This note variable selection in the semiparametric linear regression model for censored data. Semiparametric linear regression for censored data is a natural extension of the linear model for uncensored data; however, random censoring introduces substantial theoretical and numerical challenges. By now, a number of authors have made significant contributions for estimation and inference in the s...

The accelerated failure time (AFT) model is an important alternative to the Cox proportional hazards model (PHM) in survival analysis. For multivariate failure time data we propose to use frailties to explicitly account for possible correlations (and heterogeneity) among failure times. An EM-like algorithm analogous to that in the frailty model for the Cox model is adapted. Through simulation i...

Cardiovascular disease is the major cause of morbidity and mortality in chronic renal failure. The aim of this review is to summarise current evidence suggesting that there is increased free radical production, antioxidant depletion and changes in lipoprotein composition in renal failure which will lead to oxidation of LDL and hence to accelerated development of atherosclerosis.

We extend the Dahlberg and Wang (Biometrics 2007, 63, 1237-1244) proportional hazards (PH) cure model for the analysis of time-to-event data that is subject to a cure rate with masked event to a setting where the PH assumption does not hold. Assuming an accelerated failure time (AFT) model with unspecified error distribution for the time to the event of interest, we propose rank-based estimatin...

Chen (2009, Biometrics) studies the semi-parametric accelerated failure time model for data that are size biased. Chen considers only the uncensored case and uses hazard-based estimation methods originally developed for censored observations. However, for uncensored data, a simple linear regression on the log scale is more natural and provides better estimators.

The accelerated failure time (AFT) model assumes a linear relationship between the event time and the covariates. We propose a robust weighted least-absolute-deviations (LAD) method for estimation in the AFT model with right-censored data. This method uses the Kaplan-Meier weights in the LAD objective function to account for censoring. We show that the proposed estimator is root-n consistent an...

We provide new conditions for identi…cation of accelerated failure time competing risks models. These include Roy models and some auction models. In our set up, unknown regression functions and the joint survivor function of latent disturbance terms are all nonparametric. We show that this model is identi…ed given covariates that are independent of latent errors, provided that a certain rank co...

We describe a Bayesian semiparametric (failure time) transformation model for which an unknown monotone transformation of failure times is assumed linearly dependent on observed covariates with an unspecified error distribution. The two unknowns: the monotone transformation and error distribution are assigned prior distributions with large supports. Our class of regression model includes the pr...

In this article, we formulate a semiparametric model for counting processes in which the effect of covariates is to transform the time scale for a baseline rate function. We assume an arbitrary dependence structure for the counting process and propose a class of estimating equations for the regression parameters. Asymptotic results for these estimators are derived. In addition, goodness of fit ...

SUMMARY In the presence of high-dimensional predictors, it is challenging to develop reliable regression models that can be used to accurately predict future outcomes. Further complications arise when the outcome of interest is an event time, which is often not fully observed due to censoring. In this article, we develop robust prediction models for event time outcomes by regularizing the Gehan...