is packed full of useful and insightful information. The table of contents may seem daunting 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

Additional resources for this book can be found by accessing the link below.