Evaluating Imputation Methods to Improve Prediction Accuracy for an HIV Study in Uganda

Evaluating Imputation Methods to Improve Prediction Accuracy for an HIV Study in Uganda

Standard statistical analyses often exclude incomplete observations, which can be particularly problematic when predicting rare outcomes, such as HIV positivity. In the linkage to the HIV care dataset, there were initially 553 complete HIV positive cases, with an additional 554 cases added through i...

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Bibliographic Details
Main Authors: Nadia B. Mendoza, Chii-Dean Lin, Susan M. Kiene, Nicolas A. Menzies, Rhoda K. Wanyenze, Katherine A. Schmarje, Rose Naigino, Michael Ediau, Seth C. Kalichman, Barbara A. Bailey
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Stats
Subjects:
missing data imputation
random forest classification
<i>amelia</i>
<i>hmisc</i>
<i>mice</i>
<i>missForest</i>
Online Access:https://www.mdpi.com/2571-905X/7/4/82
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