ArticleJournal of biometrics & biostatistics2012
Missing Data Methods for Partial Correlations.
Article in Journal of biometrics & biostatistics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Causal risk factors for stroke, delirium, and mortality after elective and urgent open aortic arch surgery for older adults.GeroScience · 2026Article
- Regression-based Modeling of Spearman's Rho for Longitudinal Metabolomics and Mental Wellness in Breast Cancer Patients.bioRxiv : the preprint server for biology · 2026Article
- Comprehensive Evaluation of Advanced Imputation Methods for Proteomic Data Acquired via the Label-Free Approach.International journal of molecular sciences · 2024Article
- Detection of Immune Microenvironment Changes and Immune-Related Regulators in Langerhans Cell Histiocytosis Bone Metastasis.BioMed research international · 2023Article
- Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies.Metabolomics : Official journal of the Metabolomic Society · 2018Article
- An Exact Method for Partitioning Dichotomous Items Within the Framework of the Monotone Homogeneity Model.Psychometrika · 2015Article
- Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer's Dementia.Computational and mathematical methods in medicine · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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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Registered trials
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