Clinical Epidemiology: Practice and Methods

Preface
Contents
Contributors
Chapter 1: On Framing the Research Question and Choosing the Appropriate Research Design
	1 Introduction
	2 Framing the Clinical Research Question
	3 Error
	4 Sampling
	5 Sample Size Estimation
	6 Measurement
		6.1 Variable Types
		6.2 Measurement Errors
	7 External and Internal Validity
	8 Clinical Relevance vs. Statistical Significance
	9 Hierarchy of Evidence
	10 Experimental Designs for Intervention Questions
	11 Designs for Diagnostic Questions
	12 Maximizing the Validity of Nonexperimental Studies
	13 Reporting
	References
Chapter 2: Bias in Clinical Research
	1 Introduction
	2 Types of Bias Common in Epidemiologic Studies
		2.1 Selection Bias
			2.1.1 Ascertainment Bias
			2.1.2 Sampling Bias
			2.1.3 Competing Risks Bias
			2.1.4 Volunteer Bias
			2.1.5 Nonresponse Bias
			2.1.6 Loss to Follow-Up Bias
			2.1.7 Attrition Bias
			2.1.8 Prevalence-Incidence (Neyman) Bias
			2.1.9 Survivor Treatment Selection Bias
			2.1.10 Overmatching Bias
		2.2 Information Bias
			2.2.1 Recall Bias
			2.2.2 Interviewer Bias
			2.2.3 Observer Bias
			2.2.4 Lead-Time Bias
			2.2.5 Length-Time Bias
			2.2.6 Diagnostic Bias
			2.2.7 Will Rogers Phenomenon
			2.2.8 Family Information Bias
		2.3 Intervention (Exposure) Bias
			2.3.1 Allocation Bias
			2.3.2 Compliance Bias
			2.3.3 Proficiency Bias
			2.3.4 Contamination Bias
		2.4 Confounding
	3 Biases Linked to Specific Study Designs
	4 Methods to Minimize the Impact of Bias and Confounding in Epidemiologic Studies
		4.1 Strategies for Dealing with Bias
		4.2 Strategies for Dealing with Confounding
			4.2.1 Prevention in the Design Phase
			4.2.2 Adjustment in the Analysis Phase
	5 Conclusions
	References
Chapter 3: Definitions of Bias in Clinical Research
	1 Introduction
	2 General Terms (Table 1)
	3 Biases Associated with Literature Review and Publications (Table 3)
	4 Biases Associated with Study Design and Subject Selection (Table 4)
	5 Biases Associated with Executing the Intervention (Table 5)
	6 Biases Associated with Measuring Exposures and Outcomes (Table 6)
	7 Biases Associated with Data Analysis (Table 7)
	8 Biases Associated with Interpretation and Publication (Table 8)
	References
Chapter 4: Research Ethics for Clinical Researchers
	1 The Importance of Research Ethics
	2 Core Principles
	3 Research Ethics Review
	4 Privacy and Confidentiality
	5 Informed Consent
	6 Inclusiveness in Research
	7 Practical Tips for Researchers
	8 Limitations of This Chapter
	References
Chapter 5: Ethical, Legal, and Social Issues (ELSI) in Clinical Genetics Research
