Inferring latent heterogeneity using many feature variables supervised by survival outcome.

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

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

Afiliação

Jia B; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Zeng D; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Liao JJZ; Incyte Corporation, Wilmington, Delaware, USA.
Liu GF; Biostatistics and Research Decision Sciences, Merck & Co., Inc, Kenilworth, Pennsylvania, USA.
Tan X; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Diao G; Department of Biostatistics and Bioinformatics, The George Washington University, Washington, District of Columbia, USA.
Ibrahim JG; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Stat Med ; 40(13): 3181-3195, 2021 06 15.

Article em En | MEDLINE | ID: mdl-33819928

Assuntos

Medicina de Precisão; Teorema de Bayes; Biomarcadores; Simulação por Computador; Humanos; Probabilidade

Palavras-chave

adaptive lasso; censoring; latent model; mixture distribution; oracle property

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Medicina de Precisão Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

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