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Commun Stat Theory Methods. 2019;48(5):1149-1165. doi: 10.1080/03610926.2018.1425447. Epub 2018 Jan 24.

On the Distribution of Summary Statistics for Missing Data.

Author information

1
Department of Biostatistics and Informatics, University of Colorado Denver.
2
Neptune and Company.
3
Department of Health Outcomes and Policy, University of Florida, Gainesville.

Abstract

Under an assumption that missing values occur randomly in a matrix, formulae are developed for the expected value and variance of six statistics that summarize the number and location of the missing values. For a seventh statistic, a regression model based on simulated data yields an estimate of the expected value. The results can be used in the development of methods to control the Type I error and approximate power and sample size for multilevel and longitudinal studies with missing data.

KEYWORDS:

longitudinal; missing data; multilevel; multinomial; power

PMID:
31439981
PMCID:
PMC6706086
[Available on 2020-01-01]
DOI:
10.1080/03610926.2018.1425447

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