Edsel A. Peña

Resveratrol suppresses colitis and colon cancer associated with colitis

Resveratrol is a naturally occurring polyphenol that exhibits pleiotropic health beneficial effects, including anti-inflammatory, cardio-protective, and cancer-protective activities. It is recognized as one of the more promising natural molecules in the prevention and treatment of chronic inflammatory and autoimmune disorders. Ulcerative colitis is an idiopathic, chronic inflammatory disease of the colon associated with a high colon cancer risk. Here, we used a dextran sulfate sodium (DSS) mouse model of colitis, which resembles human ulcerative colitis pathology. Resveratrol mixed in food ameliorates DSS-induced colitis in mice in a dose-dependent manner. Resveratrol significantly improves inflammation score, downregulates the percentage of neutrophils in the mesenteric lymph nodes and lamina propria, and modulates CD3+ T cells that express tumor necrosis factor-α and IFN-γ. Markers of inflammation and inflammatory stress (p53 and p53-phospho-Ser15) are also downregulated by resveratrol. Because chronic colitis drives colon cancer risk, we carried out experiments to determine the chemopreventive properties of resveratrol. Tumor incidence is reduced from 80% in mice treated with azoxymethane (AOM) + DSS to 20% in mice treated with AOM + DSS + resveratrol (300 ppm). Tumor multiplicity also decreased with resveratrol treatment. AOM + DSS–treated mice had 2.4 ± 0.7 tumors per animal compared with AOM + DSS + 300 ppm resveratrol, which had 0.2 ± 0.13 tumors per animal. The current study indicates that resveratrol is a useful, nontoxic complementary and alternative strategy to abate colitis and potentially colon cancer associated with colitis. Cancer Prev Res; 3(4); 549–59. ©2010 AACR.

Nonparametric Estimation With Recurrent Event Data

The problem of nonparametric estimation for the distribution function governing the time to occurrence of a recurrent event in the presence of censoring is considered. We derive Nelson–Aalen and Kaplan–Meier-type estimators for the distribution function, and establish their respective finite-sample and asymptotic properties. We allow for random observation periods for each subject under study and explicitly account for the informative sum-quota nature of the data accrual scheme. These allowances complicate technical matters considerably and, in particular, invalidate the direct use of martingale methods. Consistency and weak convergence of our estimators are obtained by extending an approach due to Sellke, who considered a single renewal process (i.e., recurrent events on a single subject) observed over an infinite time period. A useful feature of the present analysis is that strong parallels are drawn to the usual “single-event” setting, providing a natural route toward developing extensions that involve covariates (e.g., weighted log-rank tests, Cox-type regression, and frailty models). Another advantage is that we obtain explicit, closed-form expressions for the asymptotic variances for these estimators. This enables, for instance, the characterization of the efficiency loss that results from employing only the first, possibly right-censored, observation per subject. An interesting feature of these results is the prominent role of the renewal function. Finally, we discuss the case of correlated interoccurrence times, propose an estimator in the case where the within-unit interoccurrence times follow a gamma frailty model, and compare the performance of our estimators to an estimator recently proposed by Wang and Chang.

Global Validation of Linear Model Assumptions

An easy-to-implement global procedure for testing the four assumptions of the linear model is proposed. The test can be viewed as a Neyman smooth test and relies only on the standardized residual vector. If the global procedure indicates a violation of at least one of the assumptions, then the components of the global test statistic can be used to gain insight into which assumptions have been violated. The procedure can also be used in conjunction with associated deletion statistics to detect unusual observations. Simulation results are presented indicating the sensitivity of the procedure in detecting model violations under a variety of situations, and its performance is compared with three potential competitors, including a procedure based on the Box–Cox power transformation. The procedure is demonstrated by applying it to a new car mileage dataset and a water salinity dataset that has been used earlier to illustrate model diagnostics.

Estimating Load-Sharing Properties in a Dynamic Reliability System

An estimator for the load-share parameters in an equal load-share model is derived based on observing k-component parallel systems of identical components that have a continuous distribution function F (˙) and failure rate r (˙). In an equal load-share model, after the first of k components fails, failure rates for the remaining components change from r (t) to γ1r (t), then to γ2r (t) after the next failure, and so on. On the basis of observations on n independent and identical systems, a semiparametric estimator of the component baseline cumulative hazard function R = –log(1 – F) is presented, and its asymptotic limit process is established to be a Gaussian process. The effect of estimation of the load-share parameters is considered in the derivation of the limiting process. Potential applications can be found in diverse areas, including materials testing, software reliability, and power plant safety assessment.