	1 Introduction: What Is ELSI Research?
	2 Advent of ELSI/GE3LS Research
	3 ``Descriptive´´ Versus ``Normative´´ Research
	4 Engaging Publics and Patients About Genomics: An Illustrative Case Study of ELSI Research Topics and Methods
		4.1 Public and Patient Engagement Has Been a Significant ELSI Focus in Genetics and Genomics Research
		4.2 Engagement Approaches and Topics in Genetics ELSI Research
		4.3 National Surveys of Attitudes and Perceptions
		4.4 Assessing Attitudes and Raising Genomics Awareness-Beyond Surveys
	5 ELSI Topics of Interest to Genomics Policy and Research
		5.1 Informed Consent
		5.2 The Return of Genomic Sequencing Test Results
		5.3 Deliberative ELSI Approaches
	6 Practical Considerations and Suggestions for Others Undertaking an ELSI Initiative
	7 Conclusion
	References
Chapter 6: Longitudinal Studies 1: Determinants of Risk
	1 Introduction
		1.1 Non-Randomized Study Designs
	2 Cohort Studies
		2.1 Types of Cohort Studies
		2.2 Advantages of Cohort Studies
		2.3 Disadvantages of Cohort Studies
			2.3.1 Missing Data
			2.3.2 Ascertainment Bias
			2.3.3 Contamination
			2.3.4 Selection Bias
			2.3.5 Bias by Indication
			2.3.6 Dose-Targeting Bias
	3 Designing a Cohort Study
		3.1 Prevalent Versus Incident Cohorts
		3.2 Data Collection
		3.3 Confounding Variables
		3.4 Selecting a Control Group
	4 Case-Control Studies
		4.1 Nested Case-Control Studies
		4.2 Advantages of Case-Control Studies
		4.3 Disadvantages of Case-Control Studies
			4.3.1 Confounding Variables
			4.3.2 Sampling Bias
			4.3.3 Information Bias
	5 Designing a Case-Control Study
		5.1 Selecting a Control Group
	6 Power and Sample Size Estimation in Observational Studies
		6.1 Statistical Power
		6.2 Factors Determining the Required Sample Size
		6.3 Calculating the Required Sample Size for a Relative Risk or Odds Ratio
		6.4 Calculating the Sample Size for a Log-Rank Test
		6.5 Calculating Sample Size for a Cox Proportional Hazards Model
	7 Analyzing Longitudinal Studies
		7.1 Identifying Confounders
		7.2 Calculating an Estimate of Risk
			7.2.1 Simple Hypothesis Testing for Risk Estimates
			7.2.2 Controlling for Confounders
		7.3 Analyzing Survival Data
		7.4 Limitations of Multivariate Techniques
	References
Chapter 7: Longitudinal Studies 2: Modeling Data Using Multivariate Analysis
	1 Introduction
	2 Principles of Regression and Modeling
		2.1 Role of Statistics
		2.2 Concept of Function
		2.3 Regression Methods
			2.3.1 Estimation Purpose
			2.3.2 Meaning of the Model Parameters
			2.3.3 Estimation Methods
			2.3.4 Likelihood and Probability
	3 Statistical Models
		3.1 Meaning and Structure
		3.2 Model Choice
			3.2.1 General Approach
			3.2.2 Form of the Exposure-Response Relationship
			3.2.3 Random Component
