TITLE: Clinical Trial Data Analysis Using R & SAS
SPEAKERS: Professor Din Chen, UNC & Pinggao Zhang, PhD, Takeda
MODERATOR: Walter Young


LEARNING OBJECTIVES: To understand and critique the major methodological issues in outcomes research on the development and validation of patient-reported outcomes, traditional meta-analysis and network meta-analysis, and health economic analysis.

Names and Addresses of Instructors: Joseph C. Cappelleri, Pfizer Inc, 445 Eastern Point Road, MS 8260-2502, Groton, CT 06340; e-mail:; phone: (860) 389-8107.

Thomas Mathew, University of Maryland Baltimore County, Department of Mathematics and Statistics, 1000 Hilltop Circle, Baltimore, MD 21250; e-mail:; phone: (410) 455-2418.


Session Description/Outline:


Based in part on the recently published co-edited volume Statistical Topics in Health Economics and Outcomes Research, this four-hour short course recognizes that, with ever-rising healthcare costs, evidence generation through health economics and outcomes research (HEOR) plays an increasingly important role in decision-making about the allocation of resources. This course highlights three major topics related to HEOR, with objectives to learn about 1) patient-reported outcomes, 2) analysis of aggregate data, and 3) methodological issues in health economic analysis. Key themes on patient-reported outcomes are presented regarding their development and validation: content validity, construct validity, exploratory factor analysis, confirmatory factor analysis, person-item maps, and reliability. Regarding analysis of aggregate data, several areas are elucidated: traditional meta-analysis, network meta-analysis, model validation, meta-regression, and best practices for the conduct and reporting of aggregated data. For methodological issues on health economic analysis, cost-effectiveness criteria are covered: traditional measures of cost-effectiveness, the cost-effectiveness acceptability curve, statistical inference for cost-effectiveness measures, the fiducial approach (or generalized pivotal quantity approach), and a probabilistic measure of cost-effectiveness. Examples are illustrated throughout the course to complement the concepts. Attendees are expected to have at least basic quantitative knowledge.


  1. Patient-Reported Outcomes
    • Introduction
    • Development of a Patient-Reported Outcome
    • Validity
      • Content Validity
      • Variations of Construct Validity
      • Factors Affecting Response
    • Reliability
      • Intraclass Correlation Coefficient (ICC) for Continuous Variables
      • ICC Illustrations
      • Bland and Altman Plot for Continuous Variables
      • Simple Kappa and Weighted Kappa Coefficients for Categorical Variables
      • Internal Consistency Reliability: Cronbach’s Alpha Coefficient

1.5. Exploratory and Confirmatory Factor Analyses

1.5.1 Concepts
1.5.2 Illustrations

1.6. Person-Item Maps

1.6.1 Rasch Model
1.6.2 Concepts and Illustrations

  1. Aggregate Data
    • Traditional Meta-Analysis
    • Fixed-Effect and Random-Effects Models
    • Types of Outcomes
    • Network Meta-Analysis
    • Assumptions
    • Guidances


  1. Health Economics
    • Introduction
    • Cost-Effectiveness Criteria and Statistical Inference
      • Traditional Measures of Cost-Effectiveness
      • Cost-Effectiveness Acceptability Curve
      • Statistical inference for Cost-Effectiveness Measures
      • Fiducial or Generalized Pivotal Quantity (GPQ) Approach
      • Example
      • Probabilistic Measure of Cost-Effectiveness



Alemayehu D, Mathew T, Willke RJ. “Methodological Isses in Health Economic Analysis.” In: Alemayehu D, Cappelleri JC, Emir B, Zou KH (co-editors). Statistical Topics in Health Economics and Outcomes Research. Boca Raton, Florida: Chapman & Hall/CRC Press; 2017: 85-149.

Alemayehu D, Bushmakin AG, Cappelleri JC. “Analysis of Aggregate Data.” In: Alemayehu D, Cappelleri JC, Emir B, Zou KH (co-editors). Statistical Topics in Health Economics and Outcomes Research. Boca Raton, Florida: Chapman & Hall/CRC Press; 2017:123-149.

Bebu I, Luta G, Mathew T, Kennedy TA, Agan BK. Parametric cost-effectiveness inference with skewed data. Computational Statistics and Data Analysis. 2016; 94:210–220.

Bebu I, Mathew T, Lachin JM. Probabilistic measures of cost-effectiveness. Statistics in Medicine. 2016; 35:3976-3986.

Cappelleri JC, Bushmakn AG, Alvir JMJ. “Patient-Reported Outcomes: Development and Validation.” In: Alemayehu D, Cappelleri JC, Emir B, Zou KH (co-editors). Statistical Topics in Health Economics and Outcomes Research. Boca Raton, Florida: Chapman & Hall/CRC Press; 2017:15-46.


Speaker Bio:

Joseph C. Cappelleri, PhD, MPH, MS is an executive director in the Statistical Research and Data Science Center at Pfizer Inc. He earned his M.S. in statistics from the City University of New York, Ph.D. in psychometrics from Cornell University, and M.P.H. in epidemiology from Harvard University. As an adjunct professor, Dr. Cappelleri 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 (over 1,200 co-authored publications and conference presentations), including on regression-discontinuity designs, meta-analyses, and health measurement scales. He is lead author of the book Patient-Reported Outcomes: Measurement, Implementation and Interpretation, co-author of the monograph Phase II Clinical Development of New Drugs, and co-edited the volume Statistical Topics in Health Economics and Outcomes Research. Dr. Cappelleri is a Fellow of the American Statistical Association.


Thomas Mathew, PhD, is Professor, Department of Mathematics & Statistics, University of Maryland Baltimore County (UMBC). He earned his PhD in statistics from the Indian Statistical Institute in 1983, and has been a faculty member at UMBC since 1985. He has delivered numerous conference presentations, nationally and internationally, and has published extensively on methodological and applied topics, including cost-effectiveness analysis, bioequivalence testing, exposure data analysis, meta-analysis, mixed and random effects models, and tolerance intervals. He is the co-author of two books Statistical Tests in Mixed Linear Models and Statistical Tolerance Regions: Theory, Applications and Computation, both published by Wiley. He has served on the Editorial Boards of several journals, and is currently an Associate Editor of Sankhya and the Journal of the American Statistical Association. Dr. Mathew is a Fellow of the American Statistical Association, and a Fellow of the Institute of Mathematical Statistics. He has also been appointed as Presidential Research Professor at his campus.


Target Audience: Statisticians, data scientists, epidemiologists, outcomes researchers, health economists, and healthcare policy and decision-makers. At least 50 attendees are expected.

Audio-Visual Equipment: One screen, one projector, and one lavaliere microphone.

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