Missing data is common in most clinical trials because some subjects withdraw from the trial before the crucial final measurements. Without those final measurements, our picture of the new treatment is imperfect, and it could actually be wrong. What if most of the withdrawals from the new treatment arm dropped out because they felt they were getting worse, or because of strong side effects?

In this webinar recording:
  • Michael O’Kelly, Ph.D., Senior Director, Centre for Statistics in Drug Development (CSDD), Innovation, Quintiles, introduces the current missing data landscape. 
  • Jessica Cooper, Clinical Project Manager, Quintiles, will show how missing data can be prevented, describing how she and the study teams used both common-sense and “outside the box” methods to minimize loss to follow-up in two recent megatrials. 
  • Returning to statistics, Ilya Lipkovich, Ph.D., Senior Director, Center for Statistics in Drug Development (CSDD), Innovation, Quintiles, will discuss the framework for handling missing data known as multiple imputation. Ilya gives examples of how multiple imputation can provide reasonable approaches to analysing missing data. 
  • Michael O’Kelly finishes the webinar by describing a particular use of multiple imputation called “control-based” multiple imputation, and shows how historic data could motivate the use of “control-based” imputation for primary analyses and sensitivity analyses, using as an example a real study of major depressive disorder.

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