Survival analysis with time-varying regression effects using a tree-based approach

Nonproportional hazards often arise in survival analysis, as is evident in the data from the International Non-Hodgkin's Lymphoma Prognostic Factors Project. A tree-based method to handle such survival data is developed for the assessment and estimation of time-dependent regression effects under a Cox-type model. The tree method approximates the time-varying regression effects as piecewise constants and is designed to estimate change points in the regression parameters. A fast algorithm that relies on maximized score statistics is used in recursive segmentation of the time axis. Following the segmentation, a pruning algorithm with optimal properties similar to those of classification and regression trees (CART) is used to determine a sparse segmentation. Bootstrap resampling is used in correcting for overoptimism due to split point optimization. The piecewise constant model is often more suitable for clinical interpretation of the regression parameters than the more flexible spline models. The utility of the algorithm is shown on the lymphoma data, where we further develop the published International Risk Index into a time-varying risk index for non-Hodgkin's lymphoma.     

Bayesian nonparametric estimation of continuous monotone functions with applications to dose-response analysis

Summary .  In this article, we consider monotone nonparametric regression in a Bayesian framework. The monotone function is modeled as a mixture of shifted and scaled parametric probability distribution functions, and a general random probability measure is assumed as the prior for the mixing distribution. We investigate the choice of the underlying parametric distribution function and find that the two-sided power distribution function is well suited both from a computational and mathematical point of view. The model is motivated by traditional nonlinear models for dose–response analysis, and provides possibilities to elicitate informative prior distributions on different aspects of the curve. The method is compared with other recent approaches to monotone nonparametric regression in a simulation study and is illustrated on a data set from dose–response analysis.     

Studies on the pathogenesis of ischemic cell injury. XI. P/O ratio and acceptor control.

Acceptor control index, P/O ratio and inner membrane permeability were examined in isolated mitochondria following periods of renal ischemia for 15, 30, 60, 120, and 240 min. It was noted that the P/O ratio remained unchanged until 1-2 h after the onset of ischemia. A similar change was noted in the contraction rate of isolated ischemic mitochondria after swelling in KCl and addition of ATP+Mg2+. Both changes are probably indications of a basic membrane alteration which correlates with the occurrence of irreversibility of cell injury. In contrast, the swelling rate in KCl and the acceptor control index are altered almost simultaneously with the onset of ischemia. Therefore, acceptor control index and the rate of swelling are affected prior to the point of irreversible cell injury. They are not, therefore, good as indicators of irreversible changes in the inner membrane of mitochondria leading to the "point-of-no-return."     

南泥湾片段森林蝗虫群落多样性比较

应用多样性指数、排序和多元逐步回归分析方法比较了南泥湾片断化森林的蝗虫群落结构,并进行了环境因素的解释。结果显示：在南泥湾,树木破坏不严重的片断化森林,随森林面积的减小,林缘草层蝗虫种类、多样性指数和均匀度指数差异不大;林中草层蝗虫密度、多样性指数和均匀度指数减小。在面积小、树木破坏严重、植被结构发生明显改变的片断森林林中草层,相对于树木破坏不严重的片断化森林,蝗虫的密度、多样性指数和均匀度指数明显增大;而林缘草层蝗虫的密度和群落优势度指数上升,多样性指数和均匀度指数下降。以主分量分析方法可明显将林中草层蝗虫群落分为森林破坏严重和不严重两种类型。通过多元逐步回归分析发现,影响片断化森林蝗虫群落结构和多样性的主要因素有片断森林面积、森林植被结构的复杂性、食料植物的多少等几个方面。     

Detecting change in an Australian flowering record: Comparisons of linear regression and cumulative sum analysis change point analysis

