Analysis of Incomplete Data and Methods for Imputation
About the course
Missing data are ubiquitous in medical research, leading to a loss of statistical power and potentially biased estimates. The aim of this course is to familiarize participants with these issues, and statistical methods for handling missing data.
Specifically the course will:
1. Introduce the issues missing data, review common jargon and argue for a principled, systematic approach
2. Briefly discuss the problems with ad-hoc approaches.
3. Introduce multiple imputation methods for handling missing data in realistically complex datasets
4. Introduce inverse probability weighting and for missing data and contrast it with imputation
5. Illustrate simple approaches for conducting sensitivity analyses for missing data. Computer practicals will use Stata.
Admission requirements
To take PhD courses you need at a minimum a master's degree or equivalent, or admission to a Medical Student Research Program.
For information about how to apply for admission, go to http://uit.no/helsefak/forskning/phd/emner
Objectives of the course
The participants will:
- have knowledge about the common terminology regarding incomplete data.
- have knowledge of the available methods for imputation of missing data.
- be able to apply the methods on new data.
Teaching methods
Language of instruction and examination
EnglishApplication deadline
Assessment
The exam will be a take home test evaluated as passed or failed.
The course will be arranged irregularly and no continuation exams will be given.
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