A Practical Approach to Quantitative Validation of Patient-Reported Outcomes E-Book

Andrew G. Bushmakin

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Statistics in Practice
• Sprache: Englisch

Seitenzahl: 563

Veröffentlichungsjahr: 2022

Beschreibung

A Simulation-Based Guide Using SAS In A Practical Approach to Quantitative Validation of Patient-Reported Outcomes, two distinguished researchers, with 50 years of collective research experience and hundreds of publications on patient-centered research, deliver a detailed and comprehensive exposition on the critical steps required for quantitative validation of patient-reported outcomes (PROs). The book provides an incisive and instructional explanation and discussion on major aspects of psychometric validation methodology on PROs, especially relevant for medical applications sponsored by the pharmaceutical industry, where SAS is the primary software, and evaluated in regulatory and other healthcare environments. Central topics include test-retest reliability, exploratory and confirmatory factor analyses, construct and criterion validity, responsiveness and sensitivity, interpretation of PRO scores and findings, and meaningful within-patient change and clinical important difference. The authors provide step-by-step guidance while walking readers through how to structure data prior to a PRO analysis and demonstrate how to implement analyses with simulated examples grounded in real-life scenarios. Readers will also find: * A thorough introduction to patient-reported outcomes, including their definition, development, and psychometric validation * Comprehensive explorations of the validation workflow, including discussions of clinical trials as a data source for validation and the validation workflow for single and multi-item scales * In-depth discussions of key concepts related to a validation of a measurement scale * Special attention is given to the US Food and Drug Administration (FDA) guidance on development and validation of the PROs, which lay the foundation and inspiration for the analytic methods executed A Practical Approach to Quantitative Validation of Patient-Reported Outcomes is a required reference that will benefit psychometricians, statisticians, biostatisticians, epidemiologists, health service and public health researchers, outcome research scientists, regulators, and payers. STATISTICS IN PRACTICE A series of practical books outlining the use of statistical techniques in a wide range of applications areas: * HUMAN AND BIOLOGICAL SCIENCES * EARTH AND ENVIRONMENTAL SCIENCES * INDUSTRY, COMMERCE AND FINANCE

Leseprobe

A Practical Approach to Quantitative Validation of Patient‐Reported Outcomes

A Simulation‐Based Guide Using SAS

Andrew G. Bushmakin and Joseph C. Cappelleri

Statistical Research and Data Science Center Pfizer Inc.

This edition first published 2023© 2023 John Wiley & Sons, Inc.

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Library of Congress Cataloging‐in‐Publication Data

Names: Bushmakin, Andrew G., author. | Cappelleri, Joseph C., author.Title: A practical approach to quantitative validation of patient‐reported outcomes : a simulation‐based guide using SAS / Andrew G. Bushmakin and Joseph C. Cappelleri.Description: Hoboken, NJ : Wiley, 2023. | Includes bibliographical  references and index.Identifiers: LCCN 2022024236 (print) | LCCN 2022024237 (ebook) | ISBN  9781119376378 (cloth) | ISBN 9781119376316 (adobe pdf) | ISBN  9781119376309 (epub)Subjects: MESH: Patient Reported Outcome Measures | Patient Outcome  Assessment | Reproducibility of Results | Computer Simulation | Clinical Outcome AssessmentClassification: LCC R853.Q34 (print) | LCC R853.Q34 (ebook) | NLM W 84.41  | DDC 610.72/1–dc23/eng/20220706LC record available at https://lccn.loc.gov/2022024236LC ebook record available at https://lccn.loc.gov/2022024237

Cover Design: WileyCover Image: Courtesy of Andrew Bushmakin

Disclosure

Andrew G. Bushmakin and Joseph C. Cappelleri are employees of Pfizer Inc. This book is written for educational and instructional purposes, with emphasis on the methodology of quantitative validation of patient‐reported outcomes. Views and opinions expressed in this book are the authors’ own and do not necessarily reflect those of Pfizer Inc.

Preface

This book is organized as one volume with interconnected chapters, with each chapter devoted to the methodology of assessments of specific measurement properties of clinical outcome assessments (COAs), which include patient‐reported outcomes (PRO), clinician‐reported outcomes (ClinRO), observer‐reported outcomes (ObsRO), and performance outcome assessments (PerfO). In covering the topics, we made a considerable effort to illustrate the methodology with an extensive number of simulated examples, motivated by and grounded in our experience with practical applications, covering all key topics of the quantitative validation of a COA scale. All simulations are conducted in SAS, the primary software used in the pharmaceutical industry.

