Background: Liver works as one of the most versatile organs in the human body. But any kind of disturbance occurs in the liver may cause the liver disease. One of the most common liver infections is hepatitis C which is caused by the Hepatitis C Virus (HCV). It is well known that liver is the largest solid organ in the human body and also it is called the exocrine gland as it secretes bile into the intestine. Aim: The aim of this study is to evaluate the causal relationship of Bilirubin with each liver biomarker using the advanced regression techniques. Methods: We use two advanced regression techniques, namely Joint Generalized Linear Model (JGLM) and Generalized Additive Model (GAM). For model selection, we check the AIC value, GCV score and adjusted R–square as well as the different diagnostic plots like Q–Q plot, Residual vs. Fitted plot etc. are displayed. Results: Bilirubin, a human liver disease biomarker, is a brownish yellow substance found in bile and it is produced in the liver when the old red blood cells break down. The present study reveals that Bilirubin is positively associated (p-value<0.05) with Aspartate Aminotransferase (AST), Creatinine (CREA), Gamma-Glutamyl Transpeptidase (GGT), Protein (PROT), Alkaline Phosphatase (ALP)*Albumin (ALB) and marginally associated with Choline Esterase (CHE)* Cholesterol (CHOL) (p-value=0.0591). While it is negatively associated (p-value < 0.05) with Age, Sex, Alkaline Phosphatase (ALP), Alanine Aminotransferase (ALT), Choline Esterase (CHE), Cholesterol (CHOL), Albumin (ALB), Creatinine (CREA)*Gamma-Glutamyl Transpeptidase (GGT) under JGLM. Besides of that, Bilirubin is positively associated with AST, CREA, GGT, (CREA*GGT), (CHE*CHOL) whereas it is negatively associated with Sex, ALT, CHE, CHOL. Also, ALB is highly positively significant as a non–parametric smoothing term (p-value < 0.001) under GAM. Conclusion: Both the advanced regression models JGLM and GAM explain the association between Bilirubin with other liver diseases biomarker in case of Hepatitis C.
| Published in | Biomedical Statistics and Informatics (Volume 6, Issue 2) |
| DOI | 10.11648/j.bsi.20210602.11 |
| Page(s) | 23-31 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
Bilirubin, Hepatitis C, Joint Generalized Linear Model, Generalized Additive Model, Gamma Distribution, Link function
| [1] | McCullagh P, Nelder JA. Generalized linear models. London: Chapman & Hall, 1989. |
| [2] | Myers RH, Montgomery DC, Vining GG. Generalized linear models with applications in engineering and the sciences. New York: John Wiley & Sons, 2002. |
| [3] | Mukherjee S. Statistical Modeling for Findings the Determinants of Neonates’ Very Low Birth Weight. American Journal of Clinical and Experimental Medicine 2018; 6: 125-135. |
| [4] | Das RN. Determinants of Diabetes Mellitus in the Pima Indian Mothers and Indian Medical Students. The Open Diabetes Journal 2014; 7: 5-13. |
| [5] | Das RN, Devi RS, Kim J. Mothers’ Lifestyle Characteristics Impact on Her Neonates’ Low Birth Weight. International Journal of Women’s Health and Reproduction Sciences 2014; 2: 229-235. |
| [6] | Box GEP. Signal-to-noise ratios, performance criteria, and transformations (with discussion). Technometrics 1988; 30: 1-40. |
| [7] | Nelder JA, Lee Y. Generalized linear models for the analysis of Taguchi-type experiments. Applied Stochastic Models and Data Analysis 1991; 7: 107-120. |
| [8] | Lee Y, Nelder JA, Pawitan Y. Generalized Linear Models with Random Effects (Unified Analysis via H-likelihood). Chapman & Hall, London 2006. |
| [9] | Das RN, Lee Y. Log normal versus gamma models for analyzing data from quality-improvement experiments. Qual Eng 2009; 21: 79-87. |
| [10] | Lee Y, Nelder JA. Generalized linear models for the analysis of quality improvement experiments. Can J Statist 1998; 26: 95-105. |
| [11] | Das RN. Robust Response Surfaces, Regression, and Positive Data Analyses. Chapman & Hall, London 2014. |
| [12] | Qu Y, Tan M, Rybicki L. A unified approach to estimating association measures via a joint generalized linear model for paired binary data. Commun Statist-Theory Methods 2000; 29: 143-56. |
