Asymptotics for non-parametric likelihood estimation with doubly censored multivariate failure times

AbstractThis paper considers non-parametric estimation of a multivariate failure time distribution function when only doubly censored data are available, which occurs in many situations such as epidemiological studies. In these situations, each of multivariate failure times of interest is defined as the elapsed time between an initial event and a subsequent event and the observations on both events can suffer censoring. As a consequence, the estimation of multivariate distribution is much more complicated than that for multivariate right- or interval-censored failure time data both theoretically and practically. For the problem, although several procedures have been proposed, they are only ad-hoc approaches as the asymptotic properties of the resulting estimates are basically unknown. We investigate both the consistency and the convergence rate of a commonly used non-parametric estimate and show that as the dimension of multivariate failure time increases or the number of censoring intervals of multivariate failure time decreases, the convergence rate for non-parametric estimate decreases, and is slower than that with multivariate singly right-censored or interval-censored data

Principal component tests: applied to temporal gene expression data

Clustering analysis is a common statistical tool for knowledge discovery. It is mainly conducted when a project still is in the exploratory phase without any priori hypotheses. However, the statistical significance testing between the clusters can be meaningful in helping the researchers to assess if the classification results from implementing a clustering algorithm need to be improved, even after the cluster number has been determined by a well-established criterion. This is important when we want to identify highly-specific patterns through classification. We proposed to use a principal component (PC) test, which is an implementation of an exact F statistic for the measures at multiple endpoints based on elliptical distribution theory, to assess the statistical significance between clusters. A challenge in the implementation is the choice of the number (q) of principal components to be considered, which can severely influence the statistical power of the method. We optimized the determination via validation according to a permutation test based on the clustering to be evaluated. The method was applied to a public dataset in classifying genes according to their temporal gene expression profiles. The results demonstrated that the PC testing were useful for determining the optimal number of clusters.https://doi.org/10.1186/1471-2105-10-S1-S2

Characterizing Gene Expressions Based on Their Temporal Observations

Temporal gene expression data are of particular interest to researchers as they contain rich information in characterization of gene function and have been widely used in biomedical studies. However, extracting information and identifying efficient treatment effects without loss of temporal information are still in problem. In this paper, we propose a method of classifying temporal gene expression curves in which individual expression trajectory is modeled as longitudinal data with changeable variance and covariance structure. The method, mainly based on generalized mixed model, is illustrated by a dense temporal gene expression data in bacteria. We aimed at evaluating gene effects and treatments. The power and time points of measurements are also characterized via the longitudinal mixed model. The results indicated that the proposed methodology is promising for the analysis of temporal gene expression data, and that it could be generally applicable to other high-throughput temporal gene expression analyses

Distribution patterns of small-molecule ligands in the protein universe and implications for origin of life and drug discovery

Ligand-protein mapping was found to follow a power law and the preferential attachment principle, leading to the identification of the molecules, mostly nucleotide-containing compounds, that are likely to have evolved earliest

ROC Analysis for Phase II Group Sequential Basket Clinical Trial

The basket trial is a recent development in the clinical trial practice. It conducts the test of the same treatment on several different related diseases in a single trial, and has the advantage of reduced cost and enhanced efficiency. A natural question is how to assess the performance of the group sequential basket trial against the classical group sequential trial? To our knowledge, a formal assessment hasn't been seen in the literature, and is the goal of this study. Specifically, we use the receiver operating characteristic curve to assess the performance of the mentioned two trials. We considered two cases, parametric and nonparametric settings. The former is efficient when the parametric model is correctly specified, but can bemis-leading if the model is incorrect; the latter is less efficient but is robust in that it cannot be wrong no matter what the true data generating model is. Simulation studies are conducted to evaluate the experiments, and it suggests that the group sequential basket trial generally outperforms the group sequential trial in either the parametric and nonparametric cases, and that the nonparametric method gives more accurate evaluation than the parametric one for moderate to large sample sizes

Effects of esculentoside A on turnour necrosis factor production by mice peritoneal macrophages

Esculentoside A (EsA) is a saponin isolated from the roots of Phytolacca esculenta. Previous experiments showed that it had strong anti-inflammatory effects. Tumour necrosis factor (TNF) is an important inflammatory mediator. In order to study the mechanism of the anti-inflammatory effect of EsA, it was determined whether TNF production from macrophages was altered by EsA under lipopolysaccharide (LPS) stimulated conditions. EsA was found to decrease both extracellular and cell associated TNF production in a dose dependent manner at concentrations higher than 1 μmol/l EsA. Previous studies have showed that EsA reduced the releasing of platelet activating factor (PAF) from rat macrophages. The reducing effects of EsA on the release of TNF and PAF may explain its anti-inflammatory effect