Chapter 1 discusses qualitative research including concept identification, item development, cognitive interviews, and other steps in the instrument development process. It is assumed that content validity for the COA instrument of interest has been achieved, and, in doing so, it covers the important concepts of the unobservable or latent attribute under study that the instrument purports to measure. Hence, the content of the PRO (or COA) instrument is taken as an adequate reflection of the construct to be measured. Given this, subsequent chapters focus on the quantitative validation of PRO measures in particular and, when applicable, to COAs in general.

Chapter 2 describes quantitative validation workflows that should be applicable for most realistic scenarios and study designs. The chapter elucidates the distinctive opportunities and challenges when using clinical trials data as the source to psychometrically validate a scale. There is, however, always a possibility that some new scale will need some adjustments to the workflows highlighted and discussed in this chapter.

Chapter 3 provides an overview of classical test theory (CTT) and compares CTT assumptions with the item response theory (IRT) model. A different paradigm on how we think about items in CTT vs. IRT is discussed and illustrated. The relationships between CTT‐based scoring and IRT‐based scoring are discussed. Based on several examples, this chapter illustrates that both theories are, in general, comparable in terms of the produced scores (which is an ultimate purpose of a measurement scale).

Chapter 4 covers test‐retest reliability and internal reliability. Test‐retest reliability is introduced based on the basic conventional measurement error model and is discussed in the context of a clinical study. Spearman–Brown prophecy formula is used to contrast the reliability of a single measurement with reliability of an average score. The other major section investigates the methodology behind Cronbach’s alpha for Likert‐type scales and also includes applications with dichotomous items.

Chapter 5 centers on construct validity. As a method to determine the factor structure of a scale, exploratory factor analysis is analyzed. As a way to test whether a measurement model of a scale fits the data, confirmatory factor analysis is examined. Methodological issues associated with both approaches are discussed at length. The chapter also describes such important properties as convergent and discriminant validity assessment. The longitudinal model for known‐groups validity assessment is introduced and detailed. A model using all available data from a clinical study for criterion validity is emphasized.

Chapter 6 centers on the ability to detect change property. An analytic model‐based implementation on the ability to detect change is presented, which allows to quantify the relationship between changes in the target PRO (or COA) scale and changes in the anchor (external) scale. Correlational analysis to support an instrument’s ability to detect change is investigated. It is shown that correlations between score changes on a pair of variables may provide only adjunct evidence on the ability to detect change on a target scale. The second theme of this chapter relates to an instrument’s sensitivity to treatment effects. A framework and an implementation of one unified multi‐domain longitudinal model, intended for a scale with multiple domains assessed over time, is discussed in detail.

Chapter 7 discusses the methods and challenges of assessments of meaningful within‐patient change (MWPC) and clinical important difference (CID) for a measurement scale. As with the other chapters, this chapter contains methods rooted in the current regulatory documents (especially from the US Food and Drug Administration) and in the existing and more recent literature. Applications of the MWPC and CID for interpretation of the results of treatment effects analyses are highlighted.

As authors, we sought to develop this book to be viewed as a comprehensive guide for all key steps that need to be undertaken in practice during the quantitative validation of a measurement scale. And, for this purpose, it is our aspiration that this monograph can serve as a single, thorough reference that will benefit readers in their research and understanding of the material.

Andrew G. BushmakinJoseph C. Cappelleri

About the Authors

Andrew G. Bushmakin earned his MS in applied mathematics and physics from the National Research Nuclear University (former Moscow Engineering Physics Institute, Moscow, Russia). He has more than 20 years of experience in mathematical modeling and data analysis. He is a director of biostatistics in the Statistical Research and Data Science Center at Pfizer Inc. He has co‐authored numerous articles and presentations on topics ranging from mathematical modeling of neutron physics processes to patient‐reported outcomes, as well as several monographs.

Joseph C. Cappelleri earned his MS in statistics from the City University of New York, PhD in psychometrics from Cornell University, and MPH in epidemiology from Harvard University. He is an executive director of biostatistics in the Statistical Research and Data Science Center at Pfizer Inc. As an adjunct professor, he has served on the faculties of Brown University, University of Connecticut, and Tufts Medical Center. He has delivered numerous conference presentations and has published extensively on clinical and methodological topics. He is a fellow of the American Statistical Association and recipient of the ISPOR Avedis Donabedian Outcomes Research Lifetime Achievement Award.