Statistical Tips and Techniques for Handling Missing Data

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 significance).

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 Should Attend:

  • Study Investigators
  • Data managers
  • Data processors
  • Statisticians
  • 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 management
  • 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

FDB3131

Elaine Eisenbeisz

Elaine Eisenbeisz is a private practice statistician and owner of Omega Statistics, a statistical consulting firm based in Southern California. Elaine has over 30 years of experience in creating data and information solutions for industries ranging from governmental agencies and corporations, to start-up companies and individual researchers.

Elaine’s love of numbers began in elementary school where she placed in regional and statewide mathematics competitions. She attended University of California, Riverside, as a National Science Foundation scholar, where she earned a B.S. in Statistics with a minor in Quantitative Management, Accounting. Elaine received her Master’s Certification in Applied Statistcs from Texas A&M, and is currently finishing her graduate studies at Rochester Institute of Technology. Elaine is a member in good standing with the American Statistical Association as well as many other professional organizations. She is also a member of the Mensa High IQ Society. Omega Statistics holds an A+ rating with the Better Business Bureau.

Elaine has designed the methodology for numerous studies in the clinical, biotech, and health care fields. She currently is an investigator on approximately 10 proton therapy clinical trials for Proton Collaborative Group, based in Illinois. She also designs and analyzes studies as a contract statistician for nutriceutical and fitness studies with QPS, a CRO based in Delaware. Elaine has also worked as a contract statistician with numerous private researchers and biotech start-ups as well as with larger companies such as Allergan and Rio Tinto Minerals. Not only is Elaine well versed in statistical methodology and analysis, she works well with project teams. Throughout her tenure as a private practice statistician, she has published work with researchers and colleagues in peer-reviewed journals. Please visit the Omega Statistics website at www.OmegaStatistics.com to learn more about Elaine and Omega Statistics.

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