Promote sign consistency in cure rate model with Weibull lifetime

<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 sign consistency in the cure model estimation and selection

In survival analysis, when a subset of subjects has extremely long survival, the two-part cure rate model has been commonly adopted. In the two-part model, the first part is for a binary response and describes the probability of cure. The second part is for a survival response and describes the probability of survival. Despite their intuitive interconnections, most of the existing works estimate the two parts without any constraint. The existing works on proportionality promote similarity in magnitudes (i.e. quantitative similarity) and can be too restrictive. In this study, for the two-part cure rate model, we propose imposing a sign-based penalty to promote similarity in signs (i.e. qualitative similarity). The proposed strategy can be more informative than those that neglect the two-part interconnections and be less restrictive than the existing proportionality works. Penalty is also imposed to select relevant variables and accommodate high-dimensional data. Numerical studies, including simulation and two data analyses, demonstrate the advantageous performance of the proposed approach.

Sign-consistency based variable importance for machine learning in brain imaging

An important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable importance becomes a challenge in such scenarios. This paper proposes a new measure of variable importance termed sign-consistency bagging (SCB). The SCB captures variable importance by analyzing the sign consistency of the corresponding weights in an ensemble of linear support vector machine (SVM) classifiers. Further, the SCB variable importances are enhanced by means of transductive conformal analysis. This extra step is important when the data can be assumed to be heterogeneous. Finally, the proposal of these SCB variable importance measures is completed with the derivation of a parametric hypothesis test of variable importance. The new importance measures were compared with a t-test based univariate and an SVM-based multivariate variable importances using anatomical and functional magnetic resonance imaging data. The obtained results demonstrated that the new SCB based importance measures were superior to the compared methods in terms of reproducibility and classification accuracy.