Statistical issues in survival analysis (Part XVVVVVI)
October 23, 2024 In terms of the Cox proportional hazards model, adding in a lasso feature for variable selection or other penalized method was typically done in the partial likelihood. Their method has sought to add the lasso penalty into the full likelihood. As the authors have stated, despite the predominance of the partial likelihood in existing R routines, there are some advantages when using the full likelihood which they pointed out. The first is that the baseline hazard can be modeled explicitly, for example, using a basis function approach such as B-splines (see, e.g., Eilers and Marx 1996), The second is that the full likelihood model can easily be extended by a wide class of frailty distributions including random intercepts and random slopes, The third is that time-varying covariates can be naturally incorporated. The authors stated that the partial likelihood ignores the nonfailure intervals which might influence survival outcomes so information could be lost and estimates could be biased. They have developed a function in the R software, coxFL, which implements the unregularized Cox