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Activity Number:
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486
- Missing Data Analysis
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Biometrics Section
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Abstract #313946
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Title:
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Weighted Mean Difference Statistics for Paired Biomarker Data in Presence of Missing Values
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Author(s):
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Li Chen* and Yuntong Li and Brent Shelton and Chi Wang and William St Clair and Heidi Weiss and John Villano and Arnold Stromberg
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Companies:
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University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky
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Keywords:
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Paired data;
missing data;
biomarker data;
nonparametric tests;
mean difference tests
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Abstract:
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Missing data is a common issue in many biomedical studies. Under a paired design, some subjects may have missing values in one of the two conditions due to loss of follow-up or other reasons. Such partially paired data complicate statistical comparison of population means between the two conditions. We propose a general class of test statistics based on the difference in weighted sample means without imposing any distributional or model assumption. An optimal weight is derived for this class of tests. Simulation studies and real data analysis show that our proposed test with the optimal weight performs well in practical situations.
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Authors who are presenting talks have a * after their name.
