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The article offers information on a study that used a case-finding method to identify adults in the community with respiratory symptoms but without a diagnosed lung disease. Topics include the study's methodology, which involved enrollment in a randomized trial to determine if early diagnosis and treatment reduce health care utilization and improve outcomes, and the results, which showed lower health care utilization.

Osteoporotic hip fractures in the elderly are associated with a high mortality in the first year following fracture and a high incidence of disability among survivors. We study first and second fractures of elderly women using data from the Study of Osteoporotic Fractures. We present a new conceptual framework, stochastic model, and statistical methodology for time to fracture. Our approach gives additional insights into the patterns for first and second fractures and the concomitant risk factors. Our modeling perspective involves a novel time-to-event methodology called threshold regression, which is based on the plausible idea that many events occur when an underlying process describing the health or condition of a person or system encounters a critical boundary or threshold for the first time. In the parlance of stochastic processes, this time to event is a first hitting time of the threshold. The underlying process in our model is a composite of a chronic degradation process for skeletal health combined with a random stream of shocks from external traumas, which taken together trigger fracture events. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary Birth weight and gestational age are important measures of a newborn's intrinsic health, serving both as outcome measures and explanatory variables in health studies. The measures are highly correlated but occasionally inconsistent. We anticipate that health researchers and other scientists would be helped by summary indexes of birth weight and gestational age that give more precise indications of whether the birth outcome is healthy or not. We propose a pair of indexes that we refer to as the birth normalcy index or BNI and birth discrepancy index or BDI. Both indexes are simple functions of birth weight and gestational age and in logarithmic form are orthogonal by construction. The BNI gauges whether the birth weight and gestational age combination are in a normal range. The BDI gauges whether birth weight and gestational age are consistent. We present a three-component mixture model for BNI, with the components representing premature, at-risk, and healthy births. The BNI distribution is derived from a stochastic model of fetal development proposed by Whitmore and Su (2007, Lifetime Data Analysis 13, 161-190) and takes the form of a mixture of inverse Gaussian distributions. We present a noncentral t-distribution as a model for BDI. BNI and BDI are also well suited for making comparisons of birth outcomes in different reference populations. A simple z-score and t-score are proposed for such comparisons. The BNI and BDI distributions can be estimated for births in any reference population of interest using threshold regression. [ABSTRACT FROM AUTHOR]

Time-to-event data with time-varying covariates pose an interesting challenge for statistical modeling and inference, especially where the data require a regression structure but are not consistent with the proportional hazard assumption. Threshold regression (TR) is a relatively new methodology based on the concept that degradation or deterioration of a subject's health follows a stochastic process and failure occurs when the process first reaches a failure state or threshold (a first-hitting-time). Survival data with time-varying covariates consist of sequential observations on the level of degradation and/or on covariates of the subject, prior to the occurrence of the failure event. Encounters with this type of data structure abound in practical settings for survival analysis and there is a pressing need for simple regression methods to handle the longitudinal aspect of the data. Using a Markov property to decompose a longitudinal record into a series of single records is one strategy for dealing with this type of data. This study looks at the theoretical conditions for which this Markov approach is valid. The approach is called threshold regression with Markov decomposition or Markov TR for short. A number of important special cases, such as data with unevenly spaced time points and competing risks as stopping modes, are discussed. We show that a proportional hazards regression model with time-varying covariates is consistent with the Markov TR model. The Markov TR procedure is illustrated by a case application to a study of lung cancer risk. The procedure is also shown to be consistent with the use of an alternative time scale. Finally, we present the connection of the procedure to the concept of a collapsible survival model. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In epidemiological and clinical studies, time-to-event data often violate the assumptions of Cox regression due to the presence of time-dependent covariate effects and unmeasured risk factors. An alternative approach, which does not require proportional hazards, is to use a first hitting time model which treats a subject's health status as a latent stochastic process that fails when it reaches a threshold value. Although more flexible than Cox regression, existing methods do not account for unmeasured covariates in both the initial state and the