Med Decis Making - Cost-saving tree-structured survival analysis for hip fracture of study of osteoporotic fractures data.

Tópicos

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Resumo

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.

Resumo Limpo

import predict osteoporot fractur risk accur order select highrisk patient treatment previous treestructur surviv analysi tssa method focus optim statist perform construct surviv tree howev take account cost predict variabl high cost predictor deriv algorithm may limit applic practic articl author consid costeffect tssa propos costsav tssa denot cstssa construct surviv tree identifi subject high risk hip fractur base data studi osteoporot fractur new rule compar optimum classif base logrank test statist use noninferior test lu other comparison result suggest identifi patient high risk hip fractur cstssa good altern optimum classif

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