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Analysis of Observational Health Care Data Using SAS
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Analysis of Observational Health Care Data Using SAS
by Andrew C. Leon, Josep Maria Haro, Robert Obenchain, Douglas E. Faries
Analysis of Observational Health Care Data Using SAS
Preface
Part 1 Introduction
Chapter 1 Introduction to Observational Studies
1.1 Observational vs. Experimental Studies
1.2 Issues in Observational Studies
1.3 Study Design
1.4 Methods
1.5 Some Guidelines for Reporting
Acknowledgments
References
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
Abstract
4.1 Introduction
4.2 Implemention with the DR Macro
4.3 Sample Analysis
4.4 Summary
4.5 Conclusion
References
Chapter 5 Propensity Scoring with Missing Values
Abstract
5.1 Introduction
5.2 Data Example
5.3 Using SAS for IPW Estimation with Missing Values
5.4 Sensitivity Analyses
5.5 Discussion
References
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
Abstract
7.1 Introduction
7.2 Some Traditional Analyses of Hypothetical Patient Registry Data
7.3 The Four Phases of a Local Control Analysis
7.4 Conclusion
Acknowledgments
Appendix: Propensity Scores and Blocking/Balancing Scores
References
Part 3 Longitudinal Bias Adjustment
Chapter 8 A Two-Stage Longitudinal Propensity Adjustment for Analysis of Observational Data
Abstract
8.1 Introduction
8.2 Longitudinal Model of Propensity for Treatment
8.3 Longitudinal Propensity-Adjusted Treatment Effectiveness Analyses
8.4 Application
8.5 Summary
Acknowledgments
References
Chapter 9 Analysis of Longitudinal Observational Data Using Marginal Structural Models
Abstract
9.1 Introduction
9.2 MSM Methodology
9.3 Example: MSM Analysis of a Simulated Schizophrenia Trial
9.4 Discussion
References
Chapter 10 Structural Nested Models
Abstract
10.1 Introduction
10.2 Time-Varying Causal Effect Moderation
10.3 Estimation
10.4 Empirical Example: Maximum Likelihood Data Analysis Using SAS PROC NLP
10.5 Discussion
Appendix 10.A
Appendix 10.B
Appendix 10.C
References
Chapter 11 Regression Models on Longitudinal Propensity Scores
Abstract
11.1 Introduction
11.2 Estimation Using Regression on Longitudinal Propensity Scores
11.3 Example
11.4 Summary
References
Part 4 Claims Database Research
Chapter 12 Good Research Practices for the Conduct of Observational Database Studies
Abstract
12.1 Introduction
12.2 Checklist and Discussion
Acknowledgments
References
Chapter 13 Dose-Response Safety Analyses Using Large Health Care Databases
Abstract
13.1 Introduction
13.2 Data Structure
13.3 Treatment Model and Censoring Model Setup
13.4 Structural Model Implementation
13.5 Discussion
References
Part 5 Pharmacoeconomics
Chapter 14 Costs and Cost-Effectiveness Analysis Using Propensity Score Bin Bootstrapping
Abstract
14.1 Introduction
14.2 Propensity Score Bin Bootstrapping
14.3 Example: Schizophrenia Effectiveness Study
14.4 Discussion
References
Chapter 15 Incremental Net Benefit
Abstract
15.1 Introduction
15.2 Cost-Effectiveness Analysis
15.3 Parameter Estimation
15.4 Example
15.5 Observational Studies
15.6 Discussion
Acknowledgments
References
Chapter 16 Cost and Cost-Effectiveness Analysis with Censored Data
Abstract
16.1 Introduction
16.2 Statistical Methods
16.3 Example
16.4 Discussion
Acknowledgments
References
Part 6 Designing Observational Studies
Chapter 17 Addressing Measurement and Sponsor Biases in Observational Research
Abstract
17.1 Introduction
17.2 General Design Issues
17.3 Addressing Measurement and Sponsor Bias
17.4 Summary
References
Chapter 18 Sample Size Calculation for Observational Studies
Abstract
18.1 Introduction
18.2 Continuous Variables
18.3 Binary Variables
18.4 Two-Sample Log-Rank Test for Survival Data
18.5 Two-Sample Longitudinal Data
