Читать книгу Real World Health Care Data Analysis - Uwe Siebert - Страница 4

Оглавление

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

G

H

I

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

Real World Health Care Data Analysis

Подняться наверх