%0 Journal Article %1 Huang:1998:Biometrics:9883542 %A Huang, X %A Chen, S %A Soong, S J %D 1998 %J Biometrics %K SurvivalAnalysis nonparametric statistics %N 4 %P 1420-1433 %T Piecewise exponential survival trees with time-dependent covariates %U https://www.ncbi.nlm.nih.gov/pubmed/9883542 %V 54 %X Survival trees methods are nonparametric alternatives to the semiparametric Cox regression in survival analysis. In this paper, a tree-based method for censored survival data with time-dependent covariates is proposed. The proposed method assumes a very general model for the hazard function and is fully nonparametric. The recursive partitioning algorithm uses the likelihood estimation procedure to grow trees under a piecewise exponential structure that handles time-dependent covariates in a parallel way to time-independent covariates. In general, the estimated hazard at a node gives the risk for a group of individuals during a specific time period. Both cross-validation and bootstrap resampling techniques are implemented in the tree selection procedure. The performance of the proposed survival trees method is shown to be good through simulation and application to real data.

@article{Huang:1998:Biometrics:9883542, abstract = {Survival trees methods are nonparametric alternatives to the semiparametric Cox regression in survival analysis. In this paper, a tree-based method for censored survival data with time-dependent covariates is proposed. The proposed method assumes a very general model for the hazard function and is fully nonparametric. The recursive partitioning algorithm uses the likelihood estimation procedure to grow trees under a piecewise exponential structure that handles time-dependent covariates in a parallel way to time-independent covariates. In general, the estimated hazard at a node gives the risk for a group of individuals during a specific time period. Both cross-validation and bootstrap resampling techniques are implemented in the tree selection procedure. The performance of the proposed survival trees method is shown to be good through simulation and application to real data.}, added-at = {2018-10-03T05:52:46.000+0200}, author = {Huang, X and Chen, S and Soong, S J}, biburl = {https://www.bibsonomy.org/bibtex/29b9fa28bb7dcfd8a45ca5085c3e535b8/jkd}, description = {Piecewise exponential survival trees with time-dependent covariates. - PubMed - NCBI}, interhash = {db9c87c0f3da691c40b6a907371033b4}, intrahash = {9b9fa28bb7dcfd8a45ca5085c3e535b8}, journal = {Biometrics}, keywords = {SurvivalAnalysis nonparametric statistics}, month = dec, number = 4, pages = {1420-1433}, pmid = {9883542}, timestamp = {2018-10-03T05:52:46.000+0200}, title = {Piecewise exponential survival trees with time-dependent covariates}, url = {https://www.ncbi.nlm.nih.gov/pubmed/9883542}, volume = 54, year = 1998 }