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| Document Type: | Book |
|---|---|
| All Authors / Contributors: |
Steven Howard Sheingold; Anupa Bir |
| ISBN: | 9781544333717 1544333714 |
| OCLC Number: | 1104920263 |
| Description: | xix, 312 pages : illustrations ; 24 cm |
| Contents: | List of Figures and TablesPrefaceAcknowledgmentsAbout the EditorsPART I. SETTING UP FOR EVALUATIONChapter 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 IntentChapter 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 SummaryChapter 3. Measurement and Data Guiding Principles Measure Types Measures of Structure Measures of Process Measures of Outcomes Selecting Appropriate Measures Data Sources Looking Ahead SummaryPART II. EVALUATION METHODSChapter 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 SummaryChapter 5. Randomized Designs Randomized Controlled Trials Stratified Randomization Group Randomized Trials Randomized Designs for Health Care SummaryChapter 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 SummaryChapter 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 SummaryChapter 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 SummaryChapter 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 SummaryPART III. MAKING EVALUATION MORE RELEVANT TO POLICYChapter 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 SummaryChapter 11. Program Monitoring: Aligning Decision Making With Evaluation Nature of Decisions Cases: Examples of Decisions Evidence Thresholds for Decision Making in Rapid-Cycle Evaluation SummaryChapter 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 SummaryChapter 13. Synthesizing Evaluation Findings Meta-analysis Meta-evaluation Development for Health Care Demonstrations Meta-regression Analysis Bayesian Meta-analysis Putting It Together SummaryChapter 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 EvaluatorsChapter 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 ResultsAppendix A: The Primer Measure SetAppendix 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 LevelIndex |
| Responsibility: | edited by Steven Sheingold, Department of Health and Human Services, Anupa Bir, RTI International. |
Abstract:
Evaluation for Health Policy and Health Care: A Contemporary Data-Driven Approach explores the best practices and applications for producing, synthesizing, visualizing, using, and disseminating health care evaluation research and reports.
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Publisher Synopsis
"Evaluating health policies and programs can be a very challenging process because the evaluation itself is so often an afterthought, leading to a variety of data issues that can produce biased results and poor policy decisions. This book provides an outstanding-yet highly accessible-overview of a wide variety of methods that evaluators can use to minimize these biases and generate robust evidence for decision-makers." -- Larry R. Hearld "A must read for anyone interested in monitoring and evaluation! The text does a great job addressing the important ingredients for a successful evaluation." -- Sandra Schrouder "This text offers a general introduction to the process and methods used to conduct rigorous and timely evaluations of health policies and programs using real-world examples. It would make an excellent text for a program evaluation course." -- Brad Wright Read more...
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