Linear regression and cumulative sum analysis (CUSUM) change point analyses were used to determine whether there had been a significant change in the first flowering date between 1983 and 2006 for 65 species. Both methods agreed that the first flowering date of 47 species did not change and that eight species had a significant change (P < 0.05) in their flowering. Three species shifted to later flowering and five species to earlier. Over the observation period, each method found that the average shift to later flowering was greater (37.4 days or approx. 1.56 days per year for CUSUM change point analysis and 51.4 days or 2.14 days per year for linear regression) than that to earlier flowering (28.4 days or approx. 1.20 days per year for change point analysis and 46.5 days or 1.97 days per year for linear regression). For the remaining 10 species the results of linear regression and change point methods differed. Each method found five species (three earlier flowering and two later) to have a significantly changed first flowering date over their observation period, where the other method did not. Some of these differences can be attributed to the fact that the CUSUM method can detect multiple change points whereas linear regression can not. Significant change points in first flowering date were identified for 13 species between the years 1987 to 1998. The most frequent year identified as a change point year was 1995. The two methods, although not interchangeable, had strong agreement (84.6%) in detecting shifts. This gives greater confidence that a change in flowering has occurred for eight species and equally importantly, that no change in first flowering date has occurred for 47 species.     

Change point estimation in monitoring survival time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.     

Putting background information about relative risks into conjugate prior distributions

In Bayesian and empirical Bayes analyses of epidemiologic data, the most easily implemented prior specifications use a multivariate normal distribution for the log relative risks or a conjugate distribution for the discrete response vector. This article describes problems in translating background information about relative risks into conjugate priors and a solution. Traditionally, conjugate priors have been specified through flattening constants, an approach that leads to conflicts with the true prior covariance structure for the log relative risks. One can, however, derive a conjugate prior consistent with that structure by using a data-augmentation approximation to the true log relative-risk prior, although a rescaling step is needed to ensure the accuracy of the approximation. These points are illustrated with a logistic regression analysis of neonatal-death risk.     

A probabilistic network for the diagnosis of acute cardiopulmonary diseases

In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented in detail. A panel of expert physicians collaborated to specify the qualitative part, which is a directed acyclic graph defining a factorization of the joint probability distribution of domain variables into univariate conditional distributions. The quantitative part, which is a set of parametric models defining these univariate conditional distributions, was estimated following the Bayesian paradigm. In particular, we exploited an original reparameterization of Beta and categorical logistic regression models to elicit the joint prior distribution of parameters from medical experts, and updated it by conditioning on a dataset of hospital records via Markov chain Monte Carlo simulation. Refinement was iteratively performed until the probabilistic network provided satisfactory concordance index values for several acute diseases and reasonable diagnosis for six fictitious patient cases. The probabilistic network can be employed to perform medical diagnosis on a total of 63 diseases (38 acute and 25 chronic) on the basis of up to 167 patient findings.     

Poisson Regression with Change‐Point Prior in the Modelling of Disease Risk around a Point Source

Bayesian estimation of the risk of a disease around a known point source of exposure is considered. The minimal requirements for data are that cases and populations at risk are known for a fixed set of concentric annuli around the point source, and each annulus has a uniquely defined distance from the source. The conventional Poisson likelihood is assumed for the counts of disease cases in each annular zone with zone‐specific relative risk and parameters and, conditional on the risks, the counts are considered to be independent. The prior for the relative risk parameters is assumed to be piecewise constant at the distance having a known number of components. This prior is the well‐known change‐point model. Monte Carlo sampling from the posterior results in zone‐specific posterior summaries, which can be applied for the calculation of a smooth curve describing the variation in disease risk as a function of the distance from the putative source. In addition, the posterior can be used in the calculation of posterior probabilities for interesting hypothesis. The suggested model is suitable for use in geographical information systems (GIS) aimed for monitoring disease risks. As an application, a case study on the incidence of lung cancer around a former asbestos mine in eastern Finland is presented. Further extensions of the model are discussed.     

