Modeling time-to-good control of hypertension using Cox proportional hazard and frailty models at Bahir-Dar Felege Hiwot Referral Hospital
Open accessAbstract
Purpose: The main purpose of this study was to explore possible modeling approaches of time-to-good control of hypertension using Cox proportional hazard (Cox-PH) and frailty models, using data from Bahir-Dar Felege Hiwot Referral Hospital. Patients and methods: An institution-based retrospective cohort study was conducted in June 2014. The study population consisted of all hypertensive patients who had visited the hospital at least three times between January 1, 2009, to December 31, 2013. Five hundred patients were selected using simple random sampling. The data were collected by trained data collectors using a checklist. Statistical Package for the Social Sciences version 20 and R software were used for data entry and to process the data, respectively. First, a single covariate analysis was done using Cox-PH and univariate frailty models. Then, all variables that were significant were included in the multivariable analysis. Results: The median survival time of hypertensive patients to attain good control was 48 months, and the mean survival time was 43.6 months. Age and systolic blood pressure (SBP) of patients had a negative relationship with the outcome. However, fasting blood sugar (FBS) had a positive relationship with the outcome. Moreover, the results showed that the progression of outcome depended on the patient’s age. Conclusion: Cox-PH-based analysis revealed that the factors that affected good control of hypertensive patients were age, SBP, FBS, and creatinine. The result of the univariate frailty analysis showed that there was unobserved heterogeneity between individuals in the study setup, which indicated that there were unmeasured covariates. Keywords: hypertension, time-to-event, Cox proportional hazard, frailty model, Schoenfeld residuals, good control of hypertension
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In our study, family history of psychiatric illnesses was not associated with recurrence of MDD.
“Results regarding family history and recurrence also have been inconsistent–a number of previous studies have reported that a family history of psychiatric illnesses was not associated with recurrence of MDD [11, 44, 64, 65], similar to what we have found in our study.”
From Predictors of recurrence of major depressive disorder (2020) · cites this paper as [44]
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Chobanian, A., Bakris, G., Black, H., Cushman, W., Green, L., Izzo, J., Jones, D. W., Materson, B., Oparil, S., Wright, J. T., et al. (2003) · Journal of the American Medical Association (JAMA) · doi:10.1097/00132578-200307000-00027 · Cited by 26,188 · see citing works
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Expanding the Definition and Classification of Hypertension
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Predictors of recurrence of major depressive disorder
Lye, M.-S., Tey, Y.-Y., Tor, Y.-S., Shahabudin, A. F., Ibrahim, N., Ling, K.-H., Stanslas, J., Loh, S.-P., Rosli, R., Lokman, K. A., et al. (2020) · PLOS ONE · doi:10.1371/journal.pone.0230363 · Cited by 43 · see citing works
- Authors
- Melese Sendek, E., & Hussen Hebo, S.
- Venue
- Open Access Medical Statistics
- Publisher
- Informa UK Limited
- Published
- 2017-05
- Volume
- Volume 7
- Pages
- 27-36
- ISSN
- 2230-3251 (ISSN-L)
- OpenAlex
- W2611339244
- Cited by
- 8
- References
- 5
- Citations by year
- 2026: 2 · 2024: 2 · 2023: 1 · 2022: 1 · 2020: 1 · 2019: 1
- FWCI
- 0.42
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Open Access Medical Statistics (publishedVersion, cc-by-nc)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- Funders
- Ministry of Education, Ethiopia
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Blood Pressure and Hypertension Studies
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Keywords
- hypertension, Cox proportional hazards model, frailty models, unobserved heterogeneity, systolic blood pressure, fasting blood glucose, creatinine, age, survival analysis, time to event
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