Inferring latent heterogeneity using many feature variables supervised by survival outcome-Reference-Cited by-同舟云学术

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Inferring latent heterogeneity using many feature variables supervised by survival outcome

Published:2021-04-05 Issue:13 Volume:40 Page:3181-3195
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statistics in Medicine

Author:

Jia Beilin¹,Zeng Donglin¹,Liao Jason J. Z.²^ORCID,Liu Guanghan F.³^ORCID,Tan Xianming¹^ORCID,Diao Guoqing⁴^ORCID,Ibrahim Joseph G.¹^ORCID

Affiliation:

1. Department of Biostatistics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA

2. Incyte Corporation Wilmington Delaware USA

3. Biostatistics and Research Decision Sciences Merck & Co., Inc Kenilworth Pennsylvania USA

4. Department of Biostatistics and Bioinformatics The George Washington University Washington District of Columbia USA

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.8972

Reference32 articles.

1. A flexible parametric survival model for fitting time to event data in clinical trials

2. The Use of Mixture Models for the Analysis of Survival Data with Long-Term Survivors

3. A Mixture Model for the Regression Analysis of Competing Risks Data

4. Latent Subgroup Analysis of a Randomized Clinical Trial through a Semiparametric Accelerated Failure Time Mixture Model

5. Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model

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