| [13] | Lesperance ML Park S. GLMs for the analysis of robust designs with dynamic characteristics. J Qual Tech 2003; 35: 253-63. |
| [14] | Hastie T, Tibshirani R. Generalized Additive Models, first ed. London: Chapman and Hall 1990. |
| [15] | Schimek MG. Estimation and inference in partially linear models with smoothing splines. Journal of Statistical Planning and Inference 2000; 91: 525-540. |
| [16] | Wood SN. Generalized Additive Models: An Introduction with R. London: Chapman and Hall 2006. |
| [17] | Mukherjee S, Kapoor S, Banerjee P. Diagnosis and Identification of Risk Factors for Heart Disease Patients Using Generalized Additive Model and Data Mining Techniques. J Cardiovasc Disease Res 2017; 8: 137-44. |
| [18] | Currie I, Durban M, Eilers P. Generalized linear array models with applications to Multi-dimensional smoothing. Journal of the Royal Statistical Society B 2006; 68: 259–280. |
| [19] | Green P, Silverman B. Nonparametric Regression and Generalized Linear Models. London: Chapman & Hall 1994. |
| [20] | Ruppert D. Selecting the number of knots and for penalized splines. Journal of Computational and Graphical Statistics 2002; 11: 735-757. |
| [21] | Eilers P, Marx B. Flexible smoothing with B-splines and penalties. StatSci 1996; 11: 89-121. |
| [22] | Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning, Springer-Verlag, 2001. |
| [23] | Nelder JA. The statistics of linear models: back to basics. Statistics and Computing 1994; 4: 221–234. |
| [24] | Das RN, Lee Y, Mukherjee S, et al. Relationship of Alanine Aminotransferase (SGPT) with other Liver Biomarkers. Journal of Gastroenterology, Pancreatology & Liver Disorders 2019; 7: 1–5. |
| [25] | Hoffmann G, Bietenbeck A, Lichtinghagen R, Klawonn F. Using machine learning techniques to generate laboratory diagnostic pathways—a case study. Journal of Laboratory and Precision Medicine 2018; 3: 58–67. doi: 10.21037/jlpm.2018.06.01. |
| [26] | Das RN, Lee Y, Sengupta S, et al; Complex Association of Albumin with Other Liver Biomarkers. EC Gastroenterology and Digestive System 2019; 6: 132-137. |
| [27] | Das RN. Determinants of total bilirubin for liver patients. Journal of Liver Research, Disorders & Therapy 2018; 4: 127–131. |
| [28] | Das RN, Mukherjee S, Sharma I. Alkaline Phosphatase Determinants of Liver Patients. Journal of the Pancreas 2018; 19: 18–23. |
| [29] | Smith MW. Hepatitis C and the Hep C Virus. [Online] https://www.webmd.com/hepatitis/digestive-diseases-hepatitis-c (2019, accessed 20 February 2021). |
| [30] | Center for Diseases control and prevention. Viral Hepatitis [Online]. https://www.cdc.gov/hepatitis/hcv/index.html (accessed 18 February 2021). |
| [31] | Yoshimasu Y, Furuichi Y, Kasai Y, et al. Predictive Factors for Hepatocellular Carcinoma Occurrence or Recurrence after Direct-Acting Antiviral Agents in Patients with Chronic Hepatitis C. Journal of Gastrointestinal and Liver Diseases 2019; 28: 63–71. |
| [32] | Pohl A, Behling C, Oliver D, et al. Serum Aminotransferase Levels and Platelet Counts as Predictors of Degree of Fibrosis in Chronic Hepatitis C Virus Infection. The American Journal of Gastroenterology 2001; 96: 3142-6. |
| [33] | Williams ALB, Hoofnagle JH. Ratio of Serum Aspartate to Alanine Aminotransferase in Chronic Hepatitis Relationship to Cirrhosis. Gastroenterology. 1988; 95: 734–739. |
| [34] | Hepatitis C. [Online]: https://www.who.int/news-room/fact-sheets/detail/hepatitis-c#:~:text=Hepatitis%20C%20is%20a%20liver,major%20cause%20of%20liver%20cancer (2020, accessed 20 February 2021). |
APA Style
Proloy Banerjee, Anirban Goswami, Shreya Bhunia, Sudipta Basu. (2021). Determination of Causal Relationship Between Bilirubin and Other Liver Biomarker in Case of Hepatitis C. Biomedical Statistics and Informatics, 6(2), 23-31. https://doi.org/10.11648/j.bsi.20210602.11
ACS Style
Proloy Banerjee; Anirban Goswami; Shreya Bhunia; Sudipta Basu. Determination of Causal Relationship Between Bilirubin and Other Liver Biomarker in Case of Hepatitis C. Biomed. Stat. Inform. 2021, 6(2), 23-31. doi: 10.11648/j.bsi.20210602.11
