Non-ignorable Dropout in Clinical Trials – Case Studies
View Presentation *Edward F Vonesh, Northwestern University Keywords: Non-ignorable Dropout, Shared Parameter Models Missing data due to patient dropout is a common problem in clinical trials. While likelihood-based methods offer protection against biased inference in cases where dropout is ignorable, they do not protect against bias in the presence of non-ignorable dropout. In this talk, we present methods that accommodate non-ignorable dropout using readily available software. The basic approach entails jointly modeling the outcome variable and time to dropout using a shared parameter (SP) model. The SP model will be illustrated using data from the MDRD study, a RCT of the effects of diet and blood pressure control on progression of renal disease. We then apply a SP model to the analysis of ordinal data from the NIMH Schizophrenia Collaborative Study in which we compare treatment related changes in disease severity over time with adjustment for possible non-ignorable patient dropout.
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
Short Course materials due from Instructors -
August 26, 2013
Housing Deadline -
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC