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