Patient-Reported Outcomes Measurement, Implementation and Interpretation

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.

IntroductionPatient-Reported Outcomes in PerspectivePatient-Reported Outcomes in Clinical ResearchTerms and DefinitionsMeasurement ScalesPsychometrics vs ClinimetricsSelection of a PRO Questionnaire Development of a Patient-Reported OutcomePopulationItem GenerationItem WordingCognitive Interviews ValidityContent ValidityConstruct ValiditySimulated Example Using SAS: Convergent and Divergent ValidityFactors Affecting Response ReliabilityIntraclass Correlation Coefficient for Continuous VariablesICC ExampleICC Simulated ExampleICC in ContextBland and Altman Plot for Continuous VariablesSimple Kappa and Weighted Kappa Coefficients for Categorical VariablesInternal Consistency Reliability: Cronbach’s Alpha CoefficientSimulated Example of Cronbach’s Alpha Exploratory and Confirmatory Factor AnalysesExploratory Factor AnalysisConfirmatory Factor AnalysisCausal Indicators vs Effect IndicatorsSimulated Examples Using SAS: Exploratory Factor AnalysisSimulated Examples Using SAS: Confirmatory Factor AnalysisReal-Life Examples Item Response TheoryClassical Test Theory RevisitedAssumptions of IRTItem Characteristic CurvesItem InformationItem Fit and Person FitDifferential Item FunctioningSample SizeExampleExample: Rasch Model Implementation Cross-Sectional AnalysisTypes of PRO Data and Exploratory MethodsComparing Two or More SamplesRegression Analysis Longitudinal AnalysisAnalytic ConsiderationsRepeated Measures ModelRandom Coefficient ModelReal-Life Examples Mediation ModelsSingle Mediator ModelModel InvarianceAdvanced ExampleBootstrapping Methodology Implementation Missing DataStudy Design to Minimize Missing DataMissing Data Patterns and MechanismsApproaches for Missing Items within Domains or MeasuresApproaches for Missing Entire Domains or Entire QuestionnairesSensitivity AnalysesSimulated Example Using SAS: Pattern Mixture Models Enriching InterpretationAnchor-Based ApproachesDistribution-Based ApproachesMultiple Testing Index A Summary and References appear at the end of each chapter.