Quasi-linear Cox proportional hazards model with cross- L₁ penalty.

ABSTRACT:

Background

To accurately predict the response to treatment, we need a stable and effective risk score that can be calculated from patient characteristics. When we evaluate such risks from time-to-event data with right-censoring, Cox's proportional hazards model is the most popular for estimating the linear risk score. However, the intrinsic heterogeneity of patients may prevent us from obtaining a valid score. It is therefore insufficient to consider the regression problem with a single linear predictor.

Methods

we propose the model with a quasi-linear predictor that combines several linear predictors. This provides a natural extension of Cox model that leads to a mixture hazards model. We investigate the property of the maximum likelihood estimator for the proposed model. M

SUBMITTER: Omae K 

PROVIDER: S-EPMC7336640 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature