Table Of ContentThe 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)
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ISBN 978-1-64295-799-0 (PDF)
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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
2.3.2 Neyman’s Potential Outcome Notation
2.3.3 Rubin’s Causal Model
2.3.4 Pearl’s Causal Model
2.4 Estimands
2.5 Totality of Evidence: Replication, Exploratory, and Sensitivity
Analyses
2.6 Summary
References
Chapter 3: Data Examples and Simulations
3.1 Introduction
3.2 The REFLECTIONS Study
3.3 The Lindner Study
3.4 Simulations
3.5 Analysis Data Set Examples
3.5.1 Simulated REFLECTIONS Data
3.5.2 Simulated PCI Data
3.6 Summary
References
Chapter 4: The Propensity Score
4.1 Introduction
4.2 Estimate Propensity Score
4.2.1 Selection of Covariates
4.2.2 Address Missing Covariates Values in Estimating Propensity Score
4.2.3 Selection of Propensity Score Estimation Model
4.2.4 The Criteria of “Good” Propensity Score Estimate
4.3 Example: Estimate Propensity Scores Using the Simulated
REFLECTIONS Data
4.3.1 A Priori Logistic Model
4.3.2 Automatic Logistic Model Selection
4.3.3 Boosted CART Model
4.4 Summary
References
Chapter 5: Before You Analyze – Feasibility Assessment
5.1 Introduction
5.2 Best Practices for Assessing Feasibility: Common Support
5.2.1 Walker’s Preference Score and Clinical Equipoise
5.2.2 Standardized Differences in Means and Variance Ratios
5.2.3 Tipton’s Index
5.2.4 Proportion of Near Matches
5.2.4 Proportion of Near Matches
5.2.5 Trimming the Population
5.3 Best Practices for Assessing Feasibility: Assessing Balance
5.3.1 The Standardized Difference for Assessing Balance at the
Individual Covariate Level
5.3.2 The Prognostic Score for Assessing Balance
5.4 Example: REFLECTIONS Data
5.4.1 Feasibility Assessment Using the Reflections Data
5.4.2 Balance Assessment Using the Reflections Data
5.5 Summary
References
Chapter 6: Matching Methods for Estimating Causal Treatment
Effects
6.1 Introduction
6.2 Distance Metrics
6.2.1 Exact Distance Measure
6.2.2 Mahalanobis Distance Measure
6.2.3 Propensity Score Distance Measure
6.2.4 Linear Propensity Score Distance Measure
6.2.5 Some Considerations in Choosing Distance Measures
6.3 Matching Constraints
6.3.1 Calipers
6.3.2 Matching With and Without Replacement
6.3.3 Fixed Ratio Versus Variable Ratio Matching
6.4 Matching Algorithms
6.4.1 Nearest Neighbor Matching
6.4.2 Optimal Matching
6.4.3 Variable Ratio Matching
6.4.4 Full Matching
6.4.5 Discussion: Selecting the Matching Constraints and Algorithm
6.5 Example: Matching Methods Applied to the Simulated REFLECTIONS
Data
6.5.1 Data Description
6.5.2 Computation of Different Matching Methods
6.5.3 1:1 Nearest Neighbor Matching
6.5.4 1:1 Optimal Matching with Additional Exact Matching
6.5.5 1:1 Mahalanobis Distance Matching with Caliper
6.5.6 Variable Ratio Matching
6.5.7 Full Matching
6.6 Discussion Topics: Analysis on Matched Samples, Variance Estimation
of the Causal Treatment Effect, and Incomplete Matching
6.7 Summary
References
Chapter 7: Stratification for Estimating Causal Treatment Effects
7.1 Introduction
7.2 Propensity Score Stratification
7.2.1 Forming Propensity Score Strata
7.2.2 Estimation of Treatment Effects
7.3 Local Control
7.3.1 Choice of Clustering Method and Optimal Number of Clusters
7.3.2 Confirming that the Estimated Local Effect-Size Distribution Is Not
Ignorable
7.4 Stratified Analysis of the PCI15K Data
7.4.1 Propensity Score Stratified Analysis
7.4.2 Local Control Analysis
7.5 Summary
References
Chapter 8: Inverse Weighting and Balancing Algorithms for
Estimating Causal Treatment Effects
8.1 Introduction
8.2 Inverse Probability of Treatment Weighting
8.3 Overlap Weighting
8.4 Balancing Algorithms
8.5 Example of Weighting Analyses Using the REFLECTIONS Data
8.5.1 IPTW Analysis Using PROC CAUSALTRT
8.4.2 Overlap Weighted Analysis using PROC GENMOD
8.4.3 Entropy Balancing Analysis
8.5 Summary
References
Chapter 9: Putting It All Together: Model Averaging
9.1 Introduction
