If you work with data, you should attend this webinar. Real life data is not pretty. It is messy and often incomplete.
There are many ways to handle missing data and many of these ways are
not the best. There are better ways to handle missing-ness in your data.
Not perfect, but better.
Just removing records (listwise deletion) with missing data can reduce
the power of your study and result in Type II error (when the effects
are truly there, but you don’t have enough power to achieve statistical
And, many of the adjustments that researchers use for handling missing
data introduce bias into the data. For instance, one of the most common
problems with many of the techniques is a reduction in standard error of
the estimates, which results in inflated Type I errors (seeing
significant findings when they do not truly exist).
Good power and low bias are hard to control to begin with. It is
important to learn how to handle missing-ness to make efficient use of
our data to achieve accurate and precise results. Some knowledge of
linear regression is desired.
Areas Covered in the Session:
- History, types and handling of missing-ness in a data set
- Good, bad, and ugly of some commonly used techniques in dealing with prevention of missing data
- Diagnostic tests and decision making when working with missing data
- Multiple imputation techniques
- Multiple imputation with SPSS software
Who Will Benefit:
- Study Investigators
- Data managers
- Data processors
- Site Personnel
- Clinical Research Associates
- Clinical Project Managers/Leaders
- Study Sponsors
- Professionals in pharmaceutical, medical device, clinical and
biotechnology research who oversee or work with data collection and
- Staff in the above fields who work with data collection/management
- Compliance auditors and regulatory professionals who require a
knowledge of missing data for assessment of study protocols and reports