A morphometric approach to the geographic variation of the terrestrial isopod species Armadillo tuberculatus (Isopoda: Oniscidea)

The terrestrial isopod species Armadillo tuberculatus Vogl, 1876 (Crustacea, Isopoda, Oniscidea) is a widely polymorphic species distributed in the south-central Aegean region (Greece) with a different morph on each island. Variation consists in coloration, size of cuticular tubercules, shape of telson and the shape of the male first pleopod exopodite (secondary sexual character of taxonomic importance). We studied the allometric growth of a cuticular tubercule in 17 populations (for both male and female individuals) and the shape variation of the first male pleopod exopodite in 10 populations using Elliptic Fourier Analysis, in order to test for patterns of intraspecific variation and possible relationships between morphs. In addition, Thin Plate Spline analysis was used for the calculation of the minimum bending energy between different exopodite shapes, which was then used for estimating the minimum spanning network (MSN) connecting them. The different allometric growth rates of the tubercule among island groups were significantly related to island latitude and climatic factors. On the other hand, the clustering of islands and the MSN based on male exopodite shape differences were not related to the palaeogeography of the Aegean region or to the present geographic distances of islands. These results are interpreted as evidence for non-adaptive radiation of the morphs.     

Time‐Dependent Predictive Accuracy in the Presence of Competing Risks

Summary Competing risks arise naturally in time‐to‐event studies. In this article, we propose time‐dependent accuracy measures for a marker when we have censored survival times and competing risks. Time‐dependent versions of sensitivity or true positive (TP) fraction naturally correspond to consideration of either cumulative (or prevalent) cases that accrue over a fixed time period, or alternatively to incident cases that are observed among event‐free subjects at any select time. Time‐dependent (dynamic) specificity (1–false positive (FP)) can be based on the marker distribution among event‐free subjects. We extend these definitions to incorporate cause of failure for competing risks outcomes. The proposed estimation for cause‐specific cumulative TP/dynamic FP is based on the nearest neighbor estimation of bivariate distribution function of the marker and the event time. On the other hand, incident TP/dynamic FP can be estimated using a possibly nonproportional hazards Cox model for the cause‐specific hazards and riskset reweighting of the marker distribution. The proposed methods extend the time‐dependent predictive accuracy measures of Heagerty, Lumley, and Pepe (2000, Biometrics 56, 337–344) and Heagerty and Zheng (2005, Biometrics 61, 92–105).     

Under-smoothed kernel confidence intervals for the hazard ratio based on censored data

In cancer clinical trials, it is often of interest in estimating the ratios of hazard rates at some specific time points during the study from two independent populations. In this paper, we consider nonparametric confidence interval procedures for the hazard ratio based on kernel estimates for the hazard rates with under-smoothing bandwidths. Two methods are used to derive the confidence intervals: one based on the asymptotic normality of the ratio of the kernel estimates for the hazard rates in two populations and another through Fieller's Theorem. The performances of the proposed confidence intervals are evaluated through Monte-Carlo simulations and applied to the analysis of data from a clinical trial on early breast cancer.     

Inference for the Break Point in Segmented Regression with Application to Longitudinal Data

This paper considers inference for the break point in the segmented regression or piece‐wise regression model. Standard likelihood theory does not apply because the break point is absent under the null hypothesis. We use results by Davies for this type of non‐standard set‐up [Biometrika 64 (1977), 247–254 and 74 (1987), 33–43] to obtain a test for the null hypothesis of no break point. A confidence interval can be constructed provided replicate data are available. The methods are exemplified using two longitudinal datasets, the one from ecology, the other from pharmacology.     

Classifying antibodies using flow cytometry data: class prediction and class discovery

Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative efforts of researchers from many countries have led to the classification of thousands of antibodies into 247 clusters of differentiation (CD). Classification is based on flow cytometry and biochemical data. In preliminary classifications of antibodies based on flow cytometry data, the object requiring classification (an antibody) is described by a set of random samples from unknown densities of fluorescence intensity. An individual sample is collected in the experiment, where a population of cells of a certain type is stained by the identical fluorescently marked replicates of the antibody of interest. Samples are collected for multiple cell types. The classification problems of interest include identifying new CDs (class discovery or unsupervised learning) and assigning new antibodies to the known CD clusters (class prediction or supervised learning). These problems have attracted limited attention from statisticians. We recommend a novel approach to the classification process in which a computer algorithm suggests to the analyst the subset of the "most appropriate" classifications of an antibody in class prediction problems or the "most similar" pairs/ groups of antibodies in class discovery problems. The suggested algorithm speeds up the analysis of a flow cytometry data by a factor 10-20. This allows the analyst to focus on the interpretation of the automatically suggested preliminary classification solutions and on planning the subsequent biochemical experiments.     

