This unique collection of chapters from world experts on person-centered outcome (PCO) measures addresses the following critical questions: Can individual experiences be represented in measurements that do not reduce unique differences to meaningless uniformity? How person-centric are PCO measures? Are PCO measurements capable of delivering the kind of quality assured quantification required for high-stakes decision making? Are PCO measures likely to support improved health care delivery? Have pivotal clinical studies failed to deliver treatments for diseases because of shortcomings in the PCO measures used? Are these shortcomings primarily matters of precision and meaningfulness? Or is the lack of common languages for communicating outcomes also debilitating to quality improvement, research, and the health care economy? Three key issues form an urgent basis for further investigation. First, the numbers generated by PCO measures are increasingly used as the central dependent variables upon which high stakes decisions are made. The rising profile of PCO measures places new demands for higher quality information from scale and test construction, evaluation, selection, and interpretation. Second, PCO measurement science has well-established lessons to be learned from those who have built and established the science over many decades. Finally, the goal in making a PCO measurement is to inform outcome management. As such, it is vitally important that key stakeholders understand that, over the last half century, developments in psychometrics have refocused measurement on illuminating clinically important individual differences in the context of widely reproduced patterns of variation in health and functioning, comparable scale values for quality improvement, and practical explanatory models.
This book’s audience includes anyone interested in person-centered care, including healthcare researchers and practitioners, policy makers, pharmaceutical industry representatives, clinicians, patient advocates, and metrologists.
This is an open access book.
Author(s): William P. Fisher Jr., Stefan J. Cano
Series: Springer Series in Measurement Science and Technology
Publisher: Springer
Year: 2023
Language: English
Pages: 406
City: Cham
Preface
Acknowledgments
Contents
Chapter 1: Ideas and Methods in Person-Centered Outcome Metrology
1.1 Introduction
1.2 The Chapters
1.3 Acknowledging and Incorporating Complexity
1.4 Concluding Comments
References
Chapter 2: A Clinician´s Guide to Performance Outcome Measurements
2.1 Introduction
2.2 Rasch Analysis Explained and Why Clinician Involvement Is Key
2.2.1 Guttman Principle
2.2.2 The North Star Ambulatory Assessment
2.3 Top Tips When Choosing Which Scale of Performance to Use
2.4 The Future: Telemedicine?
References
Chapter 3: Measuring Health-Related Quality of Life in Dementia
3.1 The Challenge of Measuring HRQL in Dementia
3.1.1 The Nature of Dementia
3.1.2 Requirements for Rigorous Measurement of HRQL
3.1.3 Four Challenges to Robust Measurement of HRQL in Dementia
3.1.3.1 HRQL Is Subjective
3.1.3.2 Questionnaires Are Cognitively Demanding
3.1.3.3 Proxy-Report Is Sometimes Necessary
3.1.3.4 Self- and Proxy-Reports Are Scaled on Different Metrics
3.2 Responses to the Challenge
3.2.1 Proxy Reported Instruments
3.2.2 Self-Reported Instruments
3.2.3 Instruments with Both Self- and Proxy-Reported Forms
3.2.4 Psychometric Approaches
3.3 Benefits of Using Methods Based on Rasch Measurement Theory (RMT) for HRQL in Dementia
3.3.1 Diagnostic Information About the Instrument
3.3.2 Equating HRQL Scores
3.3.3 Quantifying and Understanding Impact on HRQL
3.4 The Example of DEMQOL and DEMQOL-Proxy
3.4.1 Robust Scales for Use at the Individual Level
3.4.2 A Solution to the Proxy Problem
3.4.3 Clear Qualitative Understanding of Statistical Change
3.5 Conclusions
References
Chapter 4: Improving Clinical Practice with Person-Centered Outcome Measurement
4.1 Introduction
4.1.1 Case A
4.1.2 Person-Centeredness
4.1.3 Context for Choosing Measures
4.2 Competing Priorities: Personalization Versus Standardization
4.2.1 Equality and Equity
4.2.2 Frameworks for Assessing Effects of Health Conditions
4.2.3 Case A, as Informed by the ICF Framework (Fig. 4.2) and Systems Model
4.3 Competing Priorities: Satisfaction Versus Effectiveness
4.3.1 Distinguishing Satisfaction from Effectiveness
4.3.2 Person-Centered But Not Person-Reported: Health and Disease Biomarkers
4.3.3 Person-Centered Disability as a Measure of Effectiveness
4.3.4 Person-Reported Effectiveness: Minimizing Adverse Conditions
4.3.5 Person-Reported Mediators and Measures of Effectiveness
4.3.6 Case A, as Informed by Satisfaction and Effectiveness Measures
4.4 Competing Priorities: Scientific Rigor Versus Practical Convenience
4.4.1 Ideal Measurement
4.4.2 Accessibility and Convenience
4.4.3 When Practical Convenience Means Telehealth
4.4.4 Case A, Navigating Scientific Rigor and Practical Convenience
4.5 Summary and Recommendations
References
Chapter 5: An Adaptive Strategy for Measuring Patient-Reported Outcomes: Incorporating Patient Preferences Relevant to Cost-Be...