			3.2.4 Data Transformation
			3.2.5 Meaning of the Model Assumptions
		3.3 Multivariable vs. Univariable Analysis
	4 Confounding and Interaction
		4.1 Confounding
		4.2 Interaction in Additive Models
			4.2.1 Definition and Examples
			4.2.2 Modeling Confounding and Interaction
			4.2.3 Statistical Meaning of Interaction
			4.2.4 Epidemiological Meaning of the Interaction Coefficient
		4.3 Interaction in Multiplicative Models
	5 Reporting
		5.1 Methods
		5.2 Results
	References
Chapter 8: Longitudinal Studies 3: Data Modeling Using Standard Regression Models and Extensions
	1 Introduction
	2 Generalized Linear Models
		2.1 General Linear Model for Quantitative Responses
			2.1.1 Structure of the Linear Model
			2.1.2 Meaning of the Coefficients in Linear Regression
			2.1.3 Model Check
		2.2 Logistic Model for Qualitative Responses
			2.2.1 Structure of the Logistic Model
			2.2.2 Meaning of the Coefficients in Logistic Regression
			2.2.3 Model Check and Other Issues
		2.3 Poisson Model for Counts
			2.3.1 Structure of the Poisson Model
			2.3.2 Meaning of the Coefficients in Poisson Regression
			2.3.3 Model Check
	3 Models for Time-to-Event Data
		3.1 Survival Data
		3.2 Key Requirements for Survival Analysis
		3.3 Functions of Time-to-Event Data
		3.4 The Cox Model
			3.4.1 Structure of the Proportional Hazards Model
			3.4.2 Meaning of the Coefficients in Cox´s Regression
			3.4.3 Model Checks
	4 Extended Models
		4.1 Extended Generalized Linear Models
			4.1.1 Panel Data Layout
			4.1.2 Modeling Random Effects
			4.1.3 Correcting the Model Variance
			4.1.4 Model Choice
		4.2 Extended Survival Models
			4.2.1 Risk Sets for Survival Analysis
				Unordered Events
				Ordered Events
				Time-Dependent Effects and Time-Varying Covariates
			4.2.2 Variance-Corrected Models
			4.2.3 Frailty Models
			4.2.4 Model Choice
			4.2.5 Competing Risks
		4.3 Special Topics
	References
Chapter 9: Longitudinal Studies 4: Matching Strategies to Evaluate Risk
	1 Introduction
	2 Matching in Case-Control Studies
		2.1 Potential for Selection Bias in Matched Case-Control Studies
		2.2 Advantages of Matching in Case-Control Studies
		2.3 Disadvantages of Matching in Case-Control Studies
	3 Matching in Cohort Studies
	4 Matching Using a Propensity Score
		4.1 Advantages of Propensity Score Matching
		4.2 Limitations of Propensity Score-Matched Studies
	5 Steps in Performing a Matched Study
		5.1 Deriving a Propensity Score
		5.2 Constructing the Propensity Score-Matched Sample
		5.3 Assessing the Balance of Covariates in the Matched Sample
		5.4 Estimating the Association Between Exposure and Outcome(s)
	References
Chapter 10: Longitudinal Studies 5: Development of Risk Prediction Models for Patients with Chronic Disease
