Details der Publikation - Mixture survival trees for cancer risk classification

Mixture survival trees for cancer risk classification

Abstract In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer..

Medienart:	E-Artikel

Erscheinungsjahr:	2022
Erschienen:	2022

Enthalten in:	Zur Gesamtaufnahme - volume:28
Enthalten in:	Lifetime data analysis - 28(2022), 3 vom: 29. Apr., Seite 356-379

Sprache:	Englisch

Beteiligte Personen:	Jia, Beilin [VerfasserIn] Zeng, Donglin [VerfasserIn] Liao, Jason J. Z. [VerfasserIn] Liu, Guanghan F. [VerfasserIn] Tan, Xianming [VerfasserIn] Diao, Guoqing [VerfasserIn] Ibrahim, Joseph G. [VerfasserIn]

Links:	Volltext [lizenzpflichtig]

BKL:	31.73$jMathematische Statistik 44.32$jMedizinische Mathematik$jmedizinische Statistik
Themen:	Censoring Latent model Mixture distribution Risk classification Tree-based method

RVK:	RVK Klassifikation ELIB23 ELIB24

Anmerkungen:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

doi:	10.1007/s10985-022-09552-w

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	OLC2131244630

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520			\|a Abstract In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.
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Mixture survival trees for cancer risk classification

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