Real World Health Care Data Analysis. Uwe Siebert

Real World Health Care Data Analysis - Uwe Siebert


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      The correct bibliographic citation for this manual is as follows: Faries, Douglas, Xiang Zhang, Zbigniew Kadziola, Uwe Siebert, Felicitas Kuehne, Robert L. Obenchain, and Josep Maria Haro. 2020. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS®. Cary, NC: SAS Institute Inc.

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

      Copyright © 2020, SAS Institute Inc., Cary, NC, USA

      ISBN 978-1-64295-802-7 (Hard cover)

      ISBN 978-1-64295-798-3 (Paperback)

      ISBN 978-1-64295-799-0 (PDF)

      ISBN 978-1-64295-800-3 (epub)

      ISBN 978-1-64295-801-0 (kindle)

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      Contents

       Contents

       About the Book

       What Does This Book Cover?

       Is This Book for You?

       What Should You Know about the Examples?

       Software Used to Develop the Book’s Content

       Example Code and Data

       Acknowledgments

       We Want to Hear from You

       About the Authors

       Chapter 1: Introduction to Observational and Real World Evidence Research

       1.1 Why This Book?

       1.2 Definition and Types of Real World Data (RWD)

       1.3 Experimental Versus Observational Research

       1.4 Types of Real World Studies

       1.4.1 Cross-sectional Studies

       1.4.2 Retrospective or Case-control Studies

       1.4.3 Prospective or Cohort Studies

       1.5 Questions Addressed by Real World Studies

       1.6 The Issues: Bias and Confounding

       1.6.1 Selection Bias

       1.6.2 Information Bias

       1.6.3 Confounding

       1.7 Guidance for Real World Research

       1.8 Best Practices for Real World Research

       1.9 Contents of This Book

       References

       Chapter 2: Causal Inference and Comparative Effectiveness: A Foundation

       2.1 Introduction

       2.2 Causation

       2.3 From R.A. Fisher to Modern Causal Inference Analyses

       2.3.1 Fisher’s Randomized Experiment

      


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