Modeling Survival Time to Death Among TB and HIV Co-infected Adult Patients: an Institution-Based Retrospective Cohort Study.

BACKGROUND: Tuberculosis is a serious health threat, particularly for people living with human immunodeficiency virus (HIV), and HIV-positive people are more likely than others to contract the disease. Globally, tuberculosis (TB) is one of the leading causes of death among people living with HIV. The purpose of the current study was to identify factors associated with survival rates of TB/HIV co-infected patients using survival models. METHODS: A retrospective study was conducted on TB/HIV co-infected adult patients registered and under follow-up at Tepi General Hospital (TGH) and Mizan-Tepi University Teaching Hospital (MTUTH), southwest Ethiopia. All TB/HIV co-infection patients who were registered and under follow-up from 1st January 2015 through 1st January 2020 were considered. The global Schoenfeld test was used to test the proportional hazard (PH) assumption. Various accelerated failure time (AFT) models were compared to determine the best model for the time to death of TB/HIV co-infected patients' data set. Among the most commonly used accelerated failure time models (AFT models), the study used exponential, Weibull, log-logistic, and log-lognormal AFT models. AIC and BIC were used to compare the performance of fitted models. The data were analyzed with the statistical software R. RESULTS: Of 363 TB/HIV co-infected patients followed for 60 months, 79 (21.8%) died, while the remaining 284 (78.2%) were censored. The overall median survival time was 15.6 months. The proportional hazard assumption was checked and it was violated. In comparison to other models, the lognormal AFT model performed better. The results of the multivariable lognormal AFT model showed that age, residence, substance use, educational status, clinical stages of the disease, cluster of differentiation 4 (CD4 count/mm3), functional status, cotrimoxazole prophylactic therapy use (CPT use), and INH were all found to be significant factors, while gender, illness other than TB, and disclosure of status were insignificant variables at 5% level of significance. CONCLUSION: Current study results revealed that older age, substance use, advanced WHO clinical stages of the disease (stage IV), bedridden functional status, and CD4 less than 200 count/mm3 were significantly associated with shorter survival time to death of HIV/TB co-infected patients while having advanced educational status, being from urban residence, CPT use, and INH significantly increase the survival time to death of TB/HIV co-infected patients. Patients with TB/HIV co-infection should be given special attention based on these important factors to improve their health and prolong their lives. HIV-positive patients are more likely than others to contract the TB disease. The risk of death among TB/HIV co-infected patients was found to be high. Out of all patients, 79 (21.8%) died. Accelerated failure time models are good alternatives for scenario Cox proportional hazard assumptions not met.

Application of Parametric Shared Frailty Models to Analyze Time-to-Death of Gastric Cancer Patients.

BACKGROUND: Despite its declining incidence, gastric cancer (GC) is one of the world's leading malignancies and a major global health concern due to its high prevalence and fatality rate. Furthermore, it is the world's fourth most common cancer and the second leading cause of cancer death. Studying the determinants of time to death of gastric cancer patients will give clinicians more information to develop specific treatment plans, forecast prognosis, and track the progress of death cases. The application of the frailty model can help account for random variation in survival that may exist due to unobserved factors, as well as show the impact of latent factors on death risk. As a result, the purpose of this study was to assess the determinants of time to death of GC patients' by applying the parametric shared frailty models. METHODS: The data for this study were obtained from gastric cancer patients admitted to the Tikur Anbesa Specialized Hospital, Addis Ababa, from January 1, 2015, to February 29, 2020. With the aim of coming up with an appropriate survival model that determines factors that affect the time to death of gastric cancer patients, various parametric shared frailty models were compared. In all of the frailty models, patient regions were used as a clustering variable. The current study implemented exponential, Weibull, log-logistic, and lognormal distributions for baseline hazard functions with gamma and inverse Gaussian's frailty distributions. The performance of all models was compared using the AIC and BIC criteria. R statistical software was used to conduct the analysis. RESULTS: A retrospective study was undertaken on a total of 407 gastric cancer patients under follow-up at Tikur Anbesa Specialized Hospital. Of all 407 GC patients, 56.3% died while the remaining 43.7% were censored. The patients' median time to death was 21.9 months, with a maximum survival time of 49.6 months. In the current study, the clustering effect was significant in modeling the time to death from gastric cancer. The Weibull model with inverse Gaussian frailty has the minimum AIC and BIC value among the candidate models compared. The dependency within the clusters for the Weibull-inverse Gaussian frailty model was [Formula: see text] (13.4%). According to the results of our best model (Weibull-inverse Gaussian), the sex of the patient, the smoking status, the tumor size, the treatment taken, the vascular invasion, and the disease stage was found to be statistically significant at an alpha = 0.05 significance level. CONCLUSION: Time to death of GC patient's data set was well described by the Weibull-inverse Gaussian shared frailty. Furthermore, Weibull baseline distribution best fits the GC data set as it enables proportional hazard and accelerated failure time model, for time to failure data. There is unobserved heterogeneity between clusters (patient regions), indicating the need to account for this clustering effect. In this study, survival time to death among GC patients was discovered to be small. Covariates like older age, being male, having higher (advanced) stage of GC disease (stage three and stage four), advanced tumor size, being smoker, infected by Helicobacter pylori, and existence of vascular invasion significantly accelerate the time to death of GC patients. In contrast, talking combination of more treatments prolongs the time to death of patients. To improve the health of patients, interventions should be taken based on significant prognostic factors, with special attention dedicated to patients with such factors to prevent GC death.