Premium
Modeling Tumor Growth with Random Onset
Author(s) -
Albert Paul S.,
Shih Joanna H.
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2003.00104.x
Subject(s) - carcinogenesis , mutation , dropout (neural networks) , growth curve (statistics) , computer science , nonlinear system , mathematics , cancer , econometrics , biology , genetics , machine learning , gene , physics , quantum mechanics
Summary . The longitudinal assessment of tumor volume is commonly used as an endpoint in small animal studies in cancer research. Groups of genetically identical mice are injected with mutant cells from clones developed with different mutations. The interest is on comparing tumor onset (i.e., the time of tumor detection) and tumor growth after onset, between mutation groups. This article proposes a class of linear and nonlinear growth models for jointly modeling tumor onset and growth in this situation. Our approach allows for interval‐censored time of onset and missing‐at‐random dropout due to early sacrifice, which are common situations in animal research. We show that our approach has good small‐sample properties for testing and is robust to some key unverifiable modeling assumptions. We illustrate this methodology with an application examining the effect of different mutations on tumorigenesis.