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

825 Pages·2020·9.742 MB·English
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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) All Rights Reserved. Produced in the United States of America. For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated. U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 227.7202-4, and, to the extent required under U.S. federal law, the minimum restricted rights as set out in FAR 52.227-19 (DEC 2007). If FAR 52.227-19 is applicable, this provision serves as notice under clause (c) thereof and no other notice is required to be affixed to the Software or documentation. The Government’s rights in Software and documentation shall be only those set forth in this Agreement. SAS Institute Inc., SAS Campus Drive, Cary, NC 27513-2414 January 2020 SAS® and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. SAS software may be provided with certain third-party software, including but not limited to open-source software, which is licensed under its applicable third-party software license agreement. For license information about third-party software distributed with SAS software, refer to http://support.sas.com/thirdpartylicenses. 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 A B C D E F G H I K L M N O P Q R S T U V W X Y Z

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