Long-term effects with policosanol on lipid profile according to hypercholesterolemia severity in older patients

Background: End-point based studies have demonstrated a direct relationship between coronary disease and elevated serum levels of low density lipoprotein cholesterol (LDL-C) and total cholesterol, as well as the benefits of lowering LDL-C with statins on clinical end-points. Policosanol is a mixture of very long chain fatty alcohols purified from sugar cane wax, with dislipidemia controlling effects, proved in numerous clinical assays in which patients with different conditions were included. The efficacy and tolerability of policosanol in the elderly have been also investigated in several clinical trials, being effective, safe and well tolerated. Objectives: To investigate the effects of policosanol treatment during three years on lipid profile with a proportional intensity to the initial dislipidemia severity in older hypercholesterolemic patients. Methods: The present analysis was obtained from the data of all patients treated with policosanol included in a previous prevention study. One thousand, ford hundred seventy old patients of both sexes, between 60 to 85 years old, with type II hypercholesterolemia, and ³ 1 non-lipid coronary risk factors, were randomized in two groups and treated with policosanol or placebo, during three years. Significant changes on lipid profile with a proportional intensity to the initial dislipidemia severity were considered primary efficacy variables. The analysis was done by Intention-to-treat method. Results: An analysis of the response intensity show that after treatment, reductions of LDL-C, total cholesterol and triglycerides were greater, and according to the initial hypercholesterolemia severity, so that, patients with severe hypercholesterolemia showed the better responses, followed by moderate and mild hypercholesterolemia. An opposite pattern, however, was observed for HDL-C serum concentration. Triglycerides did not respond in the same way. The frequency of vascular serious adverse events was lower in the policosanol group (15 events) compared with those on placebo group (49 events). There were 109 patients who experienced serious adverse events: 83 (11.3 %) in placebo and 26 (3.5 %) in policosanol group (p<0.0001). Twenty-three deaths occurred up to study completion: 19 patients belonging to placebo group (2.6 %) and 4 to the policosanol group (0.5 %). Conclusions: The treatment with policosanol produce positive changes on serum lipid profile according to hypercholesterolemia severity and with a significant lower amount of vascular serious adverse events, mortality, and frequency of total adverse events in older patients.

Assessment of the effectiveness of corrective maintenance of an oil pump using the proportional intensity model (PIM)

PurposeComplex repairable systems (CRSs) are generally modeled by stochastic processes called “point processes.” These are generally summed up in the nonhomogeneous Poisson process (NHPP) and the renewal process (RP), which represent the minimum and maximum repair, respectively. However, the industrial environment affects systems in some way. This is why the main objective of this work is to model the CRS with a concept reflecting the real state of the system by incorporating an indicator in the form of covariate. This type of model, known as the proportional intensity model (PIM), will be analyzed with simulated failure data to understand the behavior of the failure process, and then it will be tested for real data from a petroleum company to evaluate the effectiveness of corrective actions carried out.Design/methodology/approachTo solve the partial repair modeling problem, the PIM was used by introducing, on the basis of the NHPP model, a multiplicative scaling factor, which reflects the degree of efficiency after each maintenance action. Several values of this multiplicative factor will be considered to generate data. Then, based on the reliability and maintenance history of 12-year pump's operation obtained from the SONATRACH Company (south industrial center (CIS), Hassi Messaoud, Algeria), the performance of the PIM will be judged and compared with the model of NHPP and RP in order to demonstrate its flexibility in modeling CRS. Using the maximum likelihood approach and relying on the Matlab software, the best fitting model should have the largest likelihood value.FindingsThe use of the PIM allows a better understanding of the physical situation of the system by allowing easy modeling to apply in practice. This is expressed by the value which, in this case, represents an improvement in the behavior of the system provided by a good quality of the corrective maintenance performed. This result is based on the hypothesis that modeling with the PIM can provide more clarification on the behavior of the system. It can indicate the effectiveness of the maintenance crew and guide managers to confirm or revise their maintenance policy.Originality/valueThe work intends to reflect the real situation in which the system operates. The originality of the work is to allow the consideration of covariates influencing the behavior of the system during its lifetime. The authors focused on modeling the degree of repair after each corrective maintenance performed on an oil pump. Since PIM does not require a specific reliability distribution to apply it, it allows a wide range of applications in the various industrial environments. Given the importance of this study, the PIM can be generalized for more covariates and working conditions.

Bayesian Joint Models for Longitudinal and Multi-state Survival Data

Joint models for longitudinal and time to event data are frequently used in many observational studies such as clinical trials with the aim of investigating how biomarkers which are recorded repeatedly in time are associated with time to an event of interest. In most cases, these joint models only consider a univariate time to event process. However, many clinical trials of patients with cancer, involve multiple recurrences of a single event together with a single terminal event experienced by patients over time. Therefore, this article proposes joint modelling approachs for longitudinal and multi-state data. The approach considers two sub-models that are linked by a common latent random variable. The first sub-model is linear mixed effect model that defines the longitudinal process and the second sub-model is a proportional intensity function for the multi-state process. Furthermore, on the proportional intensity model, two different formulations are used to define dependence structure between longitudinal and multi-state processes. In this article, a semi-Markov process that consider the time spent in the current state is defined for the transitions between states. Moreover, the time spent in each transient state is assumed to have Gompertz distribution. A Bayesian method using Markov Chain Monte Carlo (MCMC) is developed for parameter estimation and inferences. The deviance information criterion (DIC) is also derived for Bayesian model selection and comparison. Finally, our proposed joint modeling approach is evaluated through a simulation study and is applied to real datasets (colorectal and colorectal.Longi) which present a random selection of 150 patients from a multi-center randomized phase III clinical trial FFCD 2000-05 of patients diagnosed with metastatic colorectal cancer.

A Composite Model via Proportional Intensity Function and Additive Hazard Function

This paper mainly discusses a composite model which the end time is an additive hazard function and the recurrent event process is a proportional intensity function, the covariate is time-independent, and censoring is dependent on recurrent events process and end times. Based on the likelihood method, Delta method, U-statistic method and the idea of general estimation equation, the estimation of unknown parameters and unknown functions in this composite model is proposed.