2026-09-20T04:59:09Zhttps://api.drum.lib.umd.edu/server/oai/request

oai:drum.lib.umd.edu:1903/146882016-03-29T11:37:29Zcom_1903_7125com_1903_1633com_1903_2col_1903_7129col_1903_3

Ting Lee, Mei-ling Loo, Geok Yan Digital Repository at the University of Maryland University of Maryland (College Park, Md.) Epidemiology and Biostatistics 2013-10-10T05:37:13Z 2013-10-10T05:37:13Z 2013 http://hdl.handle.net/1903/14688 Hip 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. Risk prediction models for hip fracture: parametric versus Cox regression Thesis Biostatistics Public health Aging Cox Proportional Hazards regression exponential regression hip fracture threshold regression Weibull regression open.access