Analysis of overfitting in the regularized Cox model
Analysis of overfitting in the regularized Cox model
复制标题
DOI：
10.1088/1751-8121/ab375c
复制
发表时间：
2019-09-20
影响因子：
2.1
通讯作者：
Coolen, Anthony C. C.
中科院分区：
文献类型：
--
作者：
Sheikh, Mansoor;Coolen, Anthony C. C.
The Cox proportional hazards model is ubiquitous in the analysis of time-to-event data. However, when the data dimension p is comparable to the sample size N, maximum likelihood estimates for its regression parameters are known to be biased or break down entirely due to overfitting. This prompted the introduction of the so-called regularized Cox model. In this paper we use the replica method from statistical physics to investigate the relationship between the true and inferred regression parameters in regularized multivariate Cox regression with L-2 regularization, in the regime where both p and N are large but with zeta = p/N similar to O(1). We thereby generalize a recent study from maximum likelihood to maximum a posteriori inference. We also establish a relationship between the optimal regularization parameter and zeta, allowing for straightforward overfitting corrections in time-to-event analysis.
