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Activity Number: 339 - SPEED: Biopharmaceutical and General Health Studies: Statistical Methods and Applications, Part 1
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #307152
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

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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