新疆猪毛菜属植物多样性地理分布格局及其环境解释

猪毛菜属(Salsola)是新疆干旱区分布最为丰富的被子植物属之一,是盐碱和荒漠区的先锋种和建群种,对西北干旱区植被恢复与建设具有巨大生态价值。基于新疆自然分布的33种猪毛菜属物种共741个分布数据,整合利用点格局法和物种分布模型法构建了物种丰富度(SR)、加权特有性指数(WE)和校正加权特有性指数(CWE)的分布格局。选取环境能量、水分可获得性、气候季节性、生境异质性、土壤条件和历史气候变化共6类19种生态因子,利用地理加权回归模型(GWR)探究了环境异质性对猪毛菜属物种丰富度的影响。结果显示：(1)基于现实点位模型和物种分布模型构建的物种丰富度具有一致性,均呈北高南低、西高东低的破碎化分布趋势,但物种分布模型的结果在空间上比点格局法更连续,物种丰富度的高值区主要分布于准噶尔盆地南缘、准噶尔西部山地、天山西端和天山南脉南缘;(2)加权特有性指数和校正加权特有性指数的分布格局与物种丰富度分布格局具有一定差异,其最大值集中分布于准噶尔盆地南缘、伊犁河谷和塔里木盆地西南缘;(3)GWR模型结果表明,海拔变幅、土壤酸碱度和最干月降水量是制约新疆分布的猪毛菜属丰富度和特有性分布的最重要因素。     

三峡库区低山丘陵区多尺度景观指数响应及适宜粒度

景观格局具有典型的空间异质性和尺度依赖性。在对景观格局进行分析研究时需跨越多个尺度,空间粒度大小在尺度聚合分析中至关重要。三峡库区低山丘陵区斑块破碎、景观格复杂性显著、景观格局特征及尺度变异规律仍待明确,本文以三峡库区秭归县为研究区,设定1—400m内23个粒度梯度水平,定量评估县域、乡镇以及小流域不同幅度上的景观格局指数以及拟合函数,探讨多空间幅度景观格局指数随粒度大小的变化特征。基于景观指数的粒度效应特征和拟合函数的曲线特征(最大曲率点、极值点),明确适宜不同幅度的山地景观格局指数研究的粒度阈值,以揭示库区低山丘陵区景观结构的复杂性和变异性。结果表明：不同景观指数对空间粒度变化和空间幅度变化的响应存在差异,不同景观指数的空间粒度响应主要呈现增加、降低、波动和无明显规律变化的趋势,其中斑块密度、最大斑块面积等指数对斑块形状和大小的变化敏感,而多样性指数的粒度变化敏感度较低;部分指数如斑块密度、边缘密度、周长面积分维数等对空间幅度的变化并不敏感,而最大斑块面积、景观形状指数、散布与并列指数、分离度等指数对空间幅度的变化敏感,适合进行不同幅度适宜粒度阈值的推定;边缘密度、平均斑块大小、景...     

THE RELATIONSHIP BETWEEN CELL MATURATION and PROLIFERATION IN the ERYTHROID SYSTEM of the RAT

Calculations have been made of the change with time in the tritiated thymidine labelling index of erythroid cells in normal and anaemic rats, on the basis of two different models of the erythroid system. In the first model it is assumed that cells pass from one stage of maturation to the next at all phases of the cell cycle, whereas in the second model the cells can only progress to the next stage when they reach a certain point in the cell cycle. the changes in labelling index predicted on the basis of these two models are markedly different, especially in the non-dividing stages of the system, and the change in labelling index as a function of time therefore provides an experimental method for distinguishing between the two models. the experimental data favours the model in which cells cross compartmental boundaries at all stages of maturation. Some important consequences of this model are discussed.     

Centriole behavior in early mitosis of rat kangaroo cells (PTK2)

Behavior of the centriolar duplexes during early mitosis was investigated. In general, both duplexes are capable of migrating about the cell as a unit with little change in their center to center spacing prior to separation to form the spindle poles. This duplex separation may occur at any point within the mid-prophase-prometaphase period. If it is delayed to prometaphase, transitory monopolar spindles were observed.     

