This paper discusses the possible ways to analyse the data, adopting a matrix notation, so often used in Bioassays. The paper also reviews the Multistage Models (MM). The MM class of models is applied for extrapolation, to the region of Low-Dose. The effect of covariates in experimental carcinogenesis is introduced and the relative efficiency is evaluated. Certainly the discussed case was refereed to uncorrelated covariates and therefore an open problem might be the multicollinear predictive covariates.Various nonlinear models are discussed, giving more emphasis on the Michaelis-Menten and the Fisher's information for them is discussed
Functional proportional hazards mixture cure model and its application to modelling the association between cancer mortality and physical activity in NHANES 2003-2006
Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data
Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway（癌症遗传学系，癌症研究所，奥斯陆大学医院，奥斯陆，挪威）；Oslo Centre for Biostatistics and Epidemiology (OCBE), Department of Biostatistics, University of Oslo, Oslo, Norway（奥斯陆生物统计与流行病学中心（OCBE），生物统计学系，奥斯陆大学，奥斯陆，挪威）；Department of Informatics, University of Oslo, Oslo, Norway（信息学系，奥斯陆大学，奥斯陆，挪威）；Department of Statistics, Ludwig Maximilian University of Munich, Munich, Germany（统计学系，慕尼黑路德维希-马克西米利安大学，慕尼黑，德国）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））
