Data Analysis of Diabetes Mellitus with Joint Modeling Method

A'yunin Sofro, Muizzatul Mukaromah, Khusnia Nurul Khikmah

Abstract


Diabetes mellitus is a dangerous disease that requires long-term medical treatment. The cause of this disease is high blood sugar levels. If not treated immediately, complications will occur and even cause death. The data is taken from the Indonesia Family Life Survey (IFLS). IFLS is a longitudinal measurement that is performed repeatedly every five years. More data is needed for repeated measures. Therefore, this research needs to be done to accommodate the missing data, and it is assumed that it is missing at random (MAR). This study aims to analyze the causative factors that are thought to affect the recovery time of patients with diabetes mellitus using the joint modeling method. This model is a relationship between event time data and repeated measurement data. The joint modeling method uses a linear mixed model for longitudinal measurements and a Cox proportional hazard model for survival. The variables were taken from IFLS4 and IFLS5 data with 293 observations: measurement time, treatment history, gender, comorbidities, and complications. The results in this study obtained a significant influence, namely the variables of measurement time, gender, and complications, on the recovery time of patients with diabetes mellitus. With the reduced measurement time, the patient has a lower chance of recovering 8.7184 times. The variables of gender also have a lower possibility of recovery of 9.1032 times, respectively.

Keywords


diabetes mellitus; Indonesia; joint modeling; longitudinal data; survival data

Full Text:

PDF

References


S. Preethikaa and M. P. Brundha, “Awareness of diabetes mellitus among general population,” Res J Pharm Technol, vol. 11, no. 5, pp. 1825–1829, 2018.

I. D. Karantas, M. E. Okur, N. Ü. Okur, and P. I. Siafaka, “Dyslipidemia management in 2020: an update on diagnosis and therapeutic perspectives,” Endocrine, Metabolic & Immune Disorders-Drug Targets (Formerly Current Drug Targets-Immune, Endocrine & Metabolic Disorders), vol. 21, no. 5, pp. 815–834, 2021.

World Health Organization, “Global Report on Diabetes,” 2016.

D. Ratnawati, C. T. Wahyudi, and G. Zetira, “Dukungan Keluarga Berpengaruh Kualitas Hidup Pada Lansia dengan Diagnosa Diabetes Melitus,” Jurnal Ilmiah Ilmu Keperawatan Indonesia, vol. 9, no. 02, pp. 585–593, 2019.

S. Li et al., “Prevalence of diabetes mellitus and impaired fasting glucose, associated with risk factors in rural Kazakh adults in Xinjiang, China,” Int J Environ Res Public Health, vol. 12, no. 1, pp. 554–565, 2015.

F. Z. Kamilah et al., “Analysis of the determinants of diabetes mellitus in Indonesia: a case study of the 2014 indonesian family life survey,” Disease Prevention and Public Health Journal, vol. 15, no. 2, p. 88, 2021.

M. Wirastuti, “Analisis Longitudinal pada Data Pasien Diabetes Melitus,” J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, vol. 12, no. 1, pp. 13–19, 2019.

D. M. Istuti, “Analisis Ketahanan Hidup Data Ties Pasien Tuberkulosis dengan Metode Exact Likelihood pada Model Regresi Cox Proportional Hazard,” Mathunesa: Jurnal Ilmiah Matematika, vol. 7, no. 2, 2019.

J. Tanoey and H. Becher, “Diabetes prevalence and risk factors of early-onset adult diabetes: results from the Indonesian family life survey,” Glob Health Action, vol. 14, no. 1, p. 2001144, 2021.

A. Saadane, E. M. Lessieur, Y. Du, H. Liu, and T. S. Kern, “Successful induction of diabetes in mice demonstrates no gender difference in development of early diabetic retinopathy,” PLoS One, vol. 15, no. 9, p. e0238727, 2020.

S. Schlesinger et al., “Prediabetes and risk of mortality, diabetes-related complications and comorbidities: umbrella review of meta-analyses of prospective studies,” Diabetologia, pp. 1–11, 2022.

