Book description
This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data.
This book is part of the SAS Press program.
Table of contents
- Preface
- Part 1 Introduction
-
Part 2 Cross-Sectional Selection Bias Adjustment
-
Chapter 2 Propensity Score Stratification and Regression
- Abstract
- 2.1 Introduction
- 2.2 Propensity Score: Definition and Rationale
- 2.3 Estimation of Propensity Scores
- 2.4 Using Propensity Scores to Estimate Treatment Effects: Stratification and Regression Adjustment
- 2.5 Evaluation of Propensity Scores
- 2.6 Limitations and Advantages of Propensity Scores
- 2.7 Example
- 2.8 Summary
- Acknowledgments
- References
-
Chapter 3 Propensity Score Matching for Estimating Treatment Effects
- Abstract
- 3.1 Introduction
- 3.2 Estimating the Propensity Score
- 3.3 Forming Propensity Score Matched Sets
- 3.4 Assessing Balance in Baseline Characteristics
- 3.5 Estimating the Treatment Effect
- 3.6 Sensitivity Analyses for Propensity Score Matching
- 3.7 Propensity Score Matching Compared with Other Propensity Score Methods
- 3.8 Case Study
- 3.9 Summary
- Acknowledgments
- References
- Chapter 4 Doubly Robust Estimation of Treatment Effects
- Chapter 5 Propensity Scoring with Missing Values
-
Chapter 6 Instrumental Variable Method for Addressing Selection Bias
- Abstract
- 6.1 Introduction
- 6.2 Overview of Instrumental Variable Method to Control for Selection Bias
- 6.3 Description of Case Study
- 6.4 Traditional Ordinary Least Squares Regression Method Applied to Case Study
- 6.5 Instrumental Variable Method Applied to Case Study
- 6.6 Using PROC QLIM to Conduct IV Analysis
- 6.7 Comparison to Traditional Regression Adjustment Method
- 6.8 Discussion
- 6.9 Conclusion
- Acknowledgments
- References
- Chapter 7 Local Control Approach Using JMP
-
Chapter 2 Propensity Score Stratification and Regression
- Part 3 Longitudinal Bias Adjustment
- Part 4 Claims Database Research
- Part 5 Pharmacoeconomics
- Part 6 Designing Observational Studies
- Appendix: Asymptotic Distribution of Wilcoxon Rank Sum Test under Hα
- References
- Index
Product information
- Title: Analysis of Observational Health Care Data Using SAS
- Author(s):
- Release date: November 2014
- Publisher(s): SAS Institute
- ISBN: 9781607644248
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