Cost-saving tree-structured survival analysis for hip fracture of study of osteoporotic fractures data.
It is important to predict osteoporotic fracture risk accurately in order to select high-risk patients for treatment. Previous tree-structured survival analysis (TSSA) methods focused on optimization in statistical performance in construction of survival trees. However, they did not take into account the cost of the predictive variables. Because of the high cost of some predictors, the derived algorithm may have only limited application in practice. In this article, the authors consider the cost-effectiveness in TSSA and propose a cost-saving TSSA (denoted as CSTSSA) to construct the survival tree for identifying subjects at high risk of hip fracture based on the data from Study of Osteoporotic Fractures. The new rule is compared with the optimum classification based on log-rank test statistics using the noninferiority test by Lu and others. The comparison results suggest that, for identifying patients at high risk of hip fracture, the CSTSSA is a good alternative to the optimum classification.
Jin
Hua
H
School of Mathematical Sciences, South China Normal University, Guangzhou, China (HJ)
Lu
Ying
Y
Department of Radiology, Department of Epidemiology and Biostatistics, UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco (YL)
D000368:Aged; D017046:Cost Savings; D005260:Female; D006620:Hip Fractures / Q000209:etiology / Q000503:physiopathology*; D006801:Humans; D018803:Models, Economic; D010024:Osteoporosis / Q000150:complications*; D016019:Survival Analysis