	1 Introduction
	2 Methods of Model Development
		2.1 Internal Validity: Getting the Basics Right
		2.2 Metrics of Predictive Performance
			2.2.1 Discrimination
			2.2.2 Calibration
			2.2.3 Reclassification
			2.2.4 External Validity/Validation
			2.2.5 Knowledge Translation
	3 Practical Application: Development of the Kidney Failure Risk Equation
		3.1 Derivation Cohort Selection
		3.2 Selection of Variables
		3.3 Model Development
		3.4 Validation Cohort Selection
		3.5 Results of the Study
			3.5.1 Prediction Model Performance in the Development Cohort
			3.5.2 Prediction Model Performance in the Validation Cohort
			3.5.3 Net Reclassification
		3.6 Knowledge Translation
		3.7 Subsequent Validation Steps
	4 Summary
	References
Chapter 11: Randomized Controlled Trials 1: Design
	1 Introduction
	2 Asking the Question
		2.1 Identifying the Problem
		2.2 The Principal Research Question
	3 Trial Design
		3.1 Randomization
		3.2 Adaptive Trials
		3.3 Pragmatic Trials
		3.4 Crossover
		3.5 Non-Inferiority
		3.6 Blinding or Masking
		3.7 Multi-Center
		3.8 Planned Trial Allocations and Interventions
		3.9 Inclusion and Exclusion Criteria
		3.10 Primary and Secondary Outcome Measures
		3.11 Measuring the Outcome Measures at Follow-Up
	4 Size and Duration of Trial
		4.1 Estimating Sample Size
		4.2 Recruitment Rate
		4.3 Duration of the Treatment Period
	5 Trial Data
		5.1 Data Collection and Management
		5.2 Details of the Planned Analyses
		5.3 Planned Subgroup Analyses
		5.4 Frequency of Analyses
		5.5 Economic Issues
		5.6 Audit Trail
	6 Challenges to Trial Integrity
		6.1 Rate of Loss to Follow-Up
		6.2 Methods for Protecting Against Other Sources of Bias
	7 Funding
		7.1 Costs
		7.2 Licensing
		7.3 Funding Sources
	8 Details of the Trial Team
		8.1 Steering Committee
		8.2 Trial Manager
		8.3 End-Point Adjudication Committee
		8.4 Data Safety and Monitoring Committee
		8.5 Participating Centers
	9 Reporting
	References
Chapter 12: Randomized Controlled Trials 2: Analysis
	1 Introduction
	2 What Is the Primary Study Question?
	3 What Are the Characteristics of the Trial Subjects?
	4 Intention-to-Treat Analysis
	5 Interim Analyses
	6 Censoring and Death as a Non-Primary Outcome
	7 Composite Outcomes
	8 Trials with Open-Label Run-in Periods on Active Therapy
	9 Factorial, Stratified, and Crossover Trials
	10 Treatment-Target Designs
	11 Number Needed to Treat
	12 Neutral Trials: Power Issues
	13 Imbalanced Randomization of Important Baseline Characteristics
	14 Subgroup Analysis
	15 Analysis of Randomized Trials from an Observational Perspective
	16 Surrogate Markers
	17 Hypothesis-Generating Analyses from Completed Randomized Trials
	References
Chapter 13: Randomized Controlled Trials 3: Measurement and Analysis of Patient-Reported Outcomes
	1 History and Definition of Patient-Reported Outcomes
	2 Experience and Preference
	3 Regulatory Standards
	4 Instrument Selection
	5 Individualized Measures of Patient-Reported Outcomes
	6 Defining the Issues: Focus Groups
	7 Characteristics of Scales
	8 Validity
	9 Reliability
	10 Classical Test Theory and Item Response Theory
	11 Comparing Groups and Assessing Change Over Time
	References
	Further Reading
Chapter 14: Randomized Controlled Trials 4: Planning, Analysis, and Interpretation of Quality-of-Life Studies
	1 Introduction
	2 QoL Instruments
		2.1 Generic Instruments
		2.2 Disease-Specific Instruments
		2.3 QoL Scoring
	3 Analysis of Treatment Effect
		3.1 Trial Design
		3.2 Compliance with Answering Questionnaires
		3.3 Statistical Plan
		3.4 Floor and Ceiling Effects
	4 Missing Data
	5 Quality-Adjusted Survival
	6 Clinical Interpretation and Clinically Important Differences
		6.1 An Example of Assessing Clinically Important Change
	7 Planning a Quality-of-Life Study in an RCT
	8 Conclusions
	References
Chapter 15: Randomized Controlled Trials 5: Biomarkers and Surrogates/Outcomes
	1 Introduction
	2 Definition and Uses of Biomarkers
		2.1 Definition
		2.2 Conceptual Relationship Between Biomarkers, Risk Factors, and Surrogate Outcomes
		2.3 Surrogates
	3 Uses of Biomarkers
		3.1 Prognosis
		3.2 Diagnosis
		3.3 Surrogate or Mechanistic Outcomes in Interventional Studies
		3.4 Additional Uses of Biomarkers
	4 Clinical Validation of Biomarkers
		4.1 Validity
		4.2 Designing a Study to Assess the Prognostic Value of a Biomarker
		4.3 Statistical Analytical Considerations in a Prognostic Biomarker Study
			4.3.1 Analysis and Multivariate Modeling
			4.3.2 The C-Statistic
			4.3.3 Integrated Discrimination Improvement
			4.3.4 Reclassification
		4.4 Sample Size Considerations for a Prognostic Biomarker Study
	5 Designing a Study to Measure the Diagnostic Usefulness of a Biomarker
		5.1 Study Design
		5.2 Statistical Analytical Considerations in a Diagnostic Biomarker Study
			5.2.1 Dichotomous Biomarker
			5.2.2 Continuous Biomarker
		5.3 Sample Size Considerations for a Diagnostic Study
		5.4 Establishing Generalizability: Derivation and Validation  Sets
	6 Surrogate Outcomes
		6.1 Definition
		6.2 Validation
		6.3 Limitations
	7 Conclusions
	References
Chapter 16: Randomized Controlled Trials 6: Determining the Sample Size and Power for Clinical Trials and Cohort Studies
