Predictive assessment of the risk of developing cardiovascular diseases is usually provided by computational approaches centred on Cox models. The complex interdependence structure underlying clinical data patterns can limit the performance of Cox analysis and complicate the interpretation of results, thus calling for complementary and integrative methods. Prognostic models are proposed for stu...

We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criter...

Number of words: 2795 (excluding summary, references and tables) 2 SUMMARY This paper discusses techniques to generate survival times for simulation studies regarding Cox proportional hazards models. In linear regression models, the response variable is directly connected with the considered covariates, the regression coefficients and the simulated random errors. Thus, the response variable can...

There have been several attempts to extend the notion of explained variation to models for analyzing lifetime data, Cox models in particular . Most of the authors have concentrated on overcoming the difficulty of using the censored observations in the calculations, but did not pay enough attention to other, equally important issues . We present a list of properties that a measure of explained v...

In this paper, I model the intraday trading activity based on volume durations, i.e. the waiting time until a predetermined volume is absorbed by the market. Since this concept measures the trading volume per time it is strongly related to market liquidity. I focus on volumes measured independently of the side of the market as well as on buy volumes, sell volumes and volumes measured on both ma...

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...

In all sorts of regression problems, it has become more and more important to deal with high-dimensional data with lots of potentially influential covariates. A possible solution is to apply estimation methods that aim at the detection of the relevant effect structure by using penalization methods. In this article, the effect structure in the Cox frailty model, which is the most widely used mod...

Cyclooxygenase-2 (COX-2) has a pivotal role in the pathogenesis of the lung cancer. It is known that COX-2 negatively regulates the activity of a number of tumor suppressors, including p53. Consequently, inhibition of COX-2 signaling is anticipated to be a promising approach to stabilize p53 functionality. In this regard, we investigated the effect of COX-2 signaling blockade on p53 and COX-2 e...

OBJECTIVE The Cox model is the dominant tool in clinical trials to compare treatment options. This model does not specify any specific form to the hazard function. On the other hand, parametric models allow the researcher to consider an appropriate shape of hazard function for the event of interest. The aim of this article is to compare performance of Cox and parametric models. METHODS We use...

A group of regioisomeric 5-oxo-1,4,5,6,7,8 hexahydroquinoline derivatives possessing a COX-2 SO2Me pharmacophore at the para position of the C-2 or C-4 phenyl ring, in conjunction with a C-4 or C-2 phenyl (4-H) or substituted-phenyl ring (4-F,4-Cl,4-Br,4-OMe,4-Me, 4-NO2), were designed for evaluation as selective cyclooxygenase-2 (COX-2) inhibitors. These target 5-oxo-1,4,5,6,7,8 hexahydroquino...