ArticleBiometrics2014
Integrative analysis of prognosis data on multiple cancer subtypes.
Article in Biometrics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.Journal of machine learning research : JMLR · 2024Article
- Integrative Learning of Structured High-Dimensional Data from Multiple Datasets.Statistical analysis and data mining · 2023Article
- Integrative functional linear model for genome-wide association studies with multiple traits.Biostatistics (Oxford, England) · 2022Article
- Weighted Cox regression for the prediction of heterogeneous patient subgroups.BMC medical informatics and decision making · 2021Article
- Integrative sparse partial least squares.Statistics in medicine · 2021Article
- An integrative sparse boosting analysis of cancer genomic commonality and difference.Statistical methods in medical research · 2020Article
- GRIA: Graphical Regularization for Integrative Analysis.Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining · 2020Article
- Simultaneous Covariance Inference for Multimodal Integrative Analysis.Journal of the American Statistical Association · 2020Article
- Model-based optimization of subgroup weights for survival analysis.Bioinformatics (Oxford, England) · 2019Article
- Article
- A Statistical Framework for Pathway and Gene Identification from Integrative Analysis.Journal of multivariate analysis · 2017Article
- Promoting Similarity of Sparsity Structures in Integrative Analysis with Penalization.Journal of the American Statistical Association · 2017Article
- Integrative Analysis of "-Omics" Data Using Penalty Functions.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In cancer research, profiling studies have been extensively conducted, searching for genes/SNPs associated with prognosis. Cancer is diverse. Examining the similarity and difference in the genetic basis of multiple subtypes of the same cancer can lead to a better understanding of their connections and distinctions. Classic meta-analysis methods analyze each subtype separately and then compare analysis results across subtypes. Integrative analysis methods, in contrast, analyze the raw data on multiple subtypes simultaneously and can outperform meta-analysis methods. In this study, prognosis data on multiple subtypes of the same cancer are analyzed. An AFT (accelerated failure time) model is adopted to describe survival. The genetic basis of multiple subtypes is described using the heterogeneity model, which allows a gene/SNP to be associated with prognosis of some subtypes but not others. A compound penalization method is developed to identify genes that contain important SNPs associated with prognosis. The proposed method has an intuitive formulation and is realized using an iterative algorithm. Asymptotic properties are rigorously established. Simulation shows that the proposed method has satisfactory performance and outperforms a penalization-based meta-analysis method and a regularized thresholding method. An NHL (non-Hodgkin lymphoma) prognosis study with SNP measurements is analyzed. Genes associated with the three major subtypes, namely DLBCL, FL, and CLL/SLL, are identified. The proposed method identifies genes that are different from alternatives and have important implications and satisfactory prediction performance.
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Registered trials
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