Mixture survival trees for cancer risk classification
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature..
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 |
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Erscheinungsjahr: |
2022 |
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Erschienen: |
2022 |
Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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Enthalten in: |
Lifetime data analysis - 28(2022), 3 vom: 29. Juli, Seite 356-379 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Jia, Beilin [VerfasserIn] |
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Themen: |
Censoring |
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Date Completed 12.07.2022 Date Revised 12.09.2023 published: Print-Electronic Citation Status MEDLINE |
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doi: |
10.1007/s10985-022-09552-w |
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funding: |
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PPN (Katalog-ID): |
NLM340150858 |
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520 | |a 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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700 | 1 | |a Diao, Guoqing |e verfasserin |4 aut | |
700 | 1 | |a Ibrahim, Joseph G |e verfasserin |4 aut | |
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