Choose the Cox proportional hazards regression model if the values in your dependent variable are duration observations. The advantage of the semi-parametric Cox proportional hazards model over fully parametric models such as the exponential or Weibull models is that it makes no assumptions about the shape of the baseline hazard. The model only requires the proportional hazards assumption that ...

We propose a robust Cox regression model with outliers. The model is fit by trimming the smallest contributions to the partial likelihood. To do so, we implement a Metropolis-type maximization routine, and show its convergence to a global optimum. We discuss global robustness properties of the approach, which is illustrated and compared through simulations. We finally fit the model on an origin...

We deal with two kinds of Cox regression models with varying coefficients. The coefficients vary with time in one model. In the other model, there is an important random variable called an index variable and the coefficients vary with the variable. In both models, we have p-dimensional covariates and p increases moderately. However, it is the case that only a small part of the covariates are re...

Time-dependent Cox regression and landmarking are the two most commonly used approaches for the analysis of time-dependent covariates in time-to-event data. The estimated effect of the time-dependent covariate in a landmarking analysis is based on the value of the time-dependent covariate at the landmark time point, after which the time-dependent covariate may change value. In this note we deri...

The branch of statistics relative to analysis of predictable period of time until one or more event occurs is termed survival analysis. Survival analysis is a system of data collection and analysis where the outcome variable is the time until the event of an interest occurs [1]. The technique deals with the creation of timing data that goes with event of either failure or death. However the pat...

In the first part, entitled “Model assisted Cox regression” and published in Journal of Multivariate Analysis (JMVA), it was shown that standard Cox regression, combined with Dikta’s semiparametric random censorship models, provides an effective framework for obtaining improved parameter estimates. Here, this methodology is exploited to construct simultaneous confidence bands (SCBs) for subject...

We derive information bounds for the regression parameters in Cox models when data are missing at random. These calculations are of interest for understanding the behavior of efficient estimation in case-cohort designs, a type of two-phase design often used in cohort studies. The derivations make use of key lemmas appearing in Robins, Rotnitzky and Zhao [J. Amer. Statist. Assoc. 89 (1994) 846–8...

The standard Cox proportional hazards model has been extended by functionally describable interaction terms. The first of which are related to neural networks by adopting the idea of transforming sums of weighted covariables by means of a logistic function. A class of reasonable weight combinations within the logistic transformation is described. Apart from the standard covariable product inter...

The Cox proportional-hazards regression model has achieved widespread use in the analysis of time-to-event data with censoring and covariates. The covariates may change their values over time. This article discusses the use of such time-dependent covariates, which offer additional opportunities but must be used with caution. The interrelationships between the outcome and variable over time can ...

A nonlinear regression model is proposed as an alternative to the Box-Cox regression model for nonnegative variables. The functional form contains as special cases the linear, exponential, constant elasticity, and generalized CES specifications, as well as other functional forms used by applied econometricians . The model can be derived from but is more general than a particular modification of...