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CSDA
2016

Bayesian network data imputation with application to survival tree analysis

4 years 5 months ago
Bayesian network data imputation with application to survival tree analysis
Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the origin...
Paola M. V. Rancoita, Marco Zaffalon, Emanuele Zuc
Added 01 Apr 2016
Updated 01 Apr 2016
Type Journal
Year 2016
Where CSDA
Authors Paola M. V. Rancoita, Marco Zaffalon, Emanuele Zucca, Francesco Bertoni, Cassio Polpo de Campos
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