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Activity Number: 426 - SPEED: Biopharmaceutical and General Health Studies: Statistical Methods and Applications, Part 2
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 3:05 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #307855
Title: Closest Similar Subset Imputation
Author(s): Macaulay Okwuokenye* and Karl E Peace
Companies: Brio Dexteri Pharmaceutical Consultant & UNE and Georgia Southern University
Keywords: Intercurrent Event; Missing at Random; Missing Data; Minimum Subset Imputation; Discrete Data Imputation; Count Data Imputation
Abstract:

Classifying patients based on stated reasons for missing outcome from different intercurrent events induces patients’ subsets in data from clinical trials. Often, data imputation disregards these patients’ subsets. We discuss a non-parametric data imputation method that reflects reasons stated for missing data and hence patients’ subsets. This subset imputation method is based on a similarity measure between baseline covariates of patients’ subset with missing data and a random closest subset without missing data. An illustration using imputation of gadolinium enhancing lesions in multiple sclerosis is provided.


Authors who are presenting talks have a * after their name.

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