A Non-parametric Frailty Model for Temporally Clustered Multivariate Failure Times

Abstract A model is introduced here for multivariate failure time data arising from heterogenous populations. In particular, we consider a situation in which the failure times of individual subjects are often temporally clustered, so that many failures occur during a relatively short age interval. The clustering is modelled by assuming that the subjects can be divided into ‘internally homogenous’ latent classes, each such class being then described by a time‐dependent frailty profile function. As an example, we reanalysed the dental caries data presented earlier in Härkänen et al. [Scand. J. Statist. 27 (2000) 577], as it turned out that our earlier model could not adequately describe the observed clustering.     

Bootstrap Estimation of Benchmark Doses and Confidence Limits with Clustered Quantal Data

The benchmark dose (BMD) is an exposure level that would induce a small risk increase (BMR level) above the background. The BMD approach to deriving a reference dose for risk assessment of noncancer effects is advantageous in that the estimate of BMD is not restricted to experimental doses and utilizes most available dose-response information. To quantify statistical uncertainty of a BMD estimate, we often calculate and report its lower confidence limit (i.e., BMDL), and may even consider it as a more conservative alternative to BMD itself. Computation of BMDL may involve normal confidence limits to BMD in conjunction with the delta method. Therefore, factors, such as small sample size and nonlinearity in model parameters, can affect the performance of the delta method BMDL, and alternative methods are useful. In this article, we propose a bootstrap method to estimate BMDL utilizing a scheme that consists of a resampling of residuals after model fitting and a one-step formula for parameter estimation. We illustrate the method with clustered binary data from developmental toxicity experiments. Our analysis shows that with moderately elevated dose-response data, the distribution of BMD estimator tends to be left-skewed and bootstrap BMDL s are smaller than the delta method BMDL s on average, hence quantifying risk more conservatively. Statistically, the bootstrap BMDL quantifies the uncertainty of the true BMD more honestly than the delta method BMDL as its coverage probability is closer to the nominal level than that of delta method BMDL. We find that BMD and BMDL estimates are generally insensitive to model choices provided that the models fit the data comparably well near the region of BMD. Our analysis also suggests that, in the presence of a significant and moderately strong dose-response relationship, the developmental toxicity experiments under the standard protocol support dose-response assessment at 5% BMR for BMD and 95% confidence level for BMDL.     

The use of GIS in informal settlement upgrading: its role and impact on the community and on local government

The Urban Geographical Information Systems (GIS) Group within the Department of Civil Engineering at the University of Cape Town has been coordinating a pilot informal settlement upgrading in Cape Town since 1998. The project objective has been the evolution of a model-based approach to informal settlement upgrading that is both structured and replicable. It was felt that the only way this could be achieved was through the use of a spatial data management system operated through a GIS system. The spatial database has been used for all facets of data collection and data process and forms the basis for all decision-making. Thus it covers all physical data pertaining to the site, cadastral and shack data, demographic and socio-economic data (with an in-depth review of every household) economic opportunities and physical planning and design data. The result is a comprehensive, integrated, settlement upgrading methodology that is built upon a GIS-based spatial data management framework. Such a framework is seen as the basic building block for large-scale informal settlement upgrading.     

Tree health mapping with multispectral remote sensing data at UC Davis, California

Tree health is a critical parameter for evaluating urban ecosystem health and sustainability. Traditionally, this parameter has been derived from field surveys. We used multispectral remote sensing data and GIS techniques to determine tree health at the University of California, Davis. The study area (363 ha) contained 8,962 trees of 215 species. Tree health conditions were mapped for each physiognomic type at two scales: pixel and whole tree. At the pixel scale, each tree pixel within the tree crown was classified as either healthy or unhealthy based on vegetation index values. At the whole tree scale, raster based statistical analysis was used to calculate tree health index which is the ratio of healthy pixels to entire tree pixels within the tree crown. The tree was classified as healthy if the index was greater than 70%. Accuracy was checked against a random sample of 1,186 trees. At the whole tree level, 86% of campus trees were classified as healthy with 88% mapping accuracy. At the pixel level, 86% of the campus tree cover was classified as healthy. This tree health evaluation approach allows managers to identify the location of unhealthy trees for further diagnosis and treatment. It can be used to track the spread of disease and monitor seasonal or annual changes in tree health. Also, it provides tree health information that is fundamental to modeling and analysis of the environmental, social, and economic services produced by urban forests.     

