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Avoiding Selection Bias in Randomized Clinical Trials

taught by Vance Berger


Brief Description:

This course covers the essential concepts required to design rigorous randomized trials so as to ensure valid treatment comparisons.

Instructor(s):
Level: Intermediate

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.

Dates:
September 14, 2012 to October 19, 2012
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Avoiding Selection Bias in Randomized Clinical Trials

taught by Vance Berger

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Avoiding Selection Bias in Randomized Clinical Trials

taught by Vance Berger



Aim of Course:

This course covers 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.

This course is a core requirement or elective in the following Program(s) in Analytics and Statistical Studies (PASS):

Prerequisite(s):

Course Program:

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.

Organization of the Course:

This course takes place over the internet at the Institute for 5 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.


Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 6.25 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

The required text for this course is Selection Bias and Covariate Imbalances in Randomized Clinical Trials by Vance W. Berger, and it can be ordered from Wiley by clicking here. Wiley typically offers statistics.com customers up to 15% discount on this book (and all other statistics titles): enter the code aff15 in the Promotion Code field when prompted during checkout and click the Apply Discount button. (If you are located in Asia, the web procedure for your location may not accept this discount – try calling your regional Wiley representative.).

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

None

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Avoiding Selection Bias in Randomized Clinical Trials

taught by Vance Berger



Instructor(s):
Dates:
September 14, 2012 to October 19, 2012
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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What our students say:

"The course was very good and well presented. The material in the notes was self-explanatory for a non-technical person, and the supplementary book provided good reading for the person who is interested in more technical details."
Gichangi
Dept. of Statistics, Univ. of Southern Denmark (doctoral student)

"I need to know R to perform my job as I am a product manager for a software company that interacts with R.  I am now able to understand R scripts and hopefully contribute some of my own. The instructor's videos were great. Just hearing his voice made it more personal. This was my first ever web based course and I really enjoyed it although I had to work hard."

A. Henry
Certara
"I really enjoyed this course and like the instructor. The discussion board provides a valuable venue to discuss questions and clarify doubts. The instructor's feedback is prompt and helpful. I not only got my questions answered but also learned a lot from other's questions."
R. Yang
Purdue University
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