2014
DOI: 10.1155/2014/536976
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Application of Cox Proportional Hazards Model in Case of Tuberculosis Patients in Selected Addis Ababa Health Centres, Ethiopia

Abstract: Introduction. Tuberculosis (TB) is a chronic infectious disease and mainly caused by mycobacterium tuberculosis (MTB). It has been one of the major causes of mortality in Ethiopia. The objective of the study was to identify factors that affect the survival of the patients with tuberculosis who started treatment for tuberculosis. Methods. This was a retrospective study in six randomly selected health centres in Addis Ababa, Ethiopia. The data were obtained from medical records of TB patients registered from Sep… Show more

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Cited by 24 publications
(32 citation statements)
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“…Early mortality reflects advanced disease and could be attributed to delayed treatment and late diagnosis [ 26 ]. In our study, 67.9% of the deaths occurred during the two month (intensive phase) TB treatment period.…”
Section: Discussionmentioning
confidence: 99%
“…Early mortality reflects advanced disease and could be attributed to delayed treatment and late diagnosis [ 26 ]. In our study, 67.9% of the deaths occurred during the two month (intensive phase) TB treatment period.…”
Section: Discussionmentioning
confidence: 99%
“…This model also considers the effect of each factor in delaying the onset of disease rather than just modelling through a binary ‘protected’ or ‘diseased’ outcome. Similar regression modelling has been described to predict outcomes for tuberculosis patients after treatment, effect of hospital-acquired Clostridium difficile on hospital stay and survival of Staphylococcus aureus in milk (73-75). The model assumes that eventually all mice will succumb to disease and due to the constraints in our data, it cannot determine threshold levels of cytokines that will predict disease outcomes.…”
Section: Discussionmentioning
confidence: 97%
“…PH assumption and homogeneity of censoring mechanism need to check before selecting suitable methods. There are three different methods to evaluate PH assumption: 1) graphical assessment (KM curves and ln (-ln(S(t))) vs ln(t) Curves); 2) add a time interaction, x*log(t), into COX regression and test its signi cance; 3) check global good of tness by plotting and testing association between ranked survival time and Schoenfeld residuals [12,13]. violations of the PH assumption suggests existence of interactions between one or more covariates and time.…”
Section: Flowchart For Statistical Methodsmentioning
confidence: 99%