We consider a new class of transformed hazard rate models. This class contains both the multiplicative hazards model and the additive hazards model as special cases. The sieve maximum likelihood estimators are derived for the model parameters and the estimators for the regression coefficients are shown to be consistent and asymptotically normal with variance achieving the semiparametric efficie...

High-dimensional sparse modeling with censored survival data is of great practical importance, as exemplified by modern applications in high-throughput genomic data analysis and credit risk analysis. In this article, we propose a class of regularization methods for simultaneous variable selection and estimation in the additive hazards model, by combining the nonconcave penalized likelihood appr...

gastric cancer is the most prevalent cancer among men and the third most prevalent cancer among women in iran. its most important reason for death is its belated diagnosis at the advanced stages of the disease. various factors can be effective on the survival of these patients after surgery, which are the major concern in this study.data from 330 patients with gastric cancer who had undergone s...

The additive hazards model has many applications in high-throughput genomic data analysis and clinical studies. In this article, we study the weighted Lasso estimator for the additive hazards model in sparse, high-dimensional settings where the number of time-dependent covariates is much larger than the sample size. Based on compatibility, cone invertibility factors, and restricted eigenvalues ...

McKeague and Sasieni [A partly parametric additive risk model. Biometrika 81 (1994) 501] propose a restriction of Aalen's additive risk model by the additional hypothesis that some of the covariates have time-independent influence on the intensity of the observed counting process. We introduce goodness-of-fit tests for this semiparametric Aalen model. The asymptotic distribution properties of t...