An Examination of the Refractometric Method for Determining the Water Potential of Plant Tissues

Some sources of error in refractometric determinations of waterpotential are examined and methods of correcting for them oravoiding them are discussed, as well as their relevance to Shardakov’s(Schlieren) method. The sources of the exudation from cut tissues,which causes a rise in the refractive index of water and affectsthat of solutions, are investigated. The exudation consistsmainly of sap released from cut and damaged cells. The amountof sap exuded is independent of the concentration of the externalsolution. A method of estimating the extent of the error thusincurred in water potential determinations is described. Errorarising also from the admixture of cell-wall water to the referencesolutions, lowering their effective concentration, and othersources of error are briefly described. A method of eliminating the curvature in graphs of change ofrefractive index plotted against molarity is suggested. Themethod permits the data from all concentrations to be used indetermining the null point for water exchange, instead of thosenear to the null point only.     

Effects of residual smoothing on the posterior of the fixed effects in disease-mapping models

Disease-mapping models for areal data often have fixed effects to measure the effect of spatially varying covariates and random effects with a conditionally autoregressive (CAR) prior to account for spatial clustering. In such spatial regressions, the objective may be to estimate the fixed effects while accounting for the spatial correlation. But adding the CAR random effects can cause large changes in the posterior mean and variance of fixed effects compared to the nonspatial regression model. This article explores the impact of adding spatial random effects on fixed effect estimates and posterior variance. Diagnostics are proposed to measure posterior variance inflation from collinearity between the fixed effect covariates and the CAR random effects and to measure each region's influence on the change in the fixed effect's estimates by adding the CAR random effects. A new model that alleviates the collinearity between the fixed effect covariates and the CAR random effects is developed and extensions of these methods to point-referenced data models are discussed.     

Bayesian analysis for generalized linear models with nonignorably missing covariates

We propose Bayesian methods for estimating parameters in generalized linear models (GLMs) with nonignorably missing covariate data. We show that when improper uniform priors are used for the regression coefficients, phi, of the multinomial selection model for the missing data mechanism, the resulting joint posterior will always be improper if (i) all missing covariates are discrete and an intercept is included in the selection model for the missing data mechanism, or (ii) at least one of the covariates is continuous and unbounded. This impropriety will result regardless of whether proper or improper priors are specified for the regression parameters, beta, of the GLM or the parameters, alpha, of the covariate distribution. To overcome this problem, we propose a novel class of proper priors for the regression coefficients, phi, in the selection model for the missing data mechanism. These priors are robust and computationally attractive in the sense that inferences about beta are not sensitive to the choice of the hyperparameters of the prior for phi and they facilitate a Gibbs sampling scheme that leads to accelerated convergence. In addition, we extend the model assessment criterion of Chen, Dey, and Ibrahim (2004a, Biometrika 91, 45-63), called the weighted L measure, to GLMs and missing data problems as well as extend the deviance information criterion (DIC) of Spiegelhalter et al. (2002, Journal of the Royal Statistical Society B 64, 583-639) for assessing whether the missing data mechanism is ignorable or nonignorable. A novel Markov chain Monte Carlo sampling algorithm is also developed for carrying out posterior computation. Several simulations are given to investigate the performance of the proposed Bayesian criteria as well as the sensitivity of the prior specification. Real datasets from a melanoma cancer clinical trial and a liver cancer study are presented to further illustrate the proposed methods.     

Sexually dimorphic expression of secreted frizzled-related (SFRP) genes in the developing mouse Müllerian duct

In developing male embryos, the female reproductive tract primordia (Müllerian ducts) regress due to the production of testicular anti-Müllerian hormone (AMH). Because of the association between secreted frizzled-related proteins (SFRPs) and apoptosis, their reported developmental expression patterns and the role of WNT signaling in female reproductive tract development, we examined expression of Sfrp2 and Sfrp5 during development of the Müllerian duct in male (XY) and female (XX) mouse embryos. We show that expression of both Sfrp2 and Sfrp5 is dynamic and sexually dimorphic. In addition, the male-specific expression observed for both genes prior to the onset of regression is absent in mutant male embryos that fail to undergo Müllerian duct regression. We identified ENU-induced point mutations in Sfrp5 and Sfrp2 that are predicted to severely disrupt the function of these genes. Male embryos and adults homozygous for these mutations, both individually and in combination, are viable and apparently fertile with no overt abnormalities of reproductive tract development.     

