Analysis and Sensitivity Analysis for Missing Data

Analysis and Sensitivity Analysis for Missing Data

taught by Geert Molenberghs

Aim of Course:

This online course, "Analysis and Sensitivity Analysis for Missing Data" 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.

Course Program:

WEEK 1:Modeling Incomplete Data

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

WEEK 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

WEEK 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

WEEK 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.

In addition to assigned readings, this course also has practice exercises, an end of course data modeling project, example software codes, and supplemental readings available online.

Analysis and Sensitivity Analysis for Missing Data

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.
Level:
advanced
Prerequisite:

Participants should also be familiar with the material covered in Missing Data, and should have facility with statistical modeling methods.
Organization of the Course:
This course has practice exercises, supplemental readings that can be found online, software example codes, and an end of course data modeling project


Options for Credit and Recognition:

Course Text:

The required text for this course is Missing Data in Clinical Studies by Geert Molenberghs and Mike Kenward.

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.
Instructor(s):

Dates:

To be scheduled.

Analysis and Sensitivity Analysis for Missing Data

Instructor(s):

Dates:
To be scheduled.

Course Fee: $589

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