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
Statistics.com. For more details please visit at http://www.statistics.com/ClinBias/.
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 statistics.com 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 statistics.com accepts registration for its courses at special
prices in Indian Rupees through its partner, the Center for eLearning and
Training (C-eLT), Pune.
Call: 020 66009116
Websites:
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