pcoxtime: Penalized Cox Proportional Hazard Model for Time-dependent Covariates
Steve Cygu, Jonathan Dushoff, Benjamin M. Bolker
Abstract
Open-access reader
Steve Cygu, Jonathan Dushoff, Benjamin M. Bolker
Abstract
Open-access reader
The penalized Cox proportional hazard model is a popular analytical approach for survival data with a large number of covariates. Such problems are especially challenging when covariates vary over follow-up time (i.e., the covariates are time-dependent). The standard R packages for fully penalized Cox models cannot currently incorporate time-dependent covariates. To address this gap, we implement a variant of gradient descent algorithm (proximal gradient descent) for fitting penalized Cox models. We apply our implementation to real and simulated data sets.
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The penalized Cox proportional hazard model is a popular analytical approach for survival data with a large number of covariates. Such problems are especially challenging when covariates vary over follow-up time (i.e., the covariates are time-dependent). The standard R packages for fully penalized Cox models cannot currently incorporate time-dependent covariates. To address this gap, we implement a variant of gradient descent algorithm (proximal gradient descent) for fitting penalized Cox models. We apply our implementation to real and simulated data sets.
Key concepts: Covariate, Proportional hazards model, Econometrics, Hazard, Statistics, Hazard model, Mathematics, Biology