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Submitted
September 5, 2025
Accepted
October 31, 2025
Published
October 31, 2025
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Abstract

Health data are often analyzed in their continuous form through approaches such as linear, logistic, or survival models. In this study, hematological variables were dichotomized based on established clinical cut-offs to enable log-linear analysis of associations among categorical variables, acknowledging the potential loss of information from this transformation. A log-linear model was applied to evaluate independence, dependence, and interaction patterns among leukocyte, hemoglobin, and hematocrit categories in a dengue hemorrhagic fever (DHF) patient dataset. Previous analyses using survival models identified these variables as factors associated with recovery rates; however, these models did not capture their interaction structure. Log-linear analysis was therefore employed to examine these associations more comprehensively. The best-fitting model was identified as , which included two-factor interactions between leukocyte–hematocrit and hemoglobin–hematocrit. This model demonstrated a good fit (Pearson , , ), including a three-factor interaction resulted in a saturated model (= 0) and did not improve model performance. These findings highlight significant interaction patterns among hematological variables in DHF patients, providing a more detailed understanding of their joint associations.

Keywords

Log-Linear Modeling Leukocyte Count Hemoglobin Level Hematocrit Value Interaction Pattern

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How to Cite
Irfan, M., Hayati, M. ., Madonna, N., & Dewi, W. U. (2025). Log-Linear Analysis of the Association among Hematological Variables in Dengue Hemorrhagic Fever Cases. Enthusiastic : International Journal of Applied Statistics and Data Science, 5(2), 166–177. https://doi.org/10.20885/enthusiastic.vol5.iss2.art6
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