book

Real World Health Care Data Analysis

Name: Real World Health Care Data Analysis
ISBN: 9781642958003

by Douglas Faries, Xiang Zhang, Zbigniew Kadziola, Uwe Siebert, Felicitas Kuehne, Robert L Obenchain, Josep Maria Haro

January 2020

Beginner to intermediate

436 pages

19h 4m

English

SAS Institute

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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 ContentExample Code and DataAcknowledgmentsWe 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 Research1.4 Types of Real World Studies1.4.1 Cross-sectional Studies1.4.2 Retrospective or Case-control Studies1.4.3 Prospective or Cohort Studies1.5 Questions Addressed by Real World Studies1.6 The Issues: Bias and Confounding1.6.1 Selection Bias1.6.2 Information Bias1.6.3 Confounding1.7 Guidance for Real World Research1.8 Best Practices for Real World Research1.9 Contents of This BookReferences
Chapter 2: Causal Inference and Comparative Effectiveness: A Foundation
2.1 Introduction2.2 Causation2.3 From R.A. Fisher to Modern Causal Inference Analyses2.3.1 Fisher’s Randomized Experiment2.3.2 Neyman’s Potential Outcome Notation2.3.3 Rubin’s Causal Model2.3.4 Pearl’s Causal Model 2.4 Estimands2.5 Totality of Evidence: Replication, Exploratory, and Sensitivity Analyses2.6 SummaryReferences
Chapter 3: Data Examples and Simulations
3.1 Introduction3.2 The REFLECTIONS Study3.3 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 SummaryReferences
Chapter 4: The Propensity Score
4.1 Introduction4.2 Estimate Propensity Score4.2.1 Selection of Covariates4.2.2 Address Missing Covariates Values in Estimating Propensity Score4.2.3 Selection of Propensity Score Estimation Model4.2.4 The Criteria of “Good” Propensity Score Estimate4.3 Example: Estimate Propensity Scores Using the Simulated REFLECTIONS Data4.3.1 A Priori Logistic Model4.3.2 Automatic Logistic Model Selection4.3.3 Boosted CART Model4.4 SummaryReferences
Chapter 5: Before You Analyze – Feasibility Assessment
5.1 Introduction 5.2 Best Practices for Assessing Feasibility: Common Support5.2.1 Walker’s Preference Score and Clinical Equipoise5.2.2 Standardized Differences in Means and Variance Ratios5.2.3 Tipton’s Index5.2.4 Proportion of Near Matches5.2.4 Proportion of Near Matches5.2.5 Trimming the Population5.3 Best Practices for Assessing Feasibility: Assessing Balance5.3.1 The Standardized Difference for Assessing Balance at the Individual Covariate Level5.3.2 The Prognostic Score for Assessing Balance5.4 Example: REFLECTIONS Data5.4.1 Feasibility Assessment Using the Reflections Data5.4.2 Balance Assessment Using the Reflections Data5.5 SummaryReferences
Chapter 6: Matching Methods for Estimating Causal Treatment Effects
6.1 Introduction6.2 Distance Metrics6.2.1 Exact Distance Measure6.2.2 Mahalanobis Distance Measure6.2.3 Propensity Score Distance Measure6.2.4 Linear Propensity Score Distance Measure6.2.5 Some Considerations in Choosing Distance Measures6.3 Matching Constraints6.3.1 Calipers6.3.2 Matching With and Without Replacement6.3.3 Fixed Ratio Versus Variable Ratio Matching6.4 Matching Algorithms6.4.1 Nearest Neighbor Matching6.4.2 Optimal Matching6.4.3 Variable Ratio Matching6.4.4 Full Matching6.4.5 Discussion: Selecting the Matching Constraints and Algorithm6.5 Example: Matching Methods Applied to the Simulated REFLECTIONS Data6.5.1 Data Description6.5.2 Computation of Different Matching Methods6.5.3 1:1 Nearest Neighbor Matching6.5.4 1:1 Optimal Matching with Additional Exact Matching6.5.5 1:1 Mahalanobis Distance Matching with Caliper6.5.6 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 SummaryReferences
Chapter 7: Stratification for Estimating Causal Treatment Effects
7.1 Introduction7.2 Propensity Score Stratification7.2.1 Forming Propensity Score Strata7.2.2 Estimation of Treatment Effects7.3 Local Control7.3.1 Choice of Clustering Method and Optimal Number of Clusters7.3.2 Confirming that the Estimated Local Effect-Size Distribution Is Not Ignorable7.4 Stratified Analysis of the PCI15K Data 7.4.1 Propensity Score Stratified Analysis7.4.2 Local Control Analysis7.5 SummaryReferences