Confidence Intervals for Current Status Data

Abstract.  The likelihood ratio statistic for testing pointwise hypotheses about the survival time distribution in the current status model can be inverted to yield confidence intervals (CIs). One advantage of this procedure is that CIs can be formed without estimating the unknown parameters that figure in the asymptotic distribution of the maximum likelihood estimator (MLE) of the distribution function. We discuss the likelihood ratio-based CIs for the distribution function and the quantile function and compare these intervals to several different intervals based on the MLE. The quantiles of the limiting distribution of the MLE are estimated using various methods including parametric fitting, kernel smoothing and subsampling techniques. Comparisons are carried out both for simulated data and on a data set involving time to immunization against rubella. The comparisons indicate that the likelihood ratio-based intervals are preferable from several perspectives.     

An evaluation of the childhood family structure measures from the sixth wave of the British Household Panel Survey

Summary.  The paper performs an evaluation of the data that were collected in the sixth wave of the British Household Panel Survey (BHPS) on childhood family structure. After comparing such data with a large number of studies by using external sources, we find that the BHPS data overestimate the proportion of people who report an experience of life in a non-intact family during childhood by about 10%. Although an explanation based on recall error that deteriorates with the age of the BHPS respondents is possible, the overestimation is likely to be accounted for by non-ignorable attrition that may affect most of the comparison studies based on longitudinal data. Conversely, comparisons with other independent measurements from the BHPS itself reveal that the wave 6 data underestimate the proportion of young people who have lived at least part of their childhood in a non-intact family by about 8%. The probability of disagreement between these two sets of measures is strongly associated with poor interview characteristics, which may affect the comparison measure more than the wave 6 measure. Despite such differences, there is therefore a substantial degree of similarity between the family structure information that was collected in the sixth wave of the BHPS and the host of highly diverse records against which it has been compared.     

Semiparametric linear transformation models for current status data

Current status data arise when the death of every subject in a study cannot be determined precisely, but is known only to have occurred before or after a random monitoring time. The authors discuss the analysis of such data under semiparametric linear transformation models for which they propose a general inference procedure based on estimating functions. They determine the properties of the estimates they propose for the regression parameters of the model and illustrate their technique using tumorigenicity data.     

中国高龄老人生活自理能力纵向动态研究

利用"中国老年健康长寿跟踪调查"1998年、2000年、2002年的跟踪数据分析中国高龄老人ADL的变动趋势及其影响因素。个体增长模型结果显示,高龄老人ADL状况随年龄增长出现了明显的下降趋势,ADL个体差异十分显著。个体之间的差异解释了ADL总差异的31.13%。经历三次追踪调查的高龄老人ADL均值较高,状况较好,下降速度较慢;基期具有3-4项残障的高龄老人,未来ADL下降最为迅速;基期ADL水平高的高龄老人,ADL下降较慢。从ADL的个体影响因素来看,对于基期ADL水平影响最大的变量是"年龄"、"疾病状况"以及"居住安排"等,而对观测期ADL变动速度影响最大的变量是"年龄"以及"过去是否经常从事体力劳动"等。     

Analysis of Competing Risks by Using Bayesian Smoothing

We consider the competing risks set-up. In many practical situations, the conditional probability of the cause of failure given the failure time is of direct interest. We propose to model the competing risks by the overall hazard rate and the conditional probabilities rather than the cause-specific hazards. We adopt a Bayesian smoothing approach for both quantities of interest. Illustrations are given at the end.     

Binary Dynamic Logit for Correlated Ordinal: estimation,application and simulation

We evaluate the estimation performance of the Binary Dynamic Logit model for correlated ordinal variables (BDLCO model), and compare it to GEE and Ordinal Logistic Regression performance in terms of bias and Mean Absolute Percentage Error (MAPE) via Monte Carlo simulation. Our results indicate that when the proportional-odds assumption does not hold, the proposed BDLCO method is superior to existing models in estimating correlated ordinal data. Moreover, this method is flexible in terms of modeling dependence and allows unequal slopes for each category, and can be used to estimate an apple bloom data set where the proportional-odds assumption is violated. We also provide a function in R to implement BDLCO.