JP Journal of Biostatistics

The JP Journal of Biostatistics is a highly regarded open-access international journal indexed in the Emerging Sources Citation Index (ESCI). It focuses on the application of statistical theory and methods in resolving problems in biological, biomedical, and agricultural sciences. The journal encourages the submission of experimental papers that employ relevant algorithms and also welcomes survey articles in the fields of biostatistics and epidemiology.

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PREDICTIVE BIOSTATISTICAL MODELING OF URIC ACID LEVELS BASED ON HIGH-DENSITY LIPOPROTEIN AND ALANINE AMINOTRANSFERASE USING R

Authors

  • Wan Muhamad Amir W Ahmad
  • Mohamad Nasarudin Adnan
  • Nor Azlida Aleng
  • Nor Farid Mohd Noor
  • Ruhaya Hasan
  • Mohamad Shafiq Mohd Ibrahim
  • Nurfadhlina Abdul Halim

Keywords:

generalized additive models (GAM), Mardia’s test, Uric acid, multivariate normality, nonparametric regression

DOI:

https://doi.org/10.17654/0973514325015

Abstract

This study uses biostatistics and R syntax to analyze and model High-Density Lipoprotein (HDL), Alanine Aminotransferase (ALT), and Uric acid values. The work addresses the intricate relationships between these parameters to improve biological prediction accuracy. After Mardia’s test of multivariate normality, data normalization was done methodically to ensure variable comparability. A multiple linear regression model was used to develop a predictive model that estimated HDL and ALT contributions to Uric acid levels, revealing their relative importance. The regression model’s p-values and contribution percentages showed that ALT affected Uric acid levels more than HDL.

Received: February 17, 2025
Accepted: April 3, 2025

References

C. Cao, Z. Mo and Y. Han, Association between alanine aminotransferase to high-density lipoprotein cholesterol ratio and nonalcoholic fatty liver disease: a retrospective cohort study in lean Chinese individuals, Scientific Reports 14 (2024), 6056.

S. Chen, X. Guo and S. Yu, Association between serum uric acid and elevated alanine aminotransferase in the general population, International Journal of Environmental Research and Public Health 13 (2016), 841.

S. Greven and F. Scheipl, A general framework for functional regression modelling, Statistical Modelling 17 (2017), 1-35.

T. J. Hastie, Generalised additive models, Statistical Models in S, J. M. Chambers and T. J. Hastie, eds., Chapman & Hall/CRC, 2017, pp. 249-307.

C. B. Hodges, B. M. Stone and P. K. Johnson, Researcher degrees of freedom in statistical software contribute to unreliable results: a comparison of nonparametric analyses conducted in SPSS, SAS, Stata, and R, Behavior Research Methods 55 (2023), 2813-2837.

Y. K. Lee, E. Mammen and J. P. Nielsen, Nonparametric regression with parametric help, Electronic Journal of Statistics 14(2) (2020), 3845-3868.

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Published

2025-04-19

Issue

Vol. 25 No. 2 (2025)

Section

Articles

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How to Cite

PREDICTIVE BIOSTATISTICAL MODELING OF URIC ACID LEVELS BASED ON HIGH-DENSITY LIPOPROTEIN AND ALANINE AMINOTRANSFERASE USING R. (2025). JP Journal of Biostatistics, 25(2), 295-302. https://doi.org/10.17654/0973514325015

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