Monday, 15 October 2012

Avoiding Selection Bias in Randomized Clinical Trials

Do you like to learn the essential concepts required to design rigorous randomized trials so as to ensure valid treatment comparisons, primarily by avoiding selection bias and other biases. The nature and objectives of randomization are discussed, as are those of masking, allocation concealment, blocking, stratification, dynamic randomization, and various types of bias that can arise. In addition, we cover analysis techniques that can be used to salvage reliable treatment comparisons even if some of these biases are detected. These methods are more advanced, and involve adaptations of the propensity score. We round out the course with consideration of crossover designs and self-controlled studies. Learn with Dr. Vance W. Berger in his online course "Avoiding Selection Bias in Randomized Clinical Trials" at For more details please visit at

Who Should Take This Course:
Anyone who designs, conducts, analyzes, or reviews randomized clinical trials. This includes research staff in pharmaceutical and biotechnology firms and CROs, regulators, journal editors, and students of biostatistics and epidemiology.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Clinical Trial Designs - A Hierarchy
  • The need for comparison groups.
  • Historical vs. parallel comparison groups.
  • Self-selection vs. physician treatment decision vs. randomization.
  • Masking and allocation concealment.

SESSION 2: Detecting Violations of the Randomization Procedure
  • Baseline comparisons by treatment groups.
  • Baseline comparisons by P groups.
  • The Berger-Exner test and graph.

SESSION 3: Preventing Subversion of the Trial by Violations of Randomization
  • Permuted blocks with fixed or varied block size.
  • The maximal procedure.
  • Unrestricted randomization and chronological bias.

SESSION 4: Salvaging Trials Affected by Violations of Randomization
  • Excluding data from contaminated centers.
  • Excluding data from patients with predictable allocations.
  • Using the RPS as a covariate.

SESSION 5: Crossover Designs
  • Washout period.

The instructor, Dr. Vance W. Berger, is an Adjunct Professor at University of Maryland Baltimore County. He has written or co-written dozens of papers in peer reviewed journals in medicine and statistics and regularly reviews manuscripts for many of the top scholarly journals. He has also taught in the past at Rutgers University and the Johns Hopkins University School of Public Health, lectured all over the country on his research, and served as an FDA reviewer for over four years.

You will be able to ask questions and exchange comments with Dr. Vance W. Berger via a private discussion board throughout the course.   The courses take place online at in a series of 5 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

For India Registration and pricing, please visit us at

Call: 020 66009116


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