Clients draw on the expertise of our internal staff and academic experts specializing in statistics, econometrics, and biostatistics to conduct analyses related to statistical sampling, statistical design and analysis, and biostatistics and epidemiology.

Statistics and Sampling Capabilities

Our statistics and econometrics experts are skilled in developing, implementing, and analyzing a variety of statistical sampling designs, as well as testing the scientific validity of statistical samples. We also have expertise in using statistical samples to assess liability and extrapolate damages for the larger population based on various sampling designs. Cornerstone Research staff and affiliated experts have provided statistical sampling consulting and expert witness testimony in:

  • False Claims Act litigation and other government investigations in a variety of industries
  • Reimbursement disputes between healthcare providers and health insurance companies
  • Mortgage loan and automotive product liability cases in which defect rates were at issue
  • Employment discrimination and affirmative action university admissions cases

Cornerstone Research regularly performs complex statistical analysis in a wide range of litigation. We have expertise in a variety of statistical methods, including:

  • causal inference methods
  • statistical significance tests
  • parametric and non-parametric modeling
  • bootstrapping techniques
  • Monte Carlo simulation
  • survival analysis

We have employed advanced statistical techniques at the class certification and merits stages of large class actions and in government investigations, including work on mergers, competition matters, product liability litigation, and employment and admissions discrimination cases. In addition, we support statistics and econometrics experts in a range of industries, including automotive, securities and loans, healthcare, pharmaceuticals, consumer goods, and high tech.

See also Statistics, Simulations, and Optimization

Biostatistical and epidemiological issues are found in a wide range of litigation, including product liability, securities, and labor class actions. Our affiliated experts bring deep expertise in:

  • Assessing clinical trial design and conducting safety and efficacy analyses for drug candidates
  • Analyzing data from sources such as observational studies and post-marketing surveillance databases
  • Analyzing medical and prescription drug claims data
  • Conducting epidemiological analyses, including assessment of studies and data related to the COVID-19 pandemic.

See also Healthcare

Our expertise in statistics and sampling is supplemented by expertise in the science of big data. Our in-house team of programming specialists, along with our secure analytics and computational infrastructure, allows us to efficiently analyze large-scale data and develop sophisticated data approaches using artificial intelligence and machine learning.

See also Data Science Center: Artificial Intelligence and Machine Learning

Our statistics and econometrics expertise is often applied in litigation related to companies’ use of algorithmic processes to make business decisions in areas such as employment, advertising, consumer finance, and healthcare. Automated decision-making processes may generate disparate impact across groups and lead to allegations of algorithmic discrimination and bias. Our experts’ cutting-edge data analytics techniques and deep statistics expertise enable us to provide insightful analyses in addressing such claims.

See also Digital Economy: Labor, Employment, and Algorithmic Bias

Our extensive network includes top experts from academia and industry.

Our extensive network includes top experts from academia and industry.

Robert D. Gibbons

Blum-Riese Professor of Biostatistics,
Professor, Departments of Medicine, Psychiatry, and Public Health Sciences (Biostatistics);
Director, Center for Health Statistics;
University of Chicago

Robert Gibbons is a nationally recognized authority in the field of statistics, with major interest areas including biostatistics, environmental statistics, and psychometrics. He has testified on product liability and drug safety matters and addressed topics related to clinical trial design and results, observational study design and results, meta-analysis, statistical power, and damages.

Professor Gibbons is a Pritzker Scholar at the University of Chicago. He is a fellow of the American Statistical Association, where he also cofounded the Mental Health Statistics section. An elected member of the International Statistical Institute, the Royal Statistical Society, and the National Academy of Medicine of the National Academy of Sciences, he has received lifetime achievement awards from the American Public Health Association and Harvard University. Professor Gibbons is a founding director of the Center for Health Statistics at the University of Chicago, which applies advanced statistical theory to a variety of health sciences challenges.

Professor Gibbons has cowritten seven books and more than a dozen book chapters. He publishes widely on statistics and biostatistics, particularly on topics related to drug safety, mental health, and environmental statistics. He has authored or coauthored more than 300 research articles and collaborative scientific studies, and his work has appeared in the Journal of the American Statistical AssociationBiometricsStatistics in Medicine, the Journal of Clinical PsychiatryJAMA Psychiatry, and Environmental Science and Technology, among many other publications.

Professor Gibbons has served on the editorial boards of Health Services and Outcomes Research Methodology and JAMA Psychiatry and is an associate editor of the Harvard Data Science Review. His research on drug safety and other subjects has appeared in an array of media outlets, such as Forbes, the Lancet, the New York TimesScientific American, and the Washington Post.

An award-winning educator, Professor Gibbons has been recognized multiple times for excellence in teaching and research.

Our extensive network includes top experts from academia and industry.

Mark E. Glickman

Senior Lecturer on Statistics,
Department of Statistics,
Harvard University

Mark Glickman is an expert in applied statistics and statistical modeling. Dr. Glickman develops statistical and probability methods to address a variety of business challenges, notably those arising in health services. He analyzes issues related to health surveys, hospitalization, surgical disclosures, medical treatment efficacy, medication adherence, patient behaviors, survival analysis, and health outcomes.

Dr. Glickman has consulted to the Department of Justice and provided expert witness testimony in multiple matters, including in deposition, arbitration, and at trial. Dr. Glickman has applied statistical algorithms in a variety of consulting matters involving evaluating sampling methodology, testing representativeness of samples, predicting success of product launches, and estimating consumer preferences. He serves as an investigator/statistician at the Center for Healthcare Organization & Implementation Research (CHOIR) in the U.S. Department of Veterans Affairs.

In addition to his work on healthcare and medical topics, Dr. Glickman researches statistical models, including for rating competitors in games and sports. He invented the Glicko and Glicko-2 rating systems, both of which have been adopted by gaming and online gaming organizations internationally.

Dr. Glickman has published numerous peer-reviewed articles, including in the American Journal of Public Health, The American Statistician, and Harvard Data Science Review. He is a current associate editor and former editor-in-chief of the Journal of Quantitative Analysis in Sports, and served as an elected member of the American Statistical Association’s (ASA’s) board of directors. Dr. Glickman also chairs the ASA’s Committee on Data Science and Artificial Intelligence. In recognition of his achievements, he was elected Fellow of the ASA.

Dr. Glickman speaks internationally on topics related to statistics, biostatistics, data science, and Bayesian methodologies. He has delivered more than one hundred invited presentations at universities, professional meetings, and research institutions, such as the Yale School of Medicine, the International Statistical Institute’s World Statistics Congress, Los Alamos National Laboratory, the Royal Statistical Society, and the National Cancer Institute.

Before joining Harvard, Dr. Glickman served as Research Professor of Health Policy and Management at Boston University School of Public Health. He teaches courses in statistics and data science, and has received awards for teaching excellence. Dr. Glickman holds a Ph.D. in statistics from Harvard University.

Our extensive network includes top experts from academia and industry.

Blake B. McShane

Professor of Marketing,
Kellogg School of Management,
Northwestern University

Blake McShane is a statistics and quantitative marketing expert. Professor McShane develops and applies statistical models to address issues arising in a variety of fields, such as internet advertising, neuro- and sleep science, and sports. His expertise includes sampling and study design, statistical modeling and inference, and machine learning (ML). Professor McShane has been retained as an expert witness and testified in depositions regarding statistical sampling and damages issues.

Professor McShane has published numerous articles in leading academic journals, including the Journal of the American Statistical Association, the Journal of Consumer Research, the Journal of Marketing Research, Management Science, Psychological Science, and Nature.

In addition to serving on the editorial boards of several marketing and psychology journals, he is an associate editor of the Journal of the American Statistical Association and The American Statistician. He speaks regularly at academic and professional conferences focused on statistics, social and behavioral science, and consumer research and analytics.

For over a decade, Professor McShane has taught marketing and statistical methods courses to M.B.A. and Ph.D. students at Kellogg School of Management. He holds a Ph.D. in statistics from The Wharton School, University of Pennsylvania.

Our extensive network includes top experts from academia and industry.

Victoria Stodden

Associate Professor,
Daniel J. Epstein Department of Industrial & Systems Engineering,
University of Southern California

Victoria Stodden is an internationally recognized statistician and data scientist. Professor Stodden analyzes the reliability of scientific results, particularly in the context of sophisticated computational approaches to research. Her expertise includes statistical sampling and statistical data analyses, big data methods, the design and implementation of scientific validation systems, and openness standards for data and code sharing.

As part of her work to make data applications more transparent and verifiable, Professor Stodden developed the “Reproducible Research Standard,” a suite of open licensing recommendations for disseminating computational results. While at Yale Law School, she won the Kaltura Prize for Access to Knowledge for her research on legal issues in reproducible research and scientific innovation.

Professor Stodden has testified on scientific reproducibility before the Congressional House Committee on Science, Space & Technology and co-chaired the National Science Foundation’s Advisory Committee for Cyberinfrastructure. She has been a member of the National Science Foundation’s Advisory Committee for the Computer and Information Science and Engineering (CISE) Directorate. She has also served on several National Academies of Science, Engineering, and Medicine committees.

Professor Stodden has published more than fifty papers in academic journals and conference proceedings, and co-edited two books: Privacy, Big Data, and the Public Good: Frameworks for Engagement, and Implementing Reproducible Research. She has served as associate editor for the Harvard Data Science Review, the Annals of Applied Statistics, and on the editorial advisory boards of a number of other statistics and data science journals.

Before joining USC, Professor Stodden held visiting and permanent faculty positions at the University of California, Berkeley, Columbia University, and the University of Illinois at Urbana-Champaign, where she received tenure. She was a Kauffman Fellow in Law and Innovation at Yale Law School and a fellow at Harvard Law School’s Berkman Center for Internet & Society.

Professor Stodden holds a Ph.D. in statistics and a law degree from Stanford University. She teaches courses in data science, statistical theory, quantitative methods, and machine learning, among other topics.

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