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Analysis and Sensitivity Analysis for Missing Data

taught by Geert Molenberghs


Brief Description:

This course extends the study of missing data analysis that was introduced in "Missing Data", and covers the situation when data are not missing at random.

Instructor(s):
Level: advanced

Who Should Take This Course:

Statistical analysts and consultants who develop and apply statistical models that must be used in situations where data are incomplete, and the simpler models may not be applicable.

Dates:
February 22, 2013 to March 22, 2013February 21, 2014 to March 21, 2014
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Analysis and Sensitivity Analysis for Missing Data

taught by Geert Molenberghs

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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$499
Register Online -$399 (you must be affiliated with a college, university or high school)

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Analysis and Sensitivity Analysis for Missing Data

taught by Geert Molenberghs



Aim of Course:

This course covers the modeling and analysis of incomplete multivariate or longitudinal data - data with records for which some, but not all observations are missing. Many analysis methods cannot handle the inclusion of such records, but omitting these records discards valuable information. There are a range of techniques to handle this situation, and this course goes beyond the methods covered in the course "Missing Data." In this course, you will learn about treatments that apply when data are missing, but not at random. This is a very common situation, and when it occurs, the classic models do not apply. This course describes how "Missing at Random" counterpart models may be identified and assessed for their suitability for the "Missing Not at Random" situation.

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

Prerequisite(s):

If you are unclear as to whether you have mastered the requirements, try these placement tests here.

Participants should also be familiar with the material covered in Missing Data, and should have facility with statistical modeling methods.


Course Program:

SESSION 1:Modeling Incomplete Data

  • Setting The Scene
  • The Failure of Simple Methods
  • Proper Analysis of Incomplete Data

SESSION 2: Inverse Probability Weighting and Multiple Imputation

  • Weighted Generalized Estimating Equations
  • Multiple Imputation
  • Case Study: Age-related Macular Degeneration
  • Inverse Probability Weighting and Double Robustness

SESSION 3: Initial Topics in Methods and Sensitivity Analysis for Incomplete Data

  • An MNAR Selection Model and Local Influence
  • Mechanism for Growth Data
  • Interval of Ignorance
  • Pattern-mixture Models

SESSION 4: Further Topics in Methods and Sensitivity Analysis for Incomplete Data

  • MAR in Three Frameworks and MAR Counterparts
  • A Latent-variable Mixture Model as a Basis for Sensitivity Analysis in Incomplete Longitundinal Data
  • Concluding Remarks

HOMEWORK: Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

Organization of the Course:

This course takes place over the internet at the Institute for 4 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, 5.0 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 Missing Data in Clinical Studies by Geert Molenberghs and Mike Kenward, 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:

While the course notes and textbook use SAS-based illustrations, the course is also effective for people not using SAS, because concepts and applications are presented in a software-free fashion. For exercises, annotated output will be provided.

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Analysis and Sensitivity Analysis for Missing Data

taught by Geert Molenberghs



Instructor(s):
Dates:
February 22, 2013 to March 22, 2013February 21, 2014 to March 21, 2014
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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