Sensitivity Analysis for Non-Monotone Missing Data with Application to Tuberculosis Studies
*Daniel Oscar Scharfstein, Johns Hopkins University Keywords: Mulitple Imputation Missing data are a common problem in randomized clinical trials. In the presence of missing data, inference about treatment effects relies on untestable assumptions about the distribution of missing outcomes. To address this issue, a recent National Academy of Sciences report recommends the use of sensitivity analysis anchored at a benchmark assumption. In this talk, we develop, in the context of a randomized tuberculosis study, a sensitivity analysis scheme for non-monotone missing data centered around a benchmark assumption that leverages all the available data. We develop a likelihood-based procedure to multiply impute the missing solid culture conversion data. This is joint work with Maria Abraham and Andrea Rotnitzky.
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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