J. B. Cole and J. C. Florez, “Genetics of diabetes mellitus and diabetes complications,” Nat Rev Nephrol, vol. 16, no. 7, pp. 377–390, 2020.

D. Rizopoulos, Joint models for longitudinal and time-to-event data: With applications in R. CRC press, 2012.

G. Ambi Ramakrishnan, K. K. Srinivasan, A. Mondal, and C. R. Bhat, “Joint model of sustainable mode choice for commute, shift potential and alternative mode chosen,” Transp Res Rec, vol. 2675, no. 7, pp. 377–391, 2021.

P. K. Mondal, “Joint modeling of longitudinal measurements and survival data with competing risks: application to HIV/AIDS study,” University of Saskatchewan, Saskatoon, Canada, 2017.

T. Yu, “ Joint modelling of complex longitudinal and survival data, with applications to HIV studies,” Thesis, Vancouver, 2019.

A. Sattar and S. K. Sinha, “Joint modeling of longitudinal and survival data with a covariate subject to a limit of detection,” Stat Methods Med Res, vol. 28, no. 2, pp. 486–502, 2019.

P. Mondal, H. J. Lim, and O. C. S. Team, “The Effect of MSM and CD4+ Count on the Development of Cancer AIDS (AIDS-defining Cancer) and Non-cancer AIDS in the HAART Era,” Curr HIV Res, vol. 16, no. 4, pp. 288–296, 2018.

H. G. Vuong et al., “BRAF mutation is associated with an improved survival in glioma—a systematic review and meta-analysis,” Mol Neurobiol, vol. 55, pp. 3718–3724, 2018.

D. Rizopoulos, Joint models for longitudinal and time-to-event data: With applications in R. 2012. doi: 10.1201/b12208.

T. Eriskawati, “KORELASI ANTARA KADAR HbA1c DAN RASIO LDL/HDL KOLESTEROL PADA PENDERITA DIABETES MELITUS TIPE 2,” UNIVERSITAS SEBELAS MARET, Indonesia, 2015.

R. C. Team, “A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria,” Available online: www. R-project. org/(accessed on 11 September 2020), 2018.

D. Rizopoulos, “JM: An R package for the joint modelling of longitudinal and time-to-event data,” J Stat Softw, vol. 35, pp. 1–33, 2010.

L. F. Hidayati, “HUBUNGAN KADAR GULA DARAH DENGAN DERAJAT KEGOYAHAN GIGI PADA PASIEN DIABETES MELLITUS,” Diploma, Poltekkes Kemenkes Yogyakarta, Yogyakarta, Indonesia, 2019.

S. T. Certo, M. C. Withers, and M. Semadeni, “A tale of two effects: Using longitudinal data to compare within‐and between‐firm effects,” Strategic Management Journal, vol. 38, no. 7, pp. 1536–1556, 2017.

M. Herle et al., “Identifying typical trajectories in longitudinal data: modelling strategies and interpretations,” Eur J Epidemiol, vol. 35, no. 3, pp. 205–222, 2020.

C. Deischinger et al., “Diabetes mellitus is associated with a higher risk for major depressive disorder in women than in men,” BMJ Open Diabetes Res Care, vol. 8, no. 1, p. e001430, 2020.

Z. Gao, Z. Chen, A. Sun, and X. Deng, “Gender differences in cardiovascular disease,” Med Nov Technol Devices, vol. 4, p. 100025, 2019.

R. Balaji, R. Duraisamy, and M. P. Kumar, “Complications of diabetes mellitus: A review.,” Drug Invention Today, vol. 12, no. 1, 2019.

M. J. L. Verhulst, B. G. Loos, V. E. A. Gerdes, and W. J. Teeuw, “Evaluating all potential oral complications of diabetes mellitus,” Front Endocrinol (Lausanne), vol. 10, p. 56, 2019.




DOI: http://dx.doi.org/10.12962/j27213862.v7i3.20519

Refbacks

  • »


Bookmark and Share


Creative Commons License
Inferensi by Department of Statistics ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/inferensi.

ISSN:  0216-308X

e-ISSN: 2721-3862

Web Analytics Made Easy - StatCounter View My Stats