When fitting the Cox proportional hazards model with missing covariates, it is inefficient to exclude observations with missing values in the analysis. Furthermore, if the missing-data mechanism is not Missing Completely At Random (MCAR), it may lead to biased parameter estimation. Many approaches have been suggested to handle the Cox proportional hazards model when covariates are sometimes mis...

This article concerns the statistical modelling of emergency service response times. We apply advanced methods from spatial survival analysis to deliver inference for data collected by the London Fire Brigade on response times to reported dwelling fires. Existing approaches to the analysis of these data have been mainly descriptive; we describe and demonstrate the advantages of a more sophistic...

This paper proposes a discrete-time hazard regression approach based on the relation between hazard rate models and excess over threshold models, which are frequently encountered in extreme value modelling. The proposed duration model employs a exible link function and incorporates the grouped-duration analogue of the wellknown Cox proportional hazards model and the proportional odds model as s...

Correlated survival times can be modelled by introducing a random effect, or frailty component, into the hazard function. For multivariate survival data we extend a non-PH model, the generalized time-dependent logistic survival model, to include random effects. The hierarchical-likelihood procedure, which obviates the need for marginalization over the random effect distribution, is derived for ...

We investigate the variable selection problem for Cox’s proportional hazards model, and propose a unified model selection and estimation procedure with desired theoretical properties and computational convenience. The new method is based on a penalized log partial likelihood with the adaptively-weighted L1 penalty on regression coefficients, and is named adaptive-LASSO (ALASSO) estimator. Inste...

First hitting time models are a technique of modeling a stochastic process as it approaches or avoids a boundary, also known as a threshold. The process itself may be unobservable, making this a difficult problem. Regression techniques, however, can be employed to model the data as it compares to the threshold, creating a class of first hitting time models called threshold regression models. Su...

We provide an overview of semiparametric models commonly used in survival analysis, including proportional hazards model, proportional odds models and linear transformation models. The applications of these models to different types of censored data, either univariate or multivariate survival analysis, are given. For each case, inference procedures using censored observations are discussed. & 2...

Long-term absenteeism due to sickness has been increasing in the past two decades. This has raised many questions about causes, financing, and policy measures to prevent further increases. Answering these questions is even more important in a society with an aging population, which is expected to record even more cases. With data from the Swedish National Insurance Board, proportional hazards m...

recently, reports have shown that gastric cancer has high abundance in iran and is at the second level in men, and fourth in total. this study aimed to determine the 5-year survival of gastric cancer patients and to investigate factors affecting the performance, based on tnm-7 staging system. in this study, we investigated 760 patients with gastric cancer since the beginning of 1993 to the end ...