AMA Style
Proloy Banerjee, Anirban Goswami, Shreya Bhunia, Sudipta Basu. Determination of Causal Relationship Between Bilirubin and Other Liver Biomarker in Case of Hepatitis C. Biomed Stat Inform. 2021;6(2):23-31. doi: 10.11648/j.bsi.20210602.11
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Download@article{10.11648/j.bsi.20210602.11,
author = {Proloy Banerjee and Anirban Goswami and Shreya Bhunia and Sudipta Basu},
title = {Determination of Causal Relationship Between Bilirubin and Other Liver Biomarker in Case of Hepatitis C},
journal = {Biomedical Statistics and Informatics},
volume = {6},
number = {2},
pages = {23-31},
doi = {10.11648/j.bsi.20210602.11},
url = {https://doi.org/10.11648/j.bsi.20210602.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20210602.11},
abstract = {Background: Liver works as one of the most versatile organs in the human body. But any kind of disturbance occurs in the liver may cause the liver disease. One of the most common liver infections is hepatitis C which is caused by the Hepatitis C Virus (HCV). It is well known that liver is the largest solid organ in the human body and also it is called the exocrine gland as it secretes bile into the intestine. Aim: The aim of this study is to evaluate the causal relationship of Bilirubin with each liver biomarker using the advanced regression techniques. Methods: We use two advanced regression techniques, namely Joint Generalized Linear Model (JGLM) and Generalized Additive Model (GAM). For model selection, we check the AIC value, GCV score and adjusted R–square as well as the different diagnostic plots like Q–Q plot, Residual vs. Fitted plot etc. are displayed. Results: Bilirubin, a human liver disease biomarker, is a brownish yellow substance found in bile and it is produced in the liver when the old red blood cells break down. The present study reveals that Bilirubin is positively associated (p-valueConclusion: Both the advanced regression models JGLM and GAM explain the association between Bilirubin with other liver diseases biomarker in case of Hepatitis C.},
year = {2021}
}
TY - JOUR T1 - Determination of Causal Relationship Between Bilirubin and Other Liver Biomarker in Case of Hepatitis C AU - Proloy Banerjee AU - Anirban Goswami AU - Shreya Bhunia AU - Sudipta Basu Y1 - 2021/04/26 PY - 2021 N1 - https://doi.org/10.11648/j.bsi.20210602.11 DO - 10.11648/j.bsi.20210602.11 T2 - Biomedical Statistics and Informatics JF - Biomedical Statistics and Informatics JO - Biomedical Statistics and Informatics SP - 23 EP - 31 PB - Science Publishing Group SN - 2578-8728 UR - https://doi.org/10.11648/j.bsi.20210602.11 AB - Background: Liver works as one of the most versatile organs in the human body. But any kind of disturbance occurs in the liver may cause the liver disease. One of the most common liver infections is hepatitis C which is caused by the Hepatitis C Virus (HCV). It is well known that liver is the largest solid organ in the human body and also it is called the exocrine gland as it secretes bile into the intestine. Aim: The aim of this study is to evaluate the causal relationship of Bilirubin with each liver biomarker using the advanced regression techniques. Methods: We use two advanced regression techniques, namely Joint Generalized Linear Model (JGLM) and Generalized Additive Model (GAM). For model selection, we check the AIC value, GCV score and adjusted R–square as well as the different diagnostic plots like Q–Q plot, Residual vs. Fitted plot etc. are displayed. Results: Bilirubin, a human liver disease biomarker, is a brownish yellow substance found in bile and it is produced in the liver when the old red blood cells break down. The present study reveals that Bilirubin is positively associated (p-valueConclusion: Both the advanced regression models JGLM and GAM explain the association between Bilirubin with other liver diseases biomarker in case of Hepatitis C. VL - 6 IS - 2 ER -
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