JSM 2005 Online Program

Abstract #302898

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:	323
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Biopharmaceutical Section
Abstract - #302898
Title:	A Comparison of Imputation Methods for Missing Data on Clinical Laboratory Assessments
Author(s):	Kapildeb Sen*+ and Chen-Sheng Lin and Kannan Natarajan
Companies:	Bristol-Myers Squibb Company and Bristol-Myers Squibb Company and Bristol-Myers Squibb Company
Address:	311 PenningtonRocky Hill Road, Pennington, NJ, 08543, United States
Keywords:	Missing Data ; Imputation methods ; Clincal Trials ; Multiple Imputation ; GFR ; LDLC
Abstract:	A common problem in clinical trials is the missing data on the direct measures of clinical laboratory assessments (e.g., Glomerular Filtration Rate, low-density lipoprotein cholesterol) at various time points due to procedural complexities. In such cases, an approximate calculation of this measure can be made indirectly. We will investigate an imputation method that groups individuals on the basis of the direct measures or the indirect measures at earlier time points to estimate the missing direct measure at a later time point. Using simulations, this method will be compared to multiple imputation methods and single imputation methods such as linear regression, mean substitution, and LOCF. These methods will be evaluated with respect to the accuracy and precision of the estimates.

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