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Full-Text Articles in Biostatistics
Conditional Screening For Ultra-High Dimensional Covariates With Survival Outcomes, Hyokyoung Grace Hong, Jian Kang, Yi Li
Conditional Screening For Ultra-High Dimensional Covariates With Survival Outcomes, Hyokyoung Grace Hong, Jian Kang, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in …
A Pairwise Likelihood Augmented Estimator For The Cox Model Under Left-Truncation, Fan Wu, Sehee Kim, Jing Qin, Rajiv Saran, Yi Li
A Pairwise Likelihood Augmented Estimator For The Cox Model Under Left-Truncation, Fan Wu, Sehee Kim, Jing Qin, Rajiv Saran, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
Survival data collected from prevalent cohorts are subject to left-truncation and the analysis is challenging. Conditional approaches for left-truncated data under the Cox model are inefficient as they typically ignore the information in the marginal likelihood of the truncation times. Length-biased sampling methods can improve the estimation efficiency but only when the stationarity assumption of the disease incidence holds, i.e., the truncation distribution is uniform; otherwise they may generate biased estimates. In this paper, we propose a semi-parametric method for the Cox model under general left-truncation, where the truncation distribution is unspecified. Our approach is to make inference based on …