9.2 Model Averaging for Comparative Effectiveness
9.2.1 Selection of Individual Methods
9.2.2 Computing Model Averaging Weights
9.2.3 The Model Averaging Estimator and Inferences
9.3 Frequentist Model Averaging Example Using the Simulated
REFLECTIONS Data
9.3.1 Setup: Selection of Analytical Methods
9.3.2 SAS Code
9.3.3 Analysis Results
9.4 Summary
References
Chapter 10: Generalized Propensity Score Analyses (> 2
Treatments)
10.1 Introduction
10.2 The Generalized Propensity Score
10.2.1 Definition, Notation, and Assumptions
10.2.2 Estimating the Generalized Propensity Score
10.3 Feasibility and Balance Assessment Using the Generalized
Propensity Score
10.3.1 Extensions of Feasibility and Trimming
10.3.2 Balance Assessment
10.4 Estimating Treatment Effects Using the Generalized Propensity
Score
10.4.1 GPS Matching
10.4.2 Inverse Probability Weighting
10.4.3 Vector Matching
10.5 SAS Programs for Multi-Cohort Analyses
10.6 Three Treatment Group Analyses Using the Simulated REFLECTIONS
Data
10.6.1 Data Overview and Trimming
10.6.2 The Generalized Propensity Score and Population Trimming
10.6.3 Balance Assessment
10.6.4 Generalized Propensity Score Matching Analysis
10.6.5 Inverse Probability Weighting Analysis
10.6.6 Vector Matching Analysis
10.7 Summary
References
Chapter 11: Marginal Structural Models with Inverse Probability
Weighting
11.1 Introduction
11.2 Marginal Structural Models with Inverse Probability of Treatment
Weighting
11.3 Example: MSM Analysis of the Simulated REFLECTIONS Data
11.3.1 Study Description
11.3.2 Data Overview
11.3.3 Causal Graph
11.3.4 Computation of Weights
11.3.5 Analysis of Causal Treatment Effects Using a Marginal Structural
Model
11.4 Summary
References
Chapter 12: A Target Trial Approach with Dynamic Treatment
Regimes and Replicates Analyses
12.1 Introduction
12.2 Dynamic Treatment Regimes and Target Trial Emulation
12.2.1 Dynamic Treatment Regimes
12.2.2 Target Trial Emulation
12.3 Example: Target Trial Approach Applied to the Simulated
REFLECTIONS Data
12.3.1 Study Question
12.3.2 Study Description and Data Overview
12.3.3 Target Trial Study Protocol
12.3.4 Generating New Data
12.3.5 Creating Weights
12.3.6 Base-Case Analysis
12.3.7 Selecting the Optimal Strategy
12.3.8 Sensitivity Analyses
12.4 Summary
References
Chapter 13: Evaluating the Impact of Unmeasured Confounding in
Observational Research
13.1 Introduction
13.2 The Toolbox: A Summary of Available Analytical Methods
13.3 The Best Practice Recommendation
13.4 Example Data Analysis Using the REFLECTIONS Study
13.4.1 Array Approach
13.4.2 Propensity Score Calibration
13.4.3 Rosenbaum-Rubin Sensitivity Analysis
13.4.4 Negative Control
13.4.5 Bayesian Twin Regression Modeling
13.5 Summary
References
Chapter 14: Using Real World Data to Examine the
Generalizability of Randomized Trials
14.1 External Validity, Generalizability and Transportability
14.2 Methods to Increase Generalizability
14.3 Generalizability Re-weighting Methods for Generalizability
14.3.1 Inverse Probability Weighting
14.3.2 Entropy Balancing
14.3.3 Assumptions, Best Practices, and Limitations
14.4 Programs Used in Generalizability Analyses
14.5 Analysis of Generalizability Using the PCI15K Data
14.5.1 RCT and Target Populations
14.5.2 Inverse Probability Generalizability
14.5.3 Entropy Balancing Generalizability
14.6 Summary
References
Chapter 15: Personalized Medicine, Machine Learning, and Real
World Data
15.1 Introduction
15.2 Individualized Treatment Recommendation
15.2.1 The Individualized Treatment Recommendation Framework
15.2.2 Estimating the Optimal Individualized Treatment Rule
15.2.3 Multi-Category ITR
15.3 Programs for ITR
15.4 Example Using the Simulated REFLECTIONS Data
15.5 “Most Like Me” Displays: A Graphical Approach
15.5.1 Most Like Me Computations
15.5.2 Background Information: LTD Distributions from the PCI15K Local
Control Analysis
15.5.3 Most Like Me Example Using the PCI15K Data Set
15.5.4 Extensions and Interpretations of Most Like Me Displays
15.6 Summary
References
Index
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