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(searched for: doi:10.9734/bjmmr/2017/32123)
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Hakime Ayele Kosa,
Published: 3 August 2021
Scientific Reports, Volume 11, pp 1-13; https://doi.org/10.1038/s41598-021-94905-y

Abstract:
Hypertension is a chronic disease that has a major health problem over the centuries due to its significant contribution to the global burden. The objective of this study was to examine the association of survival time and longitudinal Systolic Blood Pressure (SBP) measurement and finding potential barrier that affects SBP measurement and the survival time of hypertension patients. The study considered a cohort of 318 hypertension patients who were aged 18 years or older and were under follow-up from January 1, 2012, to February 30, 2020, at Arba Minch General Hospital. To analyze the data we employed linear mixed effect model, Weibull model, and joint modeling approach for longitudinal and survival data. The results from joint model analysis indicate that systolic blood pressure measurement is significantly associated with survival time of hypertension patients. The results from the longitudinal sub-model reveals that alcohol use, Khat intake, smoking tobacco, stages of hypertension diseases, adherence to treatment, related diseases, and family history had statistical significant relationship with mean change in the $$\sqrt{SBP}$$ SBP measurement. Furthermore, from the survival sub-model, we found the survival probability of hypertension patients was determined by family history, stages of hypertension disease, related diseases, and smoking tobacco. The analysis suggests that there is a strong association between SBP measurement and survival time of hypertension patients. Thus we recommend aggressive work by all concerned bodies towards awareness creation about the effect of potential barriers.
, Maria Sudell, Marta García-Fiñana, Ruwanthi Kolamunnage-Dona
BMC Medical Research Methodology, Volume 20, pp 1-17; https://doi.org/10.1186/s12874-020-00976-2

Abstract:
Background In clinical research, there is an increasing interest in joint modelling of longitudinal and time-to-event data, since it reduces bias in parameter estimation and increases the efficiency of statistical inference. Inference and prediction from frequentist approaches of joint models have been extensively reviewed, and due to the recent popularity of data-driven Bayesian approaches, a review on current Bayesian estimation of joint model is useful to draw recommendations for future researches. Methods We have undertaken a comprehensive review on Bayesian univariate and multivariate joint models. We focused on type of outcomes, model assumptions, association structure, estimation algorithm, dynamic prediction and software implementation. Results A total of 89 articles have been identified, consisting of 75 methodological and 14 applied articles. The most common approach to model the longitudinal and time-to-event outcomes jointly included linear mixed effect models with proportional hazards. A random effect association structure was generally used for linking the two sub-models. Markov Chain Monte Carlo (MCMC) algorithms were commonly used (93% articles) to estimate the model parameters. Only six articles were primarily focused on dynamic predictions for longitudinal or event-time outcomes. Conclusion Methodologies for a wide variety of data types have been proposed; however the research is limited if the association between the two outcomes changes over time, and there is also lack of methods to determine the association structure in the absence of clinical background knowledge. Joint modelling has been proved to be beneficial in producing more accurate dynamic prediction; however, there is a lack of sufficient tools to validate the prediction.
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