Multilevel Modelling for Public Health and Health Services Research

Preface
Acknowledgements
Contents
About the Authors
Part I: Theoretical, Conceptual and Methodological Background
	Chapter 1: Introduction
		Importance of MLA for Research in Health and Care
		The Scope of Public Health and Health Services Research
		Research and Policy
		Conclusion
		References
	Chapter 2: Health in Context
		Relationships Between the Macro and Micro Levels
		Micro Level: Behaviour of Patients and Providers
			The Behaviour of Healthcare Providers
			The Behaviour of Patients
			Patient-Provider Interaction
		From Macro to Micro Level
		What Contexts Are Relevant?
		From Micro to Macro Level
		The Use of ``League Tables´´
		Conclusion
		References
	Chapter 3: What Is Multilevel Modelling?
		Methodological Background
		Why Use Multilevel Modelling?
			Aggregate Analysis
			Individual Analysis
			Separate Individual Analyses Within Each Higher Level Unit
			Individual-Level Analysis with Dummy Variables
		What Is a Multilevel Model?
		What Is a Level?
		How Many Units Do We Need at Each Level?
		Hypotheses That Can Be Tested with Multilevel Analysis
			Hypotheses About Variation
			Individual-Level Hypotheses
			Context Hypotheses
				Aggregated Individual-Level Characteristics
				Higher Level Characteristics
			Cross-Level Interactions
		Conclusion
		References
	Chapter 4: Multilevel Data Structures
		Strict Hierarchies: The Basic Model
		Multistage Sampling Designs
		Evaluating Community Interventions and Cluster Randomised Trials
		Designs Including Time
		Multiple Responses
		Non-hierarchical Structures
			Cross-Classified Models
			Multiple Membership Model
			Correlated Cross-Classified Model
		Other Multilevel Models
		Pseudo-levels
		Incomplete Hierarchies
		Conclusion
		References
Part II: Statistical Background
	Chapter 5: Graphs and Equations
		Ordinary Least Squares (Single-Level) Regression
		Random Intercept Model
		Random Slope Model
		Three-Level Model
		Heteroscedasticity
		Fixed Effects Model
		Rankings and Institutional Performance
		Conclusion
		References
	Chapter 6: Apportioning Variation in Multilevel Models
		Variance Partitioning for Continuous Responses
		Variance Partitioning for Multilevel Logistic Regression
		Variance Partitioning for Models with Three or More Levels
		Interpretation of Variances
		Zero Variance
		Multilevel Power Calculations
		Software for Multilevel Power Calculations
		Population Average and Cluster-Specific Estimates
		Omitting a Level
		Conclusion
		References
Part III: The Modelling Process and Presentation of Research
	Chapter 7: Context, Composition and How Their Influences Vary
		Context or Composition?
		Using Multilevel Modelling to Investigate Compositional and Contextual Effects
			Model M0: Null Model
			Model M1: Individual Social Capital
			Model M2: Neighbourhood Social Capital
			Model M3: Individual and Neighbourhood Social Capital
			Model M4: Individual and Neighbourhood Social Capital and Their Interaction
		Random Slopes and Cross-Level Interactions
		Impact of Compositional and Contextual Variables on the Variances
		Model Specification and Model Interpretation
		Sources of Error Affecting the Estimation of Contextual Effects
			Lack of Variation in the Contextual Variable
			Precision of Estimates and Study Design
			Selection Bias
			Confounding
			Information Bias
			Model Specification
		Conclusions
		References
	Chapter 8: Ecometrics: Using MLA to Construct Contextual Variables from Individual Data
		Problems with Simple Aggregation
		Single Variables
		Composite Variables: The Traditional Method
		Composite Variables: A Simple Multilevel Model
		Ecometric Approach
		Application of the Ecometric Approach
		Comparison of the Traditional and Ecometric Approach
		Further Ecometric Properties of the Scale
		Conclusions
		References
	Chapter 9: Modelling Strategies
		Define the Data Structure
		Measurement Level and Distribution of the Dependent Variable
		The Baseline Model
		Exploratory Research and Hypothesis Testing
		Context and Composition
		Modelling the Effects of Higher Level Characteristics
		Random Effects at Higher Levels
		Interpreting the Results in the Light of Common Assumptions
		Conclusions
		References
	Chapter 10: Reading and Writing
		Critical Reading
			What Is the Research Question?
			Which Levels Can Be Distinguished Theoretically?
			What Is the Structure of the Actual Data Used?
			What Statistical Model Was Used?
			What Was the Modelling Strategy?
			Does the Paper Report the Intercept Variation at Different Levels?
			Cross-Level Interactions
			What Are the Shortcomings and Strong Points of the Article?
		Writing Up Your Own Research
			The Introduction or Background Section
			The Methods Section
			The Results Section
			The Conclusion and Discussion Section
		Conclusions
		References
Part IV: Tutorials with Example Datasets
	Chapter 11: Multilevel Linear Regression Using MLwiN: Mortality in England and Wales, 1979-1992
		Introduction to the Dataset
		Research Questions
		Introduction to MLwiN
			Opening a Worksheet
			Names Window
			Data Window
			Graph Window
		Model Specification
			Creating New Variables
			Equations Window
			Fitting the Model
		Variance Components
			A 2-Level Variance Components Model
			Sorting the Data
			The Hierarchy Viewer
			Adding a Further Level
		Interpreting the Model
			Residuals
			Predictions Window
		Model Building
			Adding More Fixed Effects
			Intervals and Tests Window
		Random Coefficients
			Random Slopes
			Variance Function Window
		Higher-Level Residuals
			Complex Level 1 Variation
		A Poisson Model: Introduction
		Setting Up a Generalised Linear Model in MLwiN
			The Offset
			Non-linear Estimation
		Model Interpretation
		Predictions and Confidence Envelopes
		References
	Chapter 12: Multilevel Logistic Regression Using MLwiN: Referrals to Physiotherapy
		Multilevel Logistic Regression Model
		Example: Variation in the GP Referral Rate to Physiotherapy
		The Data
		Model Set-Up
		Non-linear Settings
		Model Interpretation and Model Building
		A Note on Estimation
		Further Exercises
		References
	Chapter 13: Untangling Context and Composition
		The Data
		Structure of the Analysis
		Estimating the Null Model
		Fixed Effects
		Additional Models
		References
Index