Evaluation for health policy and health care : a contemporary data-driven approach /: a contemporary data-driven approach. (2019)

• Record Type: Book
• Further Information: Note: Steven Sheingold, Anupa Bir.
• Authors: Sheingold, Steven Howard, 1951-; Bir, Anupa
• Contents: List of Figures and Tables; Preface; Acknowledgments; About the Editors; PART I. SETTING UP FOR EVALUATION; Chapter 1. Introduction; Background: Challenges and Opportunities; Evaluation and Health Care Delivery System Transformation; The Global Context for Considering Evaluation Methods and Evidence-Based Decision Making; Book’s Intent; Chapter 2. Setting the Stage; Typology for Program Evaluation; Planning an Evaluation: How Are the Changes Expected to Occur?; Developing Evaluations: Some Preliminary Methodological Thoughts; Prospectively Planned and Integrated Program Evaluation; Summary; Chapter 3. Measurement and Data; Guiding Principles; Measure Types; Measures of Structure; Measures of Process; Measures of Outcomes; Selecting Appropriate Measures; Data Sources; Looking Ahead; Summary; PART II. EVALUATION METHODS; Chapter 4. Causality and Real-World Evaluation; Evaluating Program/Policy Effectiveness: The Basics of Inferring Causality; Defining Causality; Assignment Mechanisms; Three Key Treatment Effects; Statistical and Real-World Considerations for Estimating Treatment Effects; Summary; Chapter 5. Randomized Designs; Randomized Controlled Trials; Stratified Randomization; Group Randomized Trials; Randomized Designs for Health Care; Summary; Chapter 6. Quasi-experimental Methods: Propensity Score Techniques; Dealing With Selection Bias; Comparison Group Formation and Propensity Scores; Regression and Regression on the Propensity Score to Estimate Treatment Effects; Summary; Chapter 7. Quasi-experimental Methods: Regression Modeling and Analysis; Interrupted Time Series Designs; Comparative Interrupted Time Series; Difference-in-Difference Designs; Confounded Designs; Instrument Variables to Estimate Treatment Effects; Regression Discontinuity to Estimate Treatment Effects; Fuzzy Regression Discontinuity Design; Additional Considerations: Dealing With Nonindependent Data; Summary; Chapter 8. Treatment Effect Variations Among the Treatment Group; Context: Factors Internal to the Organization; Evaluation Approaches and Data Sources to Incorporate Contextual Factors; Context: External Factors That Affect the Delivery or Potential Effectiveness of the Treatment; Individual-Level Factors That May Cause Treatment Effect to Vary; Methods for Examining the Individual Level Heterogeneity of Treatment Effects; Multilevel Factors; Importance of Incorporating Contextual Factors Into an Evaluation; Summary; Chapter 9. The Impact of Organizational Context on Heterogeneity of Outcomes: Lessons for Implementation Science; Context for the Evaluation: Some Examples From Centers for Medicare and Medicaid Innovation; Evaluation for Complex Systems Change; Frameworks for Implementation Research; Organizational Assessment Tools; Analyzing Implementation Characteristics; Summary; PART III. MAKING EVALUATION MORE RELEVANT TO POLICY; Chapter 10. Evaluation Model Case Study: The Learning System at the Center for Medicare and Medicaid Innovation; Step 1: Establish Clear Aims; Step 2: Develop an Explicit Theory of Change; Step 3: Create the Context Necessary for a Test of the Model; Step 4: Develop the Change Strategy; Step 5: Test the Changes; Step 6: Measure Progress Toward Aim; Step 7: Plan for Spread; Summary; Chapter 11. Program Monitoring: Aligning Decision Making With Evaluation; Nature of Decisions; Cases: Examples of Decisions; Evidence Thresholds for Decision Making in Rapid-Cycle Evaluation; Summary; Chapter 12. Alternative Ways of Analyzing Data in Rapid-Cycle Evaluation; Statistical Process Control Methods; Regression Analysis for Rapid-Cycle Evaluation; A Bayesian Approach to Program Evaluation; Summary; Chapter 13. Synthesizing Evaluation Findings; Meta-analysis; Meta-evaluation Development for Health Care Demonstrations; Meta-regression Analysis; Bayesian Meta-analysis; Putting It Together; Summary; Chapter 14. Decision Making Using Evaluation Results; Research, Evaluation, and Policymaking; Program/Policy Decision Making Using Evidence: A Conceptual Model; Multiple Alternatives for Decisions; A Research Evidence/Policy Analysis Example: Socioeconomic Status and the Hospital Readmission Reduction Program; Other Policy Factors Considered; Advice for Researchers and Evaluators; Chapter 15. Communicating Research and Evaluation Results to Policymakers; Suggested Strategies for Addressing Communication Issues; Other Considerations for Tailoring and Presenting Results; Closing Thoughts on Communicating Research Results; Appendix A: The Primer Measure Set; Appendix B: Quasi-experimental Methods That Correct for Selection Bias: Further Comments and Mathematical Derivations; Propensity Score Methods; An Alternative to Propensity Score Methods; Assessing Unconfoundedness; Using Propensity Scores to Estimate Treatment Effects; Unconfounded Design When Assignment Is at the Group Level; Index;
• Edition: 1st
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 362.10727; Medical policy -- Evaluation -- Statistical methods; Medical care -- Evaluation -- Statistical methods
• Languages: English
• ISBNs: 9781544333694
• Related ISBNs: 9781544333717
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.