Evaluation for Health Policy and Health Care: A Contemporary Data-Driven Approach

Brief Contents
Detailed Contents
List of Figures and Tables
Preface
Acknowledgments
About the Editors
PART I: SETTING UP FOR EVALUATION
	1 Introduction • Steven Sheingold and William Shrank
	2 Setting the Stage • Anupa Bir and Steven Sheingold
	3 Measurement and Data • Steve Asch and Jun Li
PART II: EVALUATION METHODS
	4 Causality and Real-World Evaluation • Anupa Bir, Nikki Freeman, and Jeffrey Wooldridge
	5 Randomized Designs • Jeff Wooldridge, Guido Imbens, Nikki Freeman, and Rachael Zuckerman
	6 Quasi-experimental Methods: Propensity Score Techniques • Jeff Wooldridge, Guido Imbens, Nikki Freeman, and Rachael Zuckerman
	7 Quasi-experimental Methods: Regression Modeling and Analysis • Jeff Wooldridge, Guido Imbens, Nikki Freeman, Rachael Zuckerman, Steven Sheingold, and John Orav
	8 Treatment Effect Variations Among the Treatment Group • Steven Sheingold, Kevin Smith, and Lok Wong Samson
	9 The Impact of Organizational Contexton Heterogeneity of Outcomes: Lessons for Implementation Science • Thomas Nolan, Leila Kahwati, and Heather Kane
PART III: MAKING EVALUATION MORE RELEVANT TO POLICY
	10 Evaluation Model Case Study: The Learning System at the Center for Medicare and Medicaid Innovation • Rocco Perla and Bruce Finke
	11 Program Monitoring: Aligning Decision Making With Evaluation • Will Shrank, Sebastian Schneeweiss, and K. Malcolm Maclure
	12 Alternative Ways of Analyzing Datain Rapid-Cycle Evaluation • Thomas Nolan, Jerry Cromwell, Martijn van Hasselt, and Nikki Freeman
	13 Synthesizing Evaluation Findings • Anupa Bir, Kevin Smith, Jim Derzon, Martijn van Hasselt, and Nikki Freeman
	14 Decision Making Using Evaluation Results • Steven Sheingold
	15 Communicating Research and Evaluation Results to Policymakers • Steven Sheingold and Anupa Bir
Appendix A: The Primer Measure Set
Appendix B: Quasi-experimental Methods That Correct for Selection Bias: Further Comments and Mathematical Derivations
Index