5.1 Introduction
5.2 A Person-Centered Measure for Vision Rehabilitation
5.2.1 A Measurement Model for Visual Ability
5.2.2 Defining and Organizing the Activity Inventory Item Content
5.2.3 Adaptive Administration of the AI
5.2.4 Properties of Estimated AI Item and Person Measures at Baseline
5.3 Functional Domains and Differential Person Functioning (DPF)
5.3.1 Latent Variable Model for Sources of Variance in AI Visual Ability Measures
5.4 Intervention-Specific Differential Item Functioning (DIF)
5.4.1 Increasing Functional Reserve
5.4.2 Rehabilitation Demand and Item Filtering
5.4.3 Utility to the Patient of Increasing Functional Reserve
5.4.4 Social Utility of AI Goals
5.4.5 Utility of Vision Rehabilitation Outcomes
5.4.6 Extension of the Utility Model to Estimation of Net Gain from Vision Rehabilitation
5.5 Visual Ability Outcomes of Vision Rehabilitation
5.5.1 Continuous Visual Ability Outcome Measure: Average Change in Functional Reserve
5.5.2 Minimum Clinically Important Difference in Visual Ability as a Clinical Endpoint
5.5.3 Reducing Rehabilitation Demand: Net Gain from Vision Rehabilitation
5.5.4 Next Steps in the Development of Preference-Based Patient-Centered Outcome Measures for Vision Rehabilitation
References
Chapter 6: Functional Binocular Vision: Toward a Person-Centered Metric
6.1 Vision and Reading
6.2 Diagnosing Binocular Vision Problems
6.3 Defining Functional Binocular Vision (FBV)
6.4 Methods
6.5 Measurement Models
6.6 Overall Scaling Results
6.7 Changes in FBV with Intervention
6.8 Relationship of FBV to Reading in School Children
6.8.1 FBV Measurements Predict Reading Outcomes
6.9 Limitations
6.10 Conclusions and Future Directions
References
Chapter 7: Advancing the Metrological Agenda in the Social Sciences
7.1 Building a Foundation
7.2 The Need for a Universal Measurement Scale for a Variable
7.2.1 Our Objective: Combining Local Measurement Scales
7.2.2 Constructing a Local Measurement Scale for a Latent Variable
7.2.3 Equating Sample 2 of Clients
7.2.4 Equating Sample 3 of Clients
7.2.5 Virtual Equating
7.2.6 Confirming the Phobia Scale
7.3 Measuring with the Benchmark Scale
7.3.1 A Phobia Intensity ``Ruler´´
7.4 Discussion
References
Chapter 8: Equating Measuring Instruments in the Social Sciences: Applying Measurement Principles of the Natural Sciences
8.1 Introduction
8.1.1 A Complex Social Science Context
8.1.2 Empirical Understanding and the Role of the Rasch Model for Measurement
8.1.3 Descriptions of Measurement
8.1.4 Measurement of Variables with No Physical Counterpart
8.1.5 Structure of This Chapter
8.2 The Rasch Model and Distribution
8.2.1 The Expected Value Curve and the Equating Function
8.2.2 The Rasch Distribution of Uncertainty
8.2.3 The Unit in the Rasch Distribution
8.2.4 The Rasch Distribution of Measurement Uncertainty
8.2.5 Maximum Likelihood Estimates of the Instrument Parameters in the Rasch Model
8.2.6 Profile Analysis and Editing of Profiles
8.2.7 Person Estimates
8.3 An Illustrative Example
8.3.1 The Raw Scores on the Instruments
8.3.2 The Equating Functions
8.3.3 The Equated Scores to Measurements on a Standard Instrument
8.4 Summary and Discussion
References
Chapter 9: Addressing Traceability in Social Measurement Establishing a Common Metric for Dependence
9.1 Introduction
9.2 Recourse to Literature
9.2.1 The Significance of Scientific Measurement
9.2.2 Measurement in the Social Sciences
9.2.2.1 The Challenge of Social Measurement
9.2.2.2 The Path Toward Quantitative Social Sciences
9.2.2.3 Measurement by Assigning Numerals
9.2.2.4 The Quest for a Measurement Model for the Social Sciences
9.2.3 The Rasch Model for Measurement in the Social Sciences
9.2.3.1 From Population-Based Score Statistics to Invariant Measurement
9.2.3.2 Accounting for Measurement Requirements
9.2.3.3 Rasch Measurement Theory as a Framework for Quantification in the Social Sciences
9.3 Metrology in the Social Sciences
9.3.1 Metrological Traceability
9.3.2 Creating Measurement Systems
9.3.3 Uncertainty
9.4 Illustrative Example: Measurement of Self-Reported Nicotine Dependence
9.4.1 Purpose
9.4.2 Background and Literature
9.4.3 Development of the ABOUT-Dependence Instrument
9.4.4 Study Design, Data Sources and Sampling
9.4.5 Psychometric Methods
9.4.6 Results of Psychometric Analyses
9.4.7 Addressing Traceability: Method
9.4.8 Addressing Traceability: Establishing a Crosswalk
9.4.9 Comparison of Predicted and Observed Scores on the Two Instruments
9.4.10 Measurement Uncertainty of Self-Reported Dependence
9.5 Discussion
9.6 Conclusion
References
Chapter 10: The Role of Construct Specification Equations and Entropy in the Measurement of Memory
