A Bias Correction in Testing Treatment Efficacy under Informative Dropout in Clinical Trials
View Presentation *Fanhui Kong, FDA Keywords: In clinical trials of drug development, patients are often followed for a certain period of time, and the outcome variables are measured at scheduled time intervals. In such trials, patient dropout is often the major source for missing data. In this talk, for a time-saturated treatment effect model and an informative dropout scheme that depends on the unobserved outcomes only through the random-coefficients, we propose a grouping method to correct the biases in the estimation of treatment effect. In a simulation study, we compare the new method with the traditional methods of the observed case (OC) analysis, the last-observation-carried-forward (LOCF) analysis, and the mixed-model-repeated-measurement (MMRM) approach, and find it improves the current methods and gives more stable results in the treatment efficacy inferences.
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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