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Independent Data Monitoring Committees in Clinical Trials

Independent Data Monitoring Committees in Clinical Trials

This course will teach you the statistical display and analysis methods used in monitoring clinical trials for safety, as well as the biases and pitfalls inherent in safety review.


Complex sample designs such as stratified cluster sampling make it possible to extract maximum information at minimum cost, but they are typically harder to work with than simple random samples. How do you analyze the resulting data – in particular, how do you determine margins of error? This course teaches you how to estimate variances when analyzing survey data from complex samples, and also how to fit linear and logistic regression models to complex sample survey data.

  • Introductory, Intermediate
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

At the conclusion of this course students will have a basic understanding of the objectives of a Data Monitoring Committee (DMC) and the role of a Data Analysis Center. They will learn how to create and administer a DMC, how to approach meaningful statistical analyses, and how to make data-driven DMC decisions.

  • Explain how to set up a data monitoring committee
  • Explain how the commmittee conducts business
  • Define adverse events
  • Describe the statistical analysis goals for DMC’s
  • Specify the issues of bias and multiplicity to be alert for
  • Describe emerging issues such as biomarkers and causal analysis

Who Should Take This Course

Anyone involved with the design, implementation or analysis of clinical trials.

Our Instructors

Dr. Jay Herson

Dr. Jay Herson

Dr. Jay Herson is currently serving on the adjunct faculty in Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His previous positions included Senior Biostatistician at the University of Texas MD Anderson Cancer Center and as president of the contract research organization he founded known as Applied Logic Associates. Under his leadership ALA grew from a solo consultancy to a 50-person full-service CRO with global capabilities. Dr. Herson started the first Data Monitoring Committee (DMC) in the pharmaceutical industry and has Chaired or served on 25 DMCs.

Course Syllabus

Week 1

Introduction and Organization of a Data Monitoring Program

  • Objectives of a Data Monitoring Committee (DMC)
    • Differences in DMC’s between the NIH-sponsored trials and industry-sponsored trials
    • Issues related to the size of the sponsoring company (Big Pharma, Middle Pharma, Infant Pharma)
  • Creation of a DMC
  • Selecting members
  • Conflicts of interest
  • Role of the Data Analysis Center (DAC)

Week 2

Meetings and Clinical Issues

  • The DMC Charter
  • Types and structure of meetings
  • Open and closed sessions
  • Adverse event definitions and coding schemes
  • Format for meeting agendas
  • Impact of multinational trials

Week 3

Statistical Issues, Biases, and Pitfalls

  • Goals of statistical analysis for DMC’s
  • Useful data displays
  • Frequentist, likelihood, and Bayesian analysis methodsIncidence
    Rate/patient year
  • Incidence
  • Rate/patient year
  • Time-to-event
  • Power
  • Multiplicity
  • Sources of bias by sponsor
  • Investigator
  • Granularity bias
  • Competing risks

Week 4

DMC Decisions and Emerging Issues

  • Types of DMC decisions and the environment in which they are made
  • Risk vs. benefit analysis
  • Steps taken when a safety issue arises
  • Meta-analysis
  • Problems particular to Infant Pharma companies
  • DMC operations for safety when adaptive designs are employed for efficacy
  • Real-time SAE reporting via the internet
  • Causal inference
  • Biomarkers
  • Training of DMC members
  • Cost control, DMC audits
  • Working with internal safety review committees
  • Effect of company mergers and licensing agreements on independent safety review


Introduction to Statistical Issues in Clinical Trials

This course will teach you the basic statistical principles in the design and analysis of randomized controlled trials.
  • Skill: Introductory, Intermediate
  • Credit Options: CEU

Private: Statistics 2 – Inference and Association

This course, the second of a three-course sequence, will teach you the use of inference and association through a series of practical applications, based on the resampling/simulation approach, and how to test hypotheses, compute confidence intervals regarding proportions or means, computer correlations, and use of simple linear regressions.
  • Skill: Introductory, Intermediate
  • Credit Options: CEU

Private: Statistics 1 – Probability and Study Design

This course, the first of a three-course sequence, provides an introduction to statistics for those with little or no prior exposure to basic probability and statistics.
  • Skill: Introductory, Intermediate
  • Credit Options: CEU
Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

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Independent Data Monitoring Committees in Clinical Trials

Additional Information


Homework in this course consists of short answer questions to test concepts.

In addition to assigned readings, this course also has supplemental readings available online.

Course Text

The course text is Data and Safety Monitoring Committees in Clinical Trials, second edition (2016) by Jay Herson, which you can order from CRC Press.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:







  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Independent Data Monitoring Committees in Clinical Trials