10.1 Introduction
10.2 Definitions
10.3 Methods for Testing Theories of What Is Being Measured
10.3.1 Different Levels of Causality in the Measurement Mechanism and in Constructs
10.3.2 Construct Specification Equations (CSEs) as Substantive Construct Theories
10.3.2.1 Construct Specification Equations (CSEs) and Validity
10.3.2.2 Construct Specification Equations and Construct Modelling
10.3.3 Construct Specification Equations and Metrology
10.3.4 Formulation of Construct Specification Equations
10.3.4.1 Measurement Uncertainties of Construct Specification Equations
10.3.4.2 Measurement Uncertainty for β-Coefficients in CSE
10.3.4.3 Measurement Uncertainty in the zR Yielded from the CSE
10.3.5 Construct Specification Equation: Ad-hoc or Pre-defined?
10.4 Theories Explaining Attributes of Interest
10.4.1 Entropy in General Terms
10.4.2 Entropy, Measurement Uncertainties and Validity
10.4.3 Entropy, Measurement System and Rasch Measurement Theory
10.4.4 Entropy to Explain Memory Tasks
10.4.5 Entropy to Explain Person Abilities
10.5 Examples and Illustrations in Memory Measurements
10.5.1 Memory Measurements
10.5.1.1 The Broad Picture of Cognition and Mental Processes
10.5.1.2 Neurodegenerative Diseases and Memory Measures
10.5.2 Subjects and Data Analyses
10.5.3 Explaining Memory Task Difficulty
10.5.3.1 Case 1: Tapping Recall
10.5.3.2 Case 2: Digit Recall
10.5.3.3 Case 3: Taps and Digits Combined
10.5.4 Explaining Person Memory Ability
10.6 Limitations and Implications in Interpreting CSEs
10.7 Chapter Summary, Strengths and Future Recommendations
Appendix: PCR Algorithms
References
Chapter 11: Assuring Measurement Quality in Person-Centered Care
11.1 Introducing Quality-Assurance of Measurement in Person-Centered Care
11.1.1 Opening the Quality-Assurance Loop
11.1.2 Quality Assurance in Person-Centered Care. Design of Experiments
11.1.3 A: Entity Attribute Description and Specification
11.1.4 Quality Assurance in Healthcare Service Provision
11.1.5 Examples of Quality Characteristics and Specification Limits for Person-Centered Care
11.1.5.1 PCC Including Physical, Psychological and Social Integrity. Specification Limits, Counted Fractions, Ability, Difficu...
11.1.5.2 PCOs: Example Neuropsychological Cases
11.1.5.3 PCOs: Example Patient Participation
11.2 Benefits of Combining Rasch Measurement Theory (RMT) and Quality Assurance
11.2.1 Measurement Quality-Assurance Loop
11.2.2 Measurement Specifications
11.2.2.1 Requirements for Traceable Measurement
11.2.3 A Way Forward for Measuring PCOs: Human as a B: Measurement Instrument
11.2.4 Benefits of Analysing Response Data with RMT
11.3 Metrological References for Comparability via Traceability and Reliable Estimates of Uncertainty
11.3.1 Metrological References for Comparability via Traceability
11.3.1.1 Reference Measurement Procedures: Construct Specification Equations as Recipes for Traceability
11.3.2 Errors and Uncertainties in PCOs
11.3.2.1 The Peculiar Sensitivity of a Human as a Measurement Instrument
11.3.2.2 Errors and Uncertainty. Reliability
11.3.2.3 Discrimination
11.3.2.4 Measurement Uncertainty and Measurement System Analysis
11.4 Decision Risks and Uncertainty
11.4.1 Comparing Test Result with Product Requirement
11.4.1.1 Requirements for Decision Risk Management
11.4.2 C: Man as an Operator: Rating the Rater
11.4.2.1 Rater Constructs
11.4.2.2 Consumer and Provider Risks
11.4.3 Receiver Operating Characteristics: A Human as an Operator & Rating the Rater
11.4.3.1 Explaining Clinical Diagnoses
11.4.4 Many-Body Modelling and Conclusions
References
Chapter 12: Measurement Systems, Brilliant Processes, and Exceptional Results in Healthcare: Untapped Potentials of Person-Cen...
12.1 Introduction: The Role of Measurement in the Shift to Quality in Health Care
12.2 Modern Statistical Approaches vs. Unmodern Metrological Approaches
12.3 Creating Contexts in Health Care for Success in Lean Thinking
12.4 Tightening the Focus on Metrological Potentials in Health Care
12.5 Discussion
12.6 Conclusion
References
Author Index
Subject Index
