Abstract #300532

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JSM 2003 Abstract #300532
Activity Number: 219
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 9:00 AM to 10:50 AM
Sponsor: Biopharmaceutical Section
Abstract - #300532
Title: A Weighted Imputation Technique Using Stratified Weighted Means
Author(s): Samantha C. Montgomery*+ and David R. Bristol
Companies: Iowa State University and Purdue Pharma, L.P.
Address: Department of Statistics, Ames, IA, 50011,
Keywords: clinical trials ; longitudinal data ; missing data ; imputation
Abstract:

In longitudinal clinical trials, missing data often occur due to premature discontinuations. An imputation technique is presented here using stratified weighted means (SWM). Patients are stratified based on assigned treatment and demographic variables. For a patient who has discontinued, the missing value for a particular analysis day is imputed using the weighted average of the last observed value for the patient and the mean of the observed values that day among patients in the same stratum who remained in the trial. The patient-specific weights are based on the amount of missing data for the patient. Using simulations based on chronic pain trials, the technique using SWM is compared to the last observation carried forward (LOCF) approach, a commonly used imputation technique. Various assumptions on the dropout patterns are considered. In many situations, the estimates obtained using the SWM imputation technique are less biased than those using the LOCF approach, and hence may result in more powerful between-treatment comparisons.

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