	1 Introduction
	2 The Importance of Statistical Power
		2.1 Definition of Statistical Power
		2.2 Importance of Conducting Well-Powered Studies
		2.3 Are Underpowered Studies Ever Justified?
	3 Basics of Sample Size Calculation
		3.1 Mechanics of Sample Size Calculation
		3.2 Determining Study Population Input Parameters
		3.3 Pilot Studies
	4 Choosing the Effect Size
		4.1 Minimum Clinically Important Effect
		4.2 Biologically Plausible Effect
		4.3 Standardized Effect Size Conventions
		4.4 Non-Inferiority Trials
	5 Accounting for Loss to Follow-Up and Nonadherence
		5.1 Loss to Follow-Up
		5.2 Nonadherence
		5.3 Pragmatic Trials
	6 Cluster Randomized Trials
	7 Options When the Initial Calculated Sample Size Is Low
	8 Multiple Outcomes
	9 Power Calculations for Longitudinal Cohort Studies
	10 Other Issues and Conclusion
	11 Conclusion
	References
Chapter 17: Randomized Controlled Trials 7: On Contamination and Estimating the Actual Treatment Effect
	1 Introduction
	2 Controlling the Impact of Contamination
		2.1 Trial Design
		2.2 Sample Size Estimate
		2.3 Study Execution
		2.4 Statistical Plan
	3 Statistical Methods
		3.1 Lag Censoring
		3.2 Inverse Probability of Censoring Weights
		3.3 Rank Preserving Structural Failure Time Model
		3.4 Iterative Parameter Estimation
		3.5 Contamination Adjusted ITT
	4 An Example of Analysis of a Trial with Extensive Non-Adherence
		4.1 Censoring at the Time of Co-Interventions
		4.2 Lag Censoring
		4.3 Inverse Probability of Censoring Weights
		4.4 Iterative Parameter Estimation
		4.5 Interpretation
	5 Conclusion
	References
Chapter 18: Evaluation of Diagnostic Tests
	1 Introduction
	2 Diagnostic Test Accuracy Criteria
		2.1 Sensitivity and Specificity
		2.2 Positive and Negative Predictive Values
		2.3 Case Study
		2.4 Likelihood Ratios
		2.5 Overall Test Accuracy
	3 Design of Diagnostic Accuracy Studies
	4 Factors Relevant to the Choice of Diagnostic Tests
	5 Combinations of Diagnostic Tests
	6 Conclusion
	References
Chapter 19: Genetic Epidemiology of Complex Phenotypes
	1 Introduction
	2 Genetics of Complex Traits
	3 Mode of Inheritance
	4 Identifying Genetic Variation of Complex Traits
		4.1 Linkage Studies
			4.1.1 Parametric Linkage Analysis
			4.1.2 Nonparametric Linkage Analysis
			4.1.3 Analysis and Interpretation of Linkage Studies
			4.1.4 Challenges and Limitations of Linkage Studies
		4.2 Association-Based Studies
			4.2.1 Design of Association-Based Studies
			4.2.2 Selection of Disease Trait
			4.2.3 Choice of Population for Association-Based Studies
			4.2.4 Selection of Markers and Genotyping Technology
			4.2.5 Analysis and Interpretation of Association-Based Studies
			4.2.6 Challenges of Association-Based Studies
		4.3 Identification of Rare Variants
			4.3.1 Choice of Study Design/Population
			4.3.2 Selection of Markers and Genotyping Technology
			4.3.3 Analysis and Interpretation
			4.3.4 Challenges
		4.4 Future Direction
	5 Conclusion
	References
Chapter 20: Qualitative Research in Clinical Epidemiology
	1 Qualitative Research and Its Role in Clinical Epidemiology
	2 Important Concepts in Qualitative Research
		2.1 Epistemology
		2.2 Inductive Versus Deductive Approaches
		2.3 Method and Methodology
	3 The Research Question
	4 Overview of Qualitative Research Methods
		4.1 Sampling
		4.2 Data Collection
			4.2.1 Focus Groups
			4.2.2 Individual Interviews
			4.2.3 Observation
			4.2.4 Data Triangulation
			4.2.5 Review and Data Analysis
	5 Overview of Qualitative Methodologies
		5.1 Qualitative Description
		5.2 Interpretive Description
		5.3 Discourse Analysis
		5.4 Grounded Theory
		5.5 Phenomenology
		5.6 Ethnography
		5.7 Participatory Action Research
	6 Ensuring Rigor in Qualitative  Work
	7 Common Pitfalls
	8 Summary
	References
Chapter 21: Evidence-Based Decision-Making 1: Critical Appraisal
	1 Introduction
	2 The Process of Evidence-Based Medicine
	3 Levels of Scientific Evidence
	4 Critical Appraisal: Basics
		4.1 Major Questions
			4.1.1 Are the Results of the Study Valid?
			4.1.2 What Are the Results?
			4.1.3 Are the Results from the Study Applicable/Relevant to My Research Question, Patient, or Population of Interest?
		4.2 Assessing Risk of Bias
	5 GRADE (Grading of Recommendations Assessment, Development and Evaluation)
		5.1 Other Tools Used to Assess the Quality of a Study and Grade the Level of Evidence
			5.1.1 The U.S. Preventive Services Task Force
			5.1.2 The Oxford Centre for Evidence-Based Medicine
			5.1.3 AMSTAR: A MeaSurement Tool to Assess Systematic Reviews
	6 Guidelines for Reporting Study Findings
	7 Assessing Causation
	8 Concluding Remarks
	References
Chapter 22: Evidence-Based Decision-Making 2: Systematic Reviews and Meta-Analysis
