Evidence map›Paper›PMID 35486260›Full record

ArticleLifetime data analysis2022

Mixture survival trees for cancer risk classification.

Beilin Jia, Donglin Zeng, Jason J Z Liao, Guanghan F Liu, Xianming Tan, Guoqing Diao, Joseph G Ibrahim

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Lifetime data analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Pseudo-value regression trees.Lifetime data analysis · 2024
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Beilin JiaDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. beilinjia@gmail.com.ORCID 0000-0001-6586-457X
Donglin ZengDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Jason J Z LiaoIncyte Corporation, Wilmington, DE, USA.
Guanghan F LiuBiostatistics and Research Decision Sciences, Merck & Co., Inc, North Wales, PA, USA.
Xianming TanDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Guoqing DiaoDepartment of Biostatistics and Bioinformatics, The George Washington University, Washington, DC, USA.
Joseph G IbrahimDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

Statistical and Machine Learning Methods to Improve Dynamic Treatment Regimens Estimation Using Real World Data.R01GM124104 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yuanjia Wang, Donglin Zeng · 2018 to 2026
$3.1M
Novel Deep Learning Tools for Clinical Decision Support in Postoperative Pain ManagementR56LM013784 · NLM · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ZOU, BAIMING · 2022 to 2023
$832k
NIGMS NIH HHS R01 GM124104NLM NIH HHS R56 LM013784
6 · The paper itself

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.

Indexed as

AlgorithmsNeoplasmsComputer SimulationHumansLikelihood FunctionsResearch DesignCensoringLatent modelMixture distributionRisk classificationTree-based method

Identifiers

PMID35486260
PMCPMC10402927

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.