Promote sign consistency in cure rate model with Weibull lifetime

Abstract

<abstract> <p>In survival analysis, the cure rate model is widely adopted when a proportion of subjects have long-term survivors. The cure rate model is composed of two parts: the first part is the incident part which describes the probability of cure (infinity survival), and the second part is the latency part which describes the conditional survival of the uncured subjects (finite survival). In the standard cure rate model, there are no constraints on the relations between the coefficients in the two model parts. However, in practical applications, the two model parts are quite related. It is desirable that there may be some relations between the two sets of the coefficients corresponding to the same covariates. Existing works have considered incorporating a joint distribution or structural effect, which is too restrictive. In this paper, we consider a more flexible model that allows the two sets of covariates can be in different distributions and magnitudes. In many practical cases, it is hard to interpret the results when the two sets of the coefficients of the same covariates have conflicting signs. Therefore, we proposed a sign consistency cure rate model with a sign-based penalty to improve interpretability. To accommodate high-dimensional data, we adopt a group lasso penalty for variable selection. Simulations and a real data analysis demonstrate that the proposed method has competitive performance compared with alternative methods.</p> </abstract>

Promoting Variable Effect Consistency in Mixture Cure Model for Credit Scoring

“…e second penalty is the sign consistency penalty. It promotes the sign consistency of α and β in the two parts of the model, which can lead to more interpretable results [31].…”

“…e proposed method is valid as long as τ is not too large, and the parameters with different signs can be distinguished [34]. erefore, as suggested in [31], we set τ � 0.1, which leads to satisfactory results.…”