American ginseng suppresses inflammation and DNA damage associated with mouse colitis

Ulcerative colitis (UC) is a dynamic, idiopathic, chronic inflammatory condition associated with a high colon cancer risk. American ginseng has antioxidant properties and targets many of the players in inflammation. The aim of this study was to test whether American ginseng extract prevents and treats colitis. Colitis in mice was induced by the presence of 1% dextran sulfate sodium (DSS) in the drinking water or by 1% oxazolone rectally. American ginseng extract was mixed in the chow at levels consistent with that currently consumed by humans as a supplement (75 p.p.m., equivalent to 58 mg daily). To test prevention of colitis, American ginseng extract was given prior to colitis induction. To test treatment of colitis, American ginseng extract was given after the onset of colitis. In vitro studies were performed to examine mechanisms. Results indicate that American ginseng extract not only prevents but it also treats colitis. Inducible nitric oxide synthase and cyclooxygenase-2 (markers of inflammation) and p53 (induced by inflammatory stress) are also downregulated by American ginseng. Mucosal and DNA damage associated with colitis is at least in part a result of an oxidative burst from overactive leukocytes. We therefore tested the hypothesis that American ginseng extract can inhibit leukocyte activation and subsequent epithelial cell DNA damage in vitro and in vivo. Results are consistent with this hypothesis. The use of American ginseng extract represents a novel therapeutic approach for the prevention and treatment of UC.

Models for Recurrent Events in Reliability and Survival Analysis

Existing models forrecurrent phenomena occurring in public health, biomedicine, reliability, engineering, economics, and sociology are reviewed. A new and general class of models for recurrent events is proposed. This class simultaneously takes into account intervention effects, effects of accumulating event occurrences, and effects of concomitant variables. It subsumes as special cases existing models for recurrent phenomena. The statistical identifiability issue for the proposed class of models is addressed.

Dynamic reliability models with conditional proportional hazards

A dynamic approach to the stochastic modelling of reliability systems is further explored. This modelling approach is particularly appropriate for load-sharing, software reliability, and multivariate failure-time models, where component failure characteristics are affected by their degree of use, amount of load, or extent of stresses experienced. This approach incorporates the intuitive notion that when a set of components in a coherent system fail at a certain time, there is a ‘jump’ from one structure function to another which governs the residual lifetimes of the remaining functioning components, and since the component lifetimes are intrinsically affected by the structure function which they constitute, then at such a failure time there should also be a jump in the stochastic structure of the lifetimes of the remaining components. For such dynamically-modelled systems, the stochastic characteristics of their jump times are studied. These properties of the jump times allow us to obtain the properties of the lifetime of the system. In particular, for a Markov dynamic model, specific expressions for the exact distribution functions of the jump times are obtained for a general coherent system, a parallel system, and a series-parallel system. We derive a new family of distribution functions which describes the distributions of the jump times for a dynamically-modelled system.

Dynamic modeling and statistical analysis of event times

This review article provides an overview of recent work in the modelling and analysis of recurrent events arising in engineering, reliability, public health, biomedical, and other areas. Recurrent event modelling possesses unique facets making it different and more difficult to handle than single event settings. For instance, the impact of an increasing number of event occurrences needs to be taken into account, the effects of covariates should be considered, potential association among the inter-event times within a unit cannot be ignored, and the effects of performed interventions after each event occurrence need to be factored in. A recent general class of models for recurrent events which simultaneously accommodates these aspects is described. Statistical inference methods for this class of models are presented and illustrated through applications to real data sets. Some existing open research problems are described.

A Chi-Squared Goodness-of-Fit Test for Randomly Censored Data

Abstract In this article, procedures analogous to Karl Pearsons well-known chi-squared goodness-of-fit test for a simple null hypothesis are developed under the random censorship model. It is shown that one straightforward analog of Pearsons statistic is diminished in applicability due to the form of its limiting distribution. This leads to the development of an asymptotically exact test based on a Wald-type statistic with a chi-squared limiting null distribution. This test is compared and contrasted theoretically and via a simulation with Akritas’ test with respect to significance levels, asymptotic local powers, and finite sample powers. The general conclusions from the simulation study are that the proposed test usually achieves the desired significance levels when the probability of observing a censored or an uncensored value in the last interval is not small, whereas Akritas’ test tends to be a bit anticonservative. On the other hand, Akritas’ test is more powerful than the proposed test in a model...

A General Class of Parametric Models for Recurrent Event Data

The general class of models proposed by Peña and Hollander for recurrent event data is considered under a fully parametric specification of the baseline hazard rate function and under the two cases where the model does and does not incorporate frailty components. Estimators of model parameters are presented, and their finite and asymptotic properties are ascertained. For the asymptotic properties, the results of Borgan concerning maximum likelihood estimators in counting process models are used to obtain weak convergence to Gaussian distributions of estimators. However, the required regularity conditions are reformulated into conditions involving gap times, which make it more feasible to obtain explicit theoretical expressions of asymptotic covariances. The procedures are applied to fit the general class of models with a parametric baseline hazard rate function to a dataset on hydraulic subsystems of “load-haul-dump” machines in mining.