	1 Introduction
	2 How Do Systematic Reviews Differ from Narrative Reviews?
	3 How Do Systematic Reviews Differ from Scoping Reviews?
	4 Why Are Systematic Reviews and Meta-Analyses Clinically Relevant?
	5 How Are Systematic Reviews Conducted?
	6 How Should the Quality of a Systematic Review or Meta-Analysis Be Appraised?
		6.1 Was the Review Conducted According to a Pre-specified Protocol?
		6.2 Was the Question Focused and Well Formulated?
		6.3 Were the ``Right´´ Types of Studies Eligible for the Review?
		6.4 Was the Method of Identifying All Relevant Information Comprehensive?
		6.5 Was the Data Abstraction from Each Study Appropriate?
		6.6 How Was the Information Synthesized and Summarized?
		6.7 Assessing a Systematic Review Using GRADE
	7 What Are the Strengths of Systematic Reviews and Meta-Analyses?
	8 What Are the Limitations of Systematic Reviews and Meta-Analyses?
	9 Summary
	References
Chapter 23: Evidence-Based Decision Making 3: Health Technology Assessment
	1 Introduction
		1.1 What Is ``Health Technology Assessment´´?
	2 Basic Framework for Conducting an HTA
		2.1 Identifying the Topic for Assessment and Setting Priorities
		2.2 Clear Specification of the Assessment Problem
		2.3 Evaluation of Social and Ethical Issues
			2.3.1 Approaches to Identifying Social Impacts
			2.3.2 Ethical Analysis in HTA
		2.4 Sources of Research Evidence for HTA
			2.4.1 Types of Literature
			2.4.2 Designing a Search Strategy
		2.5 Assessing the Quality of the Evidence
		2.6 Synthesize and Consolidate Evidence
		2.7 Collection of Primary Data (as Appropriate)
		2.8 Economic Analysis in HTA
			2.8.1 Cost-Effectiveness Analysis (CEA)
			2.8.2 Cost-Benefit Analysis (CBA)
			2.8.3 Cost-Utility Analysis (CUA)
		2.9 Formulation of Findings and Recommendations
		2.10 Dissemination of Findings and Recommendations
		2.11 Monitoring Impact of Assessment Reports
	3 Discussion
	4 Concluding Remarks
	References
Chapter 24: Evidence-Based Decision Making 4: Clinical Practice Guidelines
	1 Introduction
	2 The Goals of CPGs
	3 Development of CPGs
		3.1 Bias Minimization: Work Group Composition
		3.2 Systematic Retrieval and Review of the Evidence
			3.2.1 Evidence Sources
		3.3 Systematic Review and Meta-analysis in the Development of CPGs
		3.4 Grading of the Evidence and Statement Development
		3.5 Focus on Patient Relevant Outcomes
		3.6 Caveats and Limitations of CPGs
	4 Updating of Guidelines
	5 Systems Required for CPG Development and Implementation
	6 Evidence-Based Decision Making Versus Clinical Practice Guidelines
	7 Summary
	References
Chapter 25: Evidence-Based Decision Making 5: Knowledge Translation and the Knowledge to Action Cycle
	1 What Is Knowledge Translation?
	2 Frameworks for Knowledge Translation
	3 The Knowledge to Action Cycle
		3.1 Overview
		3.2 Timing of Dialysis Initiation: An Example
	4 Steps in Knowledge to Action Cycle
		4.1 Knowledge Creation: Knowledge Inquiry, Synthesis, and Knowledge Products
		4.2 Selecting Priorities for Knowledge Translation
		4.3 Adapting Knowledge to Local Context
		4.4 Assessing Barriers to the Implementation of Evidence
		4.5 Selecting, Tailoring, and Implementing Interventions
			4.5.1 Physician Knowledge
			4.5.2 Information Overload, Lack of Awareness of New Evidence, and Lack of Clarity on How to Implement Intervention
			4.5.3 Strategies Aimed at Other Barriers
		4.6 Monitor Knowledge  Use
		4.7 Evaluate Outcomes
		4.8 Sustaining Knowledge  Use
		4.9 Bringing It All Together: Returning to the Example of Timing of Dialysis Initiation
	5 Summary
	References
Chapter 26: Evidence-Based Decision Making 6: Administrative Databases as Secondary Data Source for Epidemiologic and Health S...
