TY - JOUR
T1 - Incorporating temporal features of repeatedly measured covariates into tree-structured survival models
AU - Wallace, Meredith L.
AU - Anderson, Stewart J.
AU - Mazumdar, Sati
AU - Kong, Lan
AU - Mulsant, Benoit H.
PY - 2012/3
Y1 - 2012/3
N2 - Tree-structured survival methods empirically identify a series of covariate-based binary split points, resulting in an algorithm that can be used to classify new patients into risk groups and subsequently guide clinical treatment decisions. Traditionally, only fixed-time (e.g. baseline) values are used in tree-structured models. However, this manuscript considers the scenario where temporal features of a repeated measures polynomial model, such as the slope and/or curvature, are useful for distinguishing risk groups to predict future outcomes. Both fixed- and random-effects methods for estimating individual temporal features are discussed, and methods for including these features in a tree model and classifying new cases are proposed. A simulation study is performed to empirically compare the predictive accuracies of the proposed methods in a wide variety of model settings. For illustration, a tree-structured survival model incorporating the linear rate of change of depressive symptomatology during the first four weeks of treatment for late-life depression is used to identify subgroups of older adults who may benefit from an early change in treatment strategy.
AB - Tree-structured survival methods empirically identify a series of covariate-based binary split points, resulting in an algorithm that can be used to classify new patients into risk groups and subsequently guide clinical treatment decisions. Traditionally, only fixed-time (e.g. baseline) values are used in tree-structured models. However, this manuscript considers the scenario where temporal features of a repeated measures polynomial model, such as the slope and/or curvature, are useful for distinguishing risk groups to predict future outcomes. Both fixed- and random-effects methods for estimating individual temporal features are discussed, and methods for including these features in a tree model and classifying new cases are proposed. A simulation study is performed to empirically compare the predictive accuracies of the proposed methods in a wide variety of model settings. For illustration, a tree-structured survival model incorporating the linear rate of change of depressive symptomatology during the first four weeks of treatment for late-life depression is used to identify subgroups of older adults who may benefit from an early change in treatment strategy.
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U2 - 10.1002/bimj.201100013
DO - 10.1002/bimj.201100013
M3 - Article
C2 - 22522377
AN - SCOPUS:84860117110
SN - 0323-3847
VL - 54
SP - 181
EP - 196
JO - Biometrical Journal
JF - Biometrical Journal
IS - 2
ER -