Chapter 8: Inverse Weighting and Balancing Algorithms for Estimating Causal Treatment Effects
8.1 Introduction8.2 Inverse Probability of Treatment Weighting8.3 Overlap Weighting8.4 Balancing Algorithms8.5 Example of Weighting Analyses Using the REFLECTIONS Data8.5.1 IPTW Analysis Using PROC CAUSALTRT8.4.2 Overlap Weighted Analysis using PROC GENMOD8.4.3 Entropy Balancing Analysis8.5 SummaryReferences
Chapter 9: Putting It All Together: Model Averaging
9.1 Introduction9.2 Model Averaging for Comparative Effectiveness9.2.1 Selection of Individual Methods 9.2.2 Computing Model Averaging Weights9.2.3 The Model Averaging Estimator and Inferences9.3 Frequentist Model Averaging Example Using the Simulated REFLECTIONS Data9.3.1 Setup: Selection of Analytical Methods9.3.2 SAS Code9.3.3 Analysis Results9.4 SummaryReferences
Chapter 10: Generalized Propensity Score Analyses (> 2 Treatments)
10.1 Introduction10.2 The Generalized Propensity Score10.2.1 Definition, Notation, and Assumptions10.2.2 Estimating the Generalized Propensity Score10.3 Feasibility and Balance Assessment Using the Generalized Propensity Score10.3.1 Extensions of Feasibility and Trimming10.3.2 Balance Assessment10.4 Estimating Treatment Effects Using the Generalized Propensity Score10.4.1 GPS Matching10.4.2 Inverse Probability Weighting10.4.3 Vector Matching10.5 SAS Programs for Multi-Cohort Analyses10.6 Three Treatment Group Analyses Using the Simulated REFLECTIONS Data10.6.1 Data Overview and Trimming10.6.2 The Generalized Propensity Score and Population Trimming10.6.3 Balance Assessment10.6.4 Generalized Propensity Score Matching Analysis10.6.5 Inverse Probability Weighting Analysis10.6.6 Vector Matching Analysis 10.7 SummaryReferences
Chapter 11: Marginal Structural Models with Inverse Probability Weighting
11.1 Introduction11.2 Marginal Structural Models with Inverse Probability of Treatment Weighting11.3 Example: MSM Analysis of the Simulated REFLECTIONS Data 11.3.1 Study Description11.3.2 Data Overview11.3.3 Causal Graph11.3.4 Computation of Weights11.3.5 Analysis of Causal Treatment Effects Using a Marginal Structural Model11.4 SummaryReferences
Chapter 12: A Target Trial Approach with Dynamic Treatment Regimes and Replicates Analyses
12.1 Introduction12.2 Dynamic Treatment Regimes and Target Trial Emulation12.2.1 Dynamic Treatment Regimes12.2.2 Target Trial Emulation12.3 Example: Target Trial Approach Applied to the Simulated REFLECTIONS Data 12.3.1 Study Question12.3.2 Study Description and Data Overview 12.3.3 Target Trial Study Protocol12.3.4 Generating New Data 12.3.5 Creating Weights12.3.6 Base-Case Analysis12.3.7 Selecting the Optimal Strategy12.3.8 Sensitivity Analyses12.4 SummaryReferences
Chapter 13: Evaluating the Impact of Unmeasured Confounding in Observational Research
13.1 Introduction13.2 The Toolbox: A Summary of Available Analytical Methods 13.3 The Best Practice Recommendation13.4 Example Data Analysis Using the REFLECTIONS Study13.4.1 Array Approach13.4.2 Propensity Score Calibration13.4.3 Rosenbaum-Rubin Sensitivity Analysis13.4.4 Negative Control 13.4.5 Bayesian Twin Regression Modeling13.5 SummaryReferences
Chapter 14: Using Real World Data to Examine the Generalizability of Randomized Trials
14.1 External Validity, Generalizability and Transportability14.2 Methods to Increase Generalizability14.3 Generalizability Re-weighting Methods for Generalizability14.3.1 Inverse Probability Weighting14.3.2 Entropy Balancing14.3.3 Assumptions, Best Practices, and Limitations 14.4 Programs Used in Generalizability Analyses14.5 Analysis of Generalizability Using the PCI15K Data14.5.1 RCT and Target Populations14.5.2 Inverse Probability Generalizability14.5.3 Entropy Balancing Generalizability14.6 SummaryReferences
Chapter 15: Personalized Medicine, Machine Learning, and Real World Data
15.1 Introduction15.2 Individualized Treatment Recommendation 15.2.1 The Individualized Treatment Recommendation Framework15.2.2 Estimating the Optimal Individualized Treatment Rule15.2.3 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 Computations15.5.2 Background Information: LTD Distributions from the PCI15K Local Control Analysis15.5.3 Most Like Me Example Using the PCI15K Data Set15.5.4 Extensions and Interpretations of Most Like Me Displays15.6 SummaryReferences
Index
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Content preview from Real World Health Care Data Analysis

About the Authors

Douglas Faries graduated from Oklahoma State University with a PhD in Statistics in 1990 and joined Eli Lilly and Company later that year. Over the past 17 years, Doug has focused his research interests on statistical methodology for real world data including causal inference, comparative effectiveness, unmeasured confounding, and the use of real world data for personalized medicine. Currently, Doug is a Sr. Research Fellow at Eli Lilly, leading the Real-World Analytics Capabilities team. He has authored or co-authored over 150 peer-reviewed manuscripts including editing the textbook Analysis of Observational Healthcare Data Using SAS® in 2010. He is active in the statistical community as a publication reviewer, speaker, workshop ...

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Real World Health Care Data Analysis

by Douglas Faries, Xiang Zhang, Zbigniew Kadziola, Uwe Siebert, Felicitas Kuehne, Robert L Obenchain, Josep Maria Haro

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.