Research Publications (Applied Sciences)
Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/213
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Item Survival analysis of patients with multidrug-resistant tuberculosis in KwaZulu-Natal, South Africa : a comparison of cox regression and parametric models(Common Ground Research Networks, 2024-06-21) Mbona, Sizwe Vincent; Mwambi, Henry; Ramroop, Shaun; Chifurira, RetiusResearchers in medical sciences often prefer the Cox semi-parametric model instead of parametric models because of its restrictive distributional assumptions, but under certain circumstances, parametric models estimate the parameters more efficiently and powerful than the Cox model. The objective of this study was to compare the Cox and parametric models by studying a dataset of patients diagnosed with multidrug-resistant tuberculosis (MDR-TB). A total of 1 542 patients were included in the study from four decentralised sites located in rural areas and one centralised hospital in KwaZulu-Natal, South Africa from 1 July 2008 to 30 July 2012. Out of 1 542 patients with MDR-TB, 886 (57.5%) were cured and 245 (15.9%) died. According to the AIC, the Lognormal and Weibull regression models were the best fitting to data and the Cox regression model was the weakest. According to the results from parametric models, baseline weight of patients had an increased risk of death in both univariate and multivariate analysis. Patients with ages 31 – 40, 41 - 50 and >50 years at diagnosis had an increased risk for death in Cox proportional hazards model. In univariate analysis the data strongly supported the Lognormal regression among parametric models, while in multivariate analysis Weibull and Lognormal are approximately similar, according to Akaike Information Criterion. Although it seems that there may not be a single model that is substantially better than others, Lognormal is the most favorable as an alternative to Cox for identifying risk factors for patients with MDR-TB.Item The performances of the Cox, Andersen-Gill, Prentice- Williams-Peterson-total time and Wei-Lin-Weissfeld- total-time models in Identifying risk factors in patients with recurrent diseases : a kidney infections example(2024-07-19) Mbona, Sizwe Vincent; Ananth, Anisha; Mzamane, Tsepang Patrick; Ndlovu, Bonginkosi, DuncanIn many longitudinal studies, when subjects are followed over a period of time, recurrent event frequently occur. However, some analysis focusses only on time to the first event, ignoring the subsequent events. The main objective of this paper was to compare the extended standard Cox models, such as Andersen-Gill (AG), Prentice-Williams-Peterson total time (PWP-TT), PWP-Gap time model, Wei Lin-Weissfeld total time (WLW-TT), and Cox frailty model, to identify risk factors associated with kidney re-infection. Empirical evaluation and comparison of these different models were performed. The better model was assessed based on the goodness of fit criteria (AIC, BIC and likelihood ratio test). Kidney data that was downloaded from the R statistical software using the command data(“kidney”) was used to perform analyses in this study. The PWP-TT model had lower standard errors, AIC and BIC values compared to other models, therefore fitted data better and was used to interpret results. The results showed that 81% (HR = 0.19; 95% CI: 0.09-0.39) of the female patients were less likely to experience kidney reinfection than male patients. The risk of recurrent kidney infection was significantly high (HR = 2.32; 95% CI: 1.25-4.29) to patients having an Acute Neptiritis (AN) disease compared to patients with other diseases. While the prevalence of kidney infection remains the public health problem, intervention strategies and awareness campaigned are needed to in order to minimize risk factors behind the recurrent of the disease.Item Multiple imputation using chained equations for missing data in survival models : applied to multidrug-resistant tuberculosis and HIV data(PAGEPress Publications, 2023) Mbona, Sizwe Vincent; Ndlovu, Principal; Mwambi, Henry; Ramroop, ShaunMissing data are a prevalent problem in almost all types of data analyses, such as survival data analysis.Objective
To evaluate the performance of multivariable imputation via chained equations in determining the factors that affect the survival of multidrug-resistant-tuberculosis (MDR-TB) and HIV-coinfected patients in KwaZulu-Natal.Materials and methods
Secondary data from 1542 multidrug-resistant tuberculosis patients were used in this study. First, data from patients with some missing observations were deleted from the original data set to obtain the complete case (CC) data set. Second, missing observations in the original data set were imputed 15 times to obtain complete data sets using a multivariable imputation case (MIC). The Cox regression model was fitted to both the CC and MIC data, and the results were compared using the model goodness of fit criteria [likelihood ratio tests, Akaike information criterion (AIC), and Bayesian Information Criterion (BIC)].Results
