Predictive Accuracy of Fixed and Random Survival Models: A case of TB-LAM Clinical Trial

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dc.contributor.authorLeonard, Oscar
dc.date.accessioned2023-03-31T06:25:52Z
dc.date.available2023-03-31T06:25:52Z
dc.date.issued2023
dc.descriptionMaster's Dissertation (Mathematical Sciences)en_US
dc.description.abstractThe current study compared the prediction accuracy and covariate selection performance of the fitted fixed and random models in covariates without validating the proportional hazard assumption. Given the complexities of the covariate structures and the higher-dimensional interactions between the explanatory covariates, predicting clinical trial results and treatment responses remains challenging As a result, effective survival prediction models that can manage and understand complex structures and variable interactions are definitely required. The Cox proportional hazard (Cox PH) model is the most widely used to predict survival time, despite its limitations due to complicated interactions and non-linearity in covariates. Random survival model, particularly random survival forest (RSF), has gained popularity in recent years due to its capacity to understand complicated interactions among explanatory covariates and nonlinearity effects among predictors. Accordingly, the tuberculosis and human immunodeficiency virus (TB-HIV) co-infection dataset was obtained from Kibong'oto Infectious Diseases Hospital in Tanzania using a retrospective cohort design. The concordance index(C-index), Brier score (BS), and Integrated Brier score (IBS) were used to calculate prediction accuracy. Furthermore, binary logistic regression were used to examine variables related to the outcome status, which signified survival or death, and was compared to predictors related to time-to-event using the Cox regression model. Consequently, the RSF model outperformed the Cox PH model in both prediction accuracy and covariate selection performance in C-index, BS, and IBS outcomes. Similarly, the covariates chosen by the binary logistic regression model based on the outcome status differed slightly from the variables picked by the Cox PH based on the time to event. The RSF model outperformed Cox PH in prediction accuracy in the TB-HIV co-infection dataset without verifying the PH assumption of the covariates, suggesting that the RSF model should be used to analyze survival datasets regardless of whether the variables' proportional hazard assumption is satisfied.en_US
dc.identifier.citationOscar, L. (2023). Predictive accuracy of fixed and random survival models: A aase of TB-LAM clinical trial (Master's dissertation). The University of Dodoma. Dodoma.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12661/3620
dc.language.isoenen_US
dc.publisherThe University of Dodomaen_US
dc.subjectPredictive accuracyen_US
dc.subjectFixed survival modelsen_US
dc.subjectTB-LAMen_US
dc.subjectRandom survival modelen_US
dc.subjectCovariate structureen_US
dc.subjectCovariate selectionen_US
dc.subjectRSF modelen_US
dc.subjectRandom Survival Foresten_US
dc.titlePredictive Accuracy of Fixed and Random Survival Models: A case of TB-LAM Clinical Trialen_US
dc.typeDissertationen_US
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