Risk prediction models for hip fracture: parametric versus Cox regression

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dc.contributor.advisorTing Lee, Mei-lingen_US
dc.contributor.authorLoo, Geok Yanen_US
dc.contributor.departmentEpidemiology and Biostatisticsen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2013-10-10T05:37:13Z
dc.date.available2013-10-10T05:37:13Z
dc.date.issued2013en_US
dc.description.abstractHip fracture is a public health burden due to high morbidity, mortality and cost. Risk prediction models can aid clinical decision-making by identifying individuals at risk. Objective: To build risk prediction model for incident hip fracture using Weibull regression and compare this with Cox regression model. Method: The Study of Osteoporosis prospectively collected risk factors were used to build a risk prediction model for first hip fracture using Threshold regression with Wiener process. Similar predictors were fitted using Cox regression for comparison. Results: There were 632 first hip fractures. Age, bone density, maternal and personal prior fractures were significant risk factors for hip fracture. Weibull had better goodness of fit, higher D-statistic and R-squared values than the exponential. Models did not differ in c-index and ten-fold cross validation showed similar areas under the ROC curves. Conclusion: Parametric and Cox models were comparable. External validation of the prediction model is required.en_US
dc.identifier.urihttp://hdl.handle.net/1903/14688
dc.subject.pqcontrolledBiostatisticsen_US
dc.subject.pqcontrolledPublic healthen_US
dc.subject.pqcontrolledAgingen_US
dc.subject.pquncontrolledCox Proportional Hazards regressionen_US
dc.subject.pquncontrolledexponential regressionen_US
dc.subject.pquncontrolledhip fractureen_US
dc.subject.pquncontrolledthreshold regressionen_US
dc.subject.pquncontrolledWeibull regressionen_US
dc.titleRisk prediction models for hip fracture: parametric versus Cox regressionen_US
dc.typeThesisen_US

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