The Cox regression model fitted the MIC data set better (likelihood ratio test statistic =76.88 on 10 df with P<0.01, AIC =1040.90, and BIC =1099.65) than the CC data set (likelihood ratio test statistic =42.68 on 10 df with P<0.01, AIC =1186.05 and BIC =1228.47). Variables that were insignificant when the model was fitted to the CC data set became significant when the model was fitted to the MIC data set.Conclusion
Correcting missing data using multiple imputation techniques for the MDR-TB problem is recommended. This approach led to better estimates and more power in the model.Item The importance of the frailty effect in survival models : for multidrug-resistant tuberculosis data(Bentham Science Publishers Ltd., 2023-09-25) Mbona, Sizwe Vincent; Mwambi, Henry; Ramroop, ShaunFrailty models have been proposed to analyse survival data, considering unobserved covariates (frailty effects). In a shared frailty model, frailties are common (or shared) amongst groups of individuals and are randomly distributed across groups. Objective: In this paper, the authors compared the semi-parametric model to shared frailty models by studying the time-to-death of patients with multidrug-resistant tuberculosis (MDR-TB). Methods: Secondary data from 1 542 multidrug-resistant tuberculosis patients were used in this study. STATA software was used to analyse frailty models via the streg command. Results: Of 1 542 patients diagnosed with MDR-TB, 245 (15.9%) died during the study period; 77 (5.0%) had treatment failure; 334 (21.7%) defaulted; 213 (13.8%) completed treatment; 651 (42.2%) were cured of MRD-TB; and 22 (1.4%) were transferred out. The results showed that 797 (51.7%) were females, and the majority were aged 18 – 30 and 31 – 40 years (35.5% and 35.7% respectively). Most of the patients (71.3%) were HIV-positive. The results also showed that most patients (95.7%) had no previous MDR-TB episodes, and 792 (51.4%) had no co-morbidities. The estimate of the variance for the frailty term in the Weibull gamma shared frailty model was 2.83, which is relatively large and therefore suggests the existence of heterogeneity. Conclusion: The Laplace transform of the frailty distribution plays a central role in relating the hazards, conditional on the frailty, to the hazards and survival functions observed in a population.Item Identifying factors that affect the probability of being cured from MDR-TB disease, KwaZulu-Natal, South Africa : a competing risks analysis(Scientific Research Publishing, Inc., 2022) Mbona, Sizwe Vincent; Mwambi, Henry; Chifurira, RetiusFour decentralised sites are located in rural areas and one centralised hospital in KwaZulu-Natal province, South Africa. Objective: To analyse risk factors associated with multidrug-resistant tuberculosis (MDR-TB) using com peting risks analysis. Understanding factors associated with MDR-TB and obtaining valid parameter estimates could help in designing control and in tervention strategies to lower TB mortality. Method: A prospective study was performed using a competing risk analysis in patients receiving treatment for MDR-TB. The study focused on 1542 patients (aged 18 years and older) who were diagnosed of MDR-TB between July 2008 and June 2010. Time to cure MDR-TB was used as the dependent variable and time to death was the com peting risk event. Results: The Fine-Gray regression model indicated that base line weight was highly significant with sub-distribution hazard ration (SHR) = 1.02, 95% CI: 1.01 - 1.02. This means that weight gain in a month increased chances of curing MDR-TB by 2%. Results show that lower chances to cure MDR-TB were among patients between 41 to 50 years compared to those pa tients who were between 18 to 30 years old (SHR = 0.80, 95% CI: 0.61 - 1.06). The chances of curing MDR-TB in female patients were low compared to male patients (SHR = 0.84, 95% CI = 0.68 - 1.03), however this was not sig nificant. Furthermore, HIV negative patients had higher chances to cure MDR-TB (SHR = 1.07, 95% CI: 0.85 - 1.35) compared to HIV positive pa tients. Patients who were treated in the decentralised sites had lower chances to be cured of MDR-TB (SHR = 0.19, 95% CI: 0.07 - 0.54) as compared to pa tients who were treated in the centralised hospital. Conclusion: Identifying key factors associated with TB and specifying strategies to prevent them can reduce mortality of patients due to TB disease, hence positive treatment out comes leading to the goal of reducing or end TB deaths. Urgent action is re quired to improve the coverage and quality of diagnosis, treatment and care for people with drug-resistant TB.