Please use this identifier to cite or link to this item: http://ir.library.ui.edu.ng/handle/123456789/5308
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dc.contributor.authorAkanbi, O. B.-
dc.contributor.authorOladoja, O. M.-
dc.contributor.authorUdomboso, C. G.-
dc.date.accessioned2021-05-24T09:04:23Z-
dc.date.available2021-05-24T09:04:23Z-
dc.date.issued2019-
dc.identifier.issn2321-3361-
dc.identifier.otherui_art_akanbi_bayesian_2019-
dc.identifier.otherInternational Journal of Engineering Science and Computing 9(3), pp. 20165-20167-
dc.identifier.urihttp://ir.library.ui.edu.ng/handle/123456789/5308-
dc.description.abstractThe problem of analyzing time to event data arises in a number of applied fields like biology and medicine. This study constructs a survival model of remission duration from a clinical trial data using Bayesian approach. Two covariates; drug and remission status, were used to describe the variation in the remission duration using the Weibull proportional hazards model which forms the likelihood function of the regression vector. Using a uniform prior, the summary of the posterior distribution; Weibull regression model of four parameters ( η, µ,β1, β2, was obtained. With Laplace transform, initial estimates of the location and spread of the posterior density of the parameters were obtained. In this present study, data from children with acute leukemia was used. The information from the Laplace transform was used to find a density for the Metropolis random walk algorithm from Markov Chain Monte Carlos simulation to indicate the acceptance rate (24.55%).en_US
dc.language.isoenen_US
dc.subjectClinical trialen_US
dc.subjectCovariatesen_US
dc.subjectLaplace transformen_US
dc.subjectMetropolis random walk algorithm.en_US
dc.titleBayesian approach to survival modeling of remission duration for acute leukemiaen_US
dc.typeArticleen_US
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