Disease progression based feature screening for ultrahigh-dimensional survival-associated biomarkers.
Stat Med
; 42(13): 2082-2100, 2023 06 15.
Article
em En
| MEDLINE
| ID: mdl-36951373
ABSTRACT
The increased availability of ultrahigh-dimensional biomarker data and the high demand of identifying biomarkers importantly related to survival outcomes made feature screening methods commonplace in the analysis of cancer genome data. When survival outcomes include endpoints of overall survival (OS) and time-to-progression (TTP), a high concordance is typically found in both endpoints in cancer studies, namely, patients' OS would most likely be extended when tumour progression is delayed. Existing screening procedures are often performed on a single survival endpoint only and may result in biased selection of features for OS in ignorance of disease progression. We propose a novel feature screening method by incorporating information of TTP into the selection of important biomarker predictors for more accurate inference of OS subsequent to disease progression. The proposal is based on the rank of correlation between individual features and the conditional distribution of OS given observations of TTP. It is advantageous for its flexible model nature, which requires no marginal model assumption for each endpoint, and its minimal computational cost for implementation. Theoretical results show its ranking consistency, sure screening and false rate control properties. Simulation results demonstrate that the proposed screener leads to more accurate feature selection than the method without considering the prior observations of disease progression. An application to breast cancer genome data illustrates its practical utility and facilitates disease classification using selected biomarker predictors.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
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Risk_factors_studies
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Screening_studies
Limite:
Female
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Humans
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China