Using change-point analysis and weighted averaging approaches to explore the relationships between common benthic diatoms and in-stream environmental variables in mid-atlantic highlands streams,USA

Diatoms are increasingly being used in the bioassessment of aquatic systems. However, autecological information for many common taxa is incomplete. We explored the potential of classification (CT) and regression tree (RT) approaches to identify the hierarchical interaction among water quality variables in predicting the relative abundance of ten common stream diatom taxa in the Mid-Atlantic Highlands ecoregion. RT analysis was also used to identify environmental change points corresponding to major shifts in species abundances. We also used traditional weighted-averaging approaches (WA) to model taxon pH and total phosphorus (TP) optima. RT and WA approaches provided different, yet complementary, information on the complex relationships between common stream diatoms and environmental variables. Both RT and CT highlighted the interaction of stream acidity (pH, acid neutralizing capacity (ANC)), and TP in structuring the stream diatom assemblage. For the RT of taxa, where pH was an important predictor, higher pH predicted higher relative abundances. In contrast, higher TP predicted lower relative abundances for some diatom taxa (e.g., Achnanthidium minutissimum (Kütz.) Czarnecki), while predicting higher relative abundances for other taxa (e.g., Planothidium lanceolatum (Bréb.) Round & Bukht., Gomphonema parvulum (Kütz.) Kütz.). The environmental change point for pH derived from RT analysis was lower than WA optima for all species. We suggest that RT change point analysis can be used to complement traditional WA optima approaches, especially when diatom taxa’s abundances are affected by interactive environmental factors, to provide more refined information on stream diatom environmental preferences. Handling editor: L. Naselli-Flores     

Comparative Analysis of Mourning Dove Population Change in North America

ABSTRACT Mourning doves (Zenaida macroura) are surveyed in North America with a Call-Count Survey (CCS) and the North American Breeding Bird Survey (BBS). Analyses in recent years have identified inconsistencies in results between surveys, and a need exists to analyze the surveys using modern methods and examine possible causes of differences in survey results. Call-Count Survey observers collect separate information on number of doves heard and number of doves seen during counting, whereas BBS observers record one index containing all doves observed. We used hierarchical log-linear models to estimate trend and annual indices of abundance for 1966–2007 from BBS data, CCS-heard data, and CCS-seen data. Trend estimates from analyses provided inconsistent results for several states and for eastern and central dove-management units. We examined differential effects of change in land use and noise-related disturbance on the CCS indices. Changes in noise-related disturbance along CCS routes had a larger influence on the heard index than on the seen index, but association analyses among states of changes in temperature and of amounts of developed land suggest that CCS indices are differentially influenced by changes in these environmental features. Our hierarchical model should be used to estimate population change from dove surveys, because it provides an efficient framework for estimating population trends from dove indices while controlling for environmental features that differentially influence the indices.     

Propagation of activity along the “stepping strip” of the cat spinal cord

Three points located approximately 8 mm apart were identified in a dorsolateral funiculus of the lower thoracic spinal cord in mesencephalic cats, each producing stepping movements on the ipsilateral hindlimb when stimulated. An area 5–17 mm caudal to the caudal stepping point (SP) was scanned for neurons responding synaptically to stimulating the rostral or caudal SP prior and subsequent to electrolytic coagulation of the medial SP. Relative incidence of neurons excited by stimulating the caudal SP did not change following this type of lesioning, although stimulation of the rostral SP at the rate of 4 Hz induced response 5 times less frequently than before. Even stimulation of the rostral SP at the rate of 40–60 Hz, which had considerably increased firing index prior to coagulation, could only produce excitation in tiny numbers of neurons. This indicates that synaptic excitation of neurons becomes considerably more difficult once the stepping strip between stimulation and recording sites has been damaged.Institute for Research into Information Transmission, Academy of Sciences of the USSR, Moscow. Translated from Neirofiziologiya, Vol. 20, No. 6, pp. 763–769, November–December, 1988.