Patient-Reported Outcomes: Measurement, Implementation and Interpretation, 1st Edition

  • Joseph C. Cappelleri
  • Published By: Chapman & Hall
  • ISBN-10: 1439873704
  • ISBN-13: 9781439873700
  • DDC: 610.72
  • Grade Level Range: College Freshman - College Senior
  • 352 Pages | eBook
  • Original Copyright 2013 | Published/Released March 2016
  • This publication's content originally published in print form: 2013

  • Price:  Sign in for price

About

Overview

Advancing the development, validation, and use of patient-reported outcome (PRO) measures, Patient-Reported Outcomes: Measurement, Implementation and Interpretation helps readers develop and enrich their understanding of PRO methodology, particularly from a quantitative perspective. Designed for biopharmaceutical researchers and others in the health sciences community, it provides an up-to-date volume on conceptual and analytical issues of PRO measures.

The book discusses key concepts relating to the measurement, implementation, and interpretation of PRO measures. It covers both introductory and advanced psychometric and biostatistical methods for constructing and analyzing PRO measures. The authors include many relevant real-life applications based on their extensive first-hand experiences in the pharmaceutical industry. They implement a wealth of simulated datasets to illustrate concepts and heighten understanding based on practical scenarios. For readers interested in conducting statistical analyses of PRO measures and delving more deeply into the analytic details, most chapters contain SAS code and output that illustrate the methodology. Along with providing numerous references, the book highlights current regulatory guidelines.

Table of Contents

Front Cover.
Half Title Page.
Other Frontmatter.
Title Page.
Copyright Page.
Contents.
Preface.
Disclosure and Acknowledgment.
Authors.
Introduction.
1: Patient-Reported Outcomes in Perspective.
2: Patient-Reported Outcomes in Clinical Research.
3: Terms and Definitions.
4: Measurement Scales.
5: Psychometrics Versus Clinimetrics.
6: Selection of a PRO Questionnaire.
7: Summary.
8: References.
Development of a Patient-Reported Outcome.
9: Population.
10: Item Generation.
11: Item Wording.
12: Cognitive Interviews.
13: Summary.
14: References.
Validity.
15: Content Validity.
16: Construct Validity.
17: Simulated Example Using SAS: Convergent and Divergent Validity.
18: Factors Affecting Response.
19: Summary.
20: References.
Reliability.
21: Intraclass Correlation Coefficient for Continuous Variables.
22: ICC Example.
23: ICC Simulated Example.
24: ICC in Context.
25: Bland and Altman Plot for Continuous Variables.
26: Simple Kappa and Weighted Kappa Coefficients for Categorical Variables.
27: Internal Consistency Reliability: Cronbach's Alpha Coefficient.
28: Simulated Example of Cronbach's Alpha.
29: Summary.
30: References.
Exploratory and Confirmatory Factor Analyses.
31: Exploratory Factor Analysis.
32: Confirmatory Factor Analysis.
33: Causal Indicators versus Effect Indicators.
34: Simulated Examples Using SAS: Exploratory Factor Analysis.
35: Simulated Examples Using SAS: Confirmatory Factor Analysis.
36: Real-Life Examples.
37: Summary.
38: References.
Item Response Theory.
39: Classical Test Theory Revisited.
40: Assumptions of IRT.
41: Item Characteristic Curves.
42: Item Information.
43: Item Fit and Person Fit.
44: Differential Item Functioning.
45: Sample Size.
46: Example.
47: Example: Rasch Model Implementation.
48: Summary.
49: References.
Cross-Sectional Analysis.
50: Types of PRO Data and Exploratory Methods.
51: Comparing Two or More Samples.
52: Regression Analysis.
53: Summary.
54: References.
Longitudinal Analysis.
55: Analytic Considerations.
56: Repeated Measures Model.
57: Random Coefficient Model.
58: Real-Life Examples.
59: Summary.
60: References.
Mediation Models.
61: Single Mediator Model.
62: Model Invariance.
63: Advanced Example.
64: Bootstrapping Methodology Implementation.
65: Summary.
66: References.
Missing Data.
67: Study Design to Minimize Missing Data.
68: Missing Data Patterns and Mechanisms.
69: Approaches for Missing Items within Domains or Measures.
70: Approaches for Missing Entire Domains or Entire Questionnaires.
71: Sensitivity Analyses.
72: Simulated Example Using SAS: Pattern Mixture Models.
73: Summary.
74: References.
Enriching Interpretation.
75: Anchor-Based Approaches.
76: Distribution-Based Approaches.
77: Multiple Testing.
78: Summary.
79: Acknowledgment.
80: References.