JSM 2005 Online Program

Abstract #304638

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	15
Type:	Topic Contributed
Date/Time:	Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Biopharmaceutical Section
Abstract - #304638
Title:	A Comparison of Two Multiple Imputation Methods for Missing Outcome Data in Clinical Trials
Author(s):	Elizabeth Galle*+ and Lei Peng
Companies:	Guidant Corporation
Address:	Cardiac Rhythm Management, St. Paul, MN, 55112,
Keywords:	Propensity Score ; Missing Data ; Multiple Imputation ; Cardiovascular Device
Abstract:	Missing or incomplete data caused by dropout is a common problem in clinical trials. A substantial amount of missing data could result in loss of study power and biased results. Several analytical methods are available for analyzing data with missing values. The choice of method should depend on the extent, mechanism, and pattern of missing data. Due to the unverifiable nature of the missing mechanism, sensitivity analysis often is utilized to assess the robustness of study conclusions using different assumptions about the behavior of the missing data. Under the missing at random assumption, multiple imputation method is considered to be more applicable because the imputation process incorporates the uncertainties involved in the estimation of missing values. This paper discusses two multiple imputation methodologies: the propensity score and the regression method. These methods are explored using data from a cardiovascular device trial. A comparison between the two methods will be presented using numerous simulations. The comparison will take into consideration different types of missing data mechanisms and evaluate the potential bias with respect to the parameter estimates.

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