Spatial variance of nutrient limitation of periphyton in montane, headwater streams (McLeod River, Alberta, Canada)

There have been few studies that have examined the spatial variance of nutrient limitation over the scale of an entire set of headwater streams. We used nutrient diffusing substrata experiments (control, nitrogen addition, phosphorus addition, and nitrogen+phosphorus addition) to examine how nutrient limitation varied throughout the five creeks that comprise the McLeod River headwaters (Alberta, Canada). We assessed the variance of chlorophyll a accrual at spatial scales within reach, within creek, among creeks and across linear distance within the entire watershed to assess the consistency and scale of nutrient limitation. We analyzed the importance of the spatial scale using several methods. We assessed the coefficient of variation at different scales, the spatial covariance of nitrogen and phosphorus deficiency indices using a spline correlogram, and the variance through traditional analyses of variance methods. Chlorophyll a accrual responded significantly to nutrients in all creeks, though the response varied in magnitude and in the limiting nutrient among reaches and among creeks. Variance in chlorophyll a accrual was due primarily to the factor of creek (R ²=0.40) and secondarily to reach (R ²=0.07). The CV was 31.4% among creeks, 18.4% among reaches, and 17.9% within reaches. The N deficiency index showed a positive correlation at sites located <4 km apart and a negative correlation at sites greater than 6.5 km apart. The P deficiency index showed no discernible spatial correlation. Our results suggest that nutrient limitation varies on small scales and is often driven by local processes.     

Low-rank scale-invariant tensor product smooths for generalized additive mixed models

A general method for constructing low-rank tensor product smooths for use as components of generalized additive models or generalized additive mixed models is presented. A penalized regression approach is adopted in which tensor product smooths of several variables are constructed from smooths of each variable separately, these "marginal" smooths being represented using a low-rank basis with an associated quadratic wiggliness penalty. The smooths offer several advantages: (i) they have one wiggliness penalty per covariate and are hence invariant to linear rescaling of covariates, making them useful when there is no "natural" way to scale covariates relative to each other; (ii) they have a useful tuneable range of smoothness, unlike single-penalty tensor product smooths that are scale invariant; (iii) the relatively low rank of the smooths means that they are computationally efficient; (iv) the penalties on the smooths are easily interpretable in terms of function shape; (v) the smooths can be generated completely automatically from any marginal smoothing bases and associated quadratic penalties, giving the modeler considerable flexibility to choose the basis penalty combination most appropriate to each modeling task; and (vi) the smooths can easily be written as components of a standard linear or generalized linear mixed model, allowing them to be used as components of the rich family of such models implemented in standard software, and to take advantage of the efficient and stable computational methods that have been developed for such models. A small simulation study shows that the methods can compare favorably with recently developed smoothing spline ANOVA methods.     

Nonparametric regression using local kernel estimating equations for correlated failure time data

We study nonparametric regression for correlated failure timedata. Kernel estimating equations are used to estimate nonparametriccovariate effects. Independent and weighted-kernel estimatingequations are studied. The derivative of the nonparametric functionis first estimated and the nonparametric function is then estimatedby integrating the derivative estimator. We show that the nonparametrickernel estimator is consistent for any arbitrary working correlationmatrix and that its asymptotic variance is minimized by assumingworking independence. We evaluate the performance of the proposedkernel estimator using simulation studies, and apply the proposedmethod to the western Kenya parasitaemia data.     

一个适于最大似然法估计物种分化年代的进化速率平滑近似方法

众所周知 ,物种分化年代的估计对分子钟 (进化速率恒定 )假定很敏感。另一方面 ,在远缘物种 (例如哺乳纲不同目的动物 )的比较中 ,分子钟几乎总是不成立的。这样在估计分化时间时考虑不同进化区系的速率差异至为重要。最大似然法可以很自然地考虑这种速率差异 ,并且可以同时分析多个基因位点的资料以及同时利用多重化石校正数据。以前提出的似然法需要研究者将进化树的树枝按速率分组 ,本文提出一个近似方法以使这个过程自动化。本方法综合了以前的似然法、贝斯法及近似速率平滑法的一些特征。此外 ,还对算法加以改进 ,以适应综合数据分析时某些基因在某些物种中缺乏资料的情形。应用新提出的方法来分析马达加斯加的倭狐猴的分化年代 ,并与以前的似然法及贝斯法的分析进行了比较