18.6 Discussion
Appendix: Asymptotic Distribution of Wilcoxon Rank Sum Test under Hα
References
Index
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Prev
Previous Chapter
Index
Contents
Preface
Part 1 Introduction
Chapter 1 Introduction to Observational Studies
1.1 Observational vs. Experimental Studies
1.2 Issues in Observational Studies
1.3 Study Design
1.4 Methods
1.5 Some Guidelines for Reporting
Acknowledgments
References
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
Abstract
4.1 Introduction
4.2 Implemention with the DR Macro
4.3 Sample Analysis
4.4 Summary
4.5 Conclusion
References
Chapter 5 Propensity Scoring with Missing Values
Abstract
5.1 Introduction
5.2 Data Example
5.3 Using SAS for IPW Estimation with Missing Values
5.4 Sensitivity Analyses
5.5 Discussion
References
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
Abstract
7.1 Introduction
7.2 Some Traditional Analyses of Hypothetical Patient Registry Data
7.3 The Four Phases of a Local Control Analysis
7.4 Conclusion
Acknowledgments
Appendix: Propensity Scores and Blocking/Balancing Scores
References
Part 3 Longitudinal Bias Adjustment
Chapter 8 A Two-Stage Longitudinal Propensity Adjustment for Analysis of Observational Data
Abstract
8.1 Introduction
8.2 Longitudinal Model of Propensity for Treatment
8.3 Longitudinal Propensity-Adjusted Treatment Effectiveness Analyses
8.4 Application
8.5 Summary
Acknowledgments
References
Chapter 9 Analysis of Longitudinal Observational Data Using Marginal Structural Models
Abstract
9.1 Introduction
9.2 MSM Methodology
9.3 Example: MSM Analysis of a Simulated Schizophrenia Trial
9.4 Discussion
References
Chapter 10 Structural Nested Models
Abstract
10.1 Introduction
10.2 Time-Varying Causal Effect Moderation
10.3 Estimation
10.4 Empirical Example: Maximum Likelihood Data Analysis Using SAS PROC NLP
10.5 Discussion
Appendix 10.A
Appendix 10.B
Appendix 10.C
References
Chapter 11 Regression Models on Longitudinal Propensity Scores
Abstract
11.1 Introduction
11.2 Estimation Using Regression on Longitudinal Propensity Scores
11.3 Example
11.4 Summary
References
Part 4 Claims Database Research
Chapter 12 Good Research Practices for the Conduct of Observational Database Studies
Abstract
12.1 Introduction
12.2 Checklist and Discussion
Acknowledgments
References
Chapter 13 Dose-Response Safety Analyses Using Large Health Care Databases
Abstract
13.1 Introduction
13.2 Data Structure
13.3 Treatment Model and Censoring Model Setup
13.4 Structural Model Implementation
13.5 Discussion
References
Part 5 Pharmacoeconomics
Chapter 14 Costs and Cost-Effectiveness Analysis Using Propensity Score Bin Bootstrapping
Abstract
14.1 Introduction
14.2 Propensity Score Bin Bootstrapping
14.3 Example: Schizophrenia Effectiveness Study
14.4 Discussion
References
Chapter 15 Incremental Net Benefit
Abstract
15.1 Introduction
15.2 Cost-Effectiveness Analysis
15.3 Parameter Estimation
15.4 Example
15.5 Observational Studies
15.6 Discussion
Acknowledgments
References
Chapter 16 Cost and Cost-Effectiveness Analysis with Censored Data
Abstract
16.1 Introduction
16.2 Statistical Methods
16.3 Example
16.4 Discussion
Acknowledgments
References
Part 6 Designing Observational Studies
Chapter 17 Addressing Measurement and Sponsor Biases in Observational Research
Abstract
17.1 Introduction
17.2 General Design Issues
17.3 Addressing Measurement and Sponsor Bias
17.4 Summary
References
Chapter 18 Sample Size Calculation for Observational Studies
Abstract
18.1 Introduction
18.2 Continuous Variables
18.3 Binary Variables
18.4 Two-Sample Log-Rank Test for Survival Data
18.5 Two-Sample Longitudinal Data
18.6 Discussion
Appendix: Asymptotic Distribution of Wilcoxon Rank Sum Test under
H
α
References
Index
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