O'Reilly logo

Preventing and Treating Missing Data in Longitudinal Clinical Trials by Craig Mallinckrodt

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

8 Methods of Dealing with Missing Data

8.1 Introduction

Until recently, guidelines for the analysis of clinical trial data provided only limited advice on how to handle missing data, and analytic approaches tended to be simple and ad hoc. The calculations required to estimate parameters from a balanced data set with the same number of patients in each treatment group at each assessment time are far easier than the calculations required when the numbers are not balanced, as is the case when patients drop out. Hence, the motivation behind early methods of dealing with missing data may have been as much to restore balance and foster computational feasibility in an era of limited computing power as to counteract the potential bias from the missing ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required