Defining and Evaluating Bias in Longitudinal Trials with NMAR Missing Data
View Presentation *Ronald W. Helms, Rho Inc. and UNC Keywords: Although it is well known that not missing at random (NMAR) missing data from longitudinal clinical trials inflict biases on estimators and consequently lead to tests of corrupted hypotheses, the model equation, , the definition of the primary parameter , and the definition of all depend critically on E[Y], which is not necessarily straightforward. To illustrate, one basic question is: what is the expected value of a NMAR missing element of Y? This presentation gives a more complete introduction to the problem, elaborates on the basic question, and presents a potentially satisfactory definition of E[Y] in this context that leads directly to definitions of ß and Bias. These definitions provide a basis for evaluating and comparing biases of specific statistical analyses and/or imputation methods for NMAR incomplete data from longitudinal clinical trials. We illustrate the methods of evaluating and comparing biases and the effects of testing corresponding corrupted hypotheses via analyses of realistic datasets from longitudinal analgesic studies.

Key Dates

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