	1 What Are Administrative Data?
	2 Potential Sources of Administrative Health  Data
	3 Administrative Health Database Creation
	4 Using Administrative Data for Research Purpose
	5 Data Linkage with Other Data Sources
		5.1 Example of a Population-Based Linkage of Health Records in Alberta, Canada: Development of Health Services Research Admini...
	6 Big Data Analytics Approach Using Administrative Data Towards Precision Medicine and Precision Public Health
	7 Pros and Cons of Using Administrative  Data
	References
Chapter 27: Evidence-Based Decision Making 7: Health Economics in Clinical Research
	1 Overview
	2 Health Economics: The Basics
		2.1 Basic Concepts: Opportunity Cost and Efficiency
			2.1.1 Opportunity  Cost
			2.1.2 What Do We Mean by Efficiency?
	3 The Different Types of Economic Evaluations
		3.1 Cost-Effectiveness Analysis
		3.2 Cost-Utility Analysis
	4 How to Interpret the Results of Economic Evaluations
		4.1 Strengths and Weaknesses of Economic Evaluations
	5 How to Conduct an Economic Evaluation
	6 What to Consider When Planning Your Clinical Trial?
	7 How Can Health-Care Professionals Use the Results of Economic Evaluations When Caring for Their Patients?
	8 Conclusion
	References
Chapter 28: Evidence-Based Decision-Making 8: A Primer on Health Policy for Researchers
	1 Introduction
	2 What Is Health Policy?
	3 Purpose of Public Policy
	4 The Policy Arena
	5 A Policy Planning Algorithm
		5.1 Problem Identification
		5.2 Policy Formulation
		5.3 Policy Implementation
		5.4 Policy Evaluation
	6 Researchers and Policy
	7 Communicating Research Results to Policy-Makers
	8 Checklist
	9 Conclusions
	References
Chapter 29: Changing Health-Related Behaviors 1: Patient-Oriented Research and Patient Engagement in Health Research
	1 Patient Involvement in Health
	2 Canada´s Strategy for Patient-Oriented Research
		2.1 SUPPORT Units for Patient-Oriented Research
	3 Levels and Forms of Patient Engagement
		3.1 Levels of Patient Engagement
		3.2 Forms of Patient Engagement
	4 Guiding Principles and Best Practices for Patient-Oriented Research
	5 Advice from Patient Partners and Researchers Involved in Patient-Oriented Research
		5.1 National Perspectives
		5.2 Local Perspectives: Fireside Chat About Patient Engagement
	6 Local Examples of Patient-Oriented Research and Patient Engagement Activities
		6.1 Project Title: Why Don´t BRCA Carriers in NL Receive Adequate Cancer Screening and Prevention?
		6.2 Project Title: Antibiotics Overuse NL
		6.3 Reflections on Other Local Projects
	7 Practical Recommendations for Patient-Oriented Research
	8 Conclusions
	References
Chapter 30: Changing Health-Related Behaviors 2: On Improving the Value of Health Spending
	1 Introduction
		1.1 Health vs. Social Spending
	2 Value of National Health Spending
		2.1 The United States
			2.1.1 National Spending
			2.1.2 Value of Health Spending
			2.1.3 Reasons for Poor Value of Health Spending
		2.2 Canada and Australia
			2.2.1 National Spending
			2.2.2 Value of Health Spending
			2.2.3 Reasons for Differences in Value of Health Spending
		2.3 Provinces of Canada
			2.3.1 Provincial Spending
			2.3.2 Value of Provincial Health Spending
			2.3.3 Reasons for Poor Value of Health Spending in NL
	3 Utilization of Healthcare Interventions
		3.1 Choosing Wisely, a Solution for Overutilization of Low-Value Care
			3.1.1 Overutilization
			3.1.2 Choosing Wisely Campaign
			3.1.3 An Example of a Regional Choosing Wisely Unit
		3.2 Quality of Care
			3.2.1 Underutilization
			3.2.2 Quality of Care Councils
			3.2.3 An Example of a Quality of Care Program
			3.2.4 Quality of Care NL Change Strategies
	References
Chapter 31: Changing Health-Related Behaviors 3: Lessons from Implementation Science
	1 What Is Health-Related Behavior Change?
	2 What Are Behavior Change Interventions and Are They Effective?
	3 Development and Design of Behavior Change Interventions
		3.1 How to Design Theory-Informed Behavior Change Interventions
			3.1.1 Steps 1, 2, and 3: Understanding the Behavior-Defining the Problem, Selecting the Target Behavior, and Specifying the Ta...
