Trees and splines in survival analysis.
During the past few years several nonparametric alternatives to the Cox proportional hazards model have appeared in the literature. These methods extend techniques that are well known from regression analysis to the analysis of censored survival data. In this paper we discuss methods based on (partition) trees and (polynomial) splines, analyse two datasets using both Survival Trees and HARE, and compare the strengths and weaknesses of the two methods. One of the strengths of HARE is that its model fitting procedure has an implicit check for proportionality of the underlying hazards model. It also provides an explicit model for the conditional hazards function, which makes it very convenient to obtain graphical summaries. On the other hand, the tree-based methods automatically partition a dataset into groups of cases that are similar in survival history. Results obtained by survival trees and HARE are often complementary. Trees and splines in survival analysis should provide the data analyst with two useful tools when analysing survival data.
Intrator
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O
Department of Statistics, Hebrew University, Jerusalem, Israel.
Kooperberg
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C
D000465:Algorithms; D001943:Breast Neoplasms / Q000401:mortality; D003327:Coronary Disease / Q000401:mortality; D003627:Data Interpretation, Statistical; D003663:Decision Trees; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012307:Risk Factors; D012984:Software; D018709:Statistics, Nonparametric; D016019:Survival Analysis