Missing Data Methods for Partial Correlations.
TL;DR: The partial correlation coefficient is extended in the presence of missing data using the expectation-maximization (EM) algorithm, and it is compared with a multiple imputation method and complete case analysis using simulation studies to find the best approach.
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Abstract: In the dementia area it is often of interest to study relationships among regional brain measures; however, it is often necessary to adjust for covariates. Partial correlations are frequently used to correlate two variables while adjusting for other variables. Complete case analysis is typically the analysis of choice for partial correlations with missing data. However, complete case analysis will lead to biased and inefficient results when the data are missing at random. We have extended the partial correlation coefficient in the presence of missing data using the expectation-maximization (EM) algorithm, and compared it with a multiple imputation method and complete case analysis using simulation studies. The EM approach performed the best of all methods with multiple imputation performing almost as well. These methods were illustrated with regional imaging data from an Alzheimer’s disease study.
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Missing Data Methods for Partial Correlations.
TL;DR: The partial correlation coefficient is extended in the presence of missing data using the expectation-maximization (EM) algorithm, and it is compared with a multiple imputation method and complete case analysis using simulation studies to find the best approach.
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Statistical Analysis with Missing Data
Roderick J. A. Little,Donald B. Rubin +1 more
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TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
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T. W. Anderson
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TL;DR: In this article, the distribution of the Mean Vector and the Covariance Matrix and the Generalized T2-Statistic is analyzed. But the distribution is not shown to be independent of sets of Variates.
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