			3.1.2 Step 4: Understand the Behavior-Identify What Needs to Change
			3.1.3 Step 4a: Identify What Needs to Change Using the Theoretical Domains Framework
			3.1.4 Step 5: Identify Intervention Options-Functions
			3.1.5 Step 6: Identify Intervention Options-Policy Categories
			3.1.6 Step 7: Identify Content and Implementation Options (Behavior Change Techniques)
			3.1.7 Step 8: Identify Content and Implementation Options (Mode of Intervention Delivery)
	4 Evaluation of Behavior Change Interventions
		4.1 Outcome Evaluation
			4.1.1 Lessons from Implementation Science on Methods to Evaluate Primary Outcomes in BCIs
			4.1.2 Evaluation of Secondary Outcomes
				Patient or Population Outcomes
				Health System Outcomes
				Cost Outcomes
	5 Process Evaluation
		5.1 Lessons Learned from Implementation Science: How to Evaluate Process Outcomes in BCIs
			5.1.1 Contextual Factors
			5.1.2 Implementation Fidelity
			5.1.3 Causal Mechanisms
	6 Lessons Learnt from Implementation Projects in a Provincial Context
		6.1 Initial Assessment of Theory-Inspired BCIs Developed Without Formal Behavioral Analysis
			6.1.1 Description of the Audit and Feedback  BCI
			6.1.2 Evaluation of the Audit and Feedback  BCI
		6.2 Lessons Learned
		6.3 QCNL-Supported Theory-Based BCIs Developed Using Behavioral Analysis
	7 Summary
	References
Chapter 32: Changing Health-Related Behaviors 4: Realizing Impact of Health Research Through Knowledge Translation
	1 What Is Knowledge Translation?
	2 Why Is Knowledge Translation Important?
	3 Knowledge Users
	4 The Difference Between Integrated and End-of-Grant Knowledge Translation
	5 Knowledge Translation Planning
	6 Common KT Challenges
		6.1 Failure to Include Knowledge Translation in Project Budget
		6.2 Culture of Publications and Presentations
			6.2.1 Example
		6.3 Making Assumptions About Knowledge User Preferences
			6.3.1 Example
	7 Examples of Knowledge Translation in Action
		7.1 Tackling Antibiotics Overuse
		7.2 Resource for Metastatic Breast Cancer Patients
		7.3 Peripheral Artery Disease Testing
		7.4 Young Adult Cancer Canada and the YAC Prime Study
		7.5 Exploring Arts-Based Knowledge Translation
	8 Summary
	References
Chapter 33: Changing Health-Related Behaviours 5: On Interventions to Change Physician Behaviours
	1 Introduction
	2 Behaviour Change Interventions
		2.1 Strategies
		2.2 Theoretical Framework
		2.3 Description of Interventions
			2.3.1 Academic Detailing/Educational Outreach
			2.3.2 eTechnology
			2.3.3 Audit and Feedback
			2.3.4 Reminders
			2.3.5 Continuing Medical Education Modules (CME)
			2.3.6 Practice Facilitation
			2.3.7 Financial Incentives
	3 Policy Changes
		3.1 Health Quality Councils
	4 Changing Physicians´ Behaviours
	5 Conclusion
	References
Chapter 34: Changing Health-Related Behaviors 6: Analysis, Interpretation, and Application of Big Data
	1 Introduction
	2 Historical Perspective
		2.1 Definition
		2.2 Data and Health
			2.2.1 Genetic Information
			2.2.2 Complex Behavior and Learning
			2.2.3 Scientific Method
			2.2.4 Big Data
		2.3 Big Data System
			2.3.1 Data Collection
			2.3.2 Processing
			2.3.3 High-Performance Computing (HPC)
			2.3.4 Predictive Models, Machine Learning, and Artificial Intelligence
			2.3.5 Security
	3 Data Sources
		3.1 Encounters
		3.2 Omics
		3.3 Activity Trackers and Other Connected Devices
		3.4 Social Media
	4 Issues
		4.1 Data Quality and Quantity
		4.2 Integration, Operation, Maintainability
		4.3 Value
		4.4 Explainability and Interpretability
		4.5 Errors
		4.6 Social Biases
		4.7 Regulatory and Ethical
	5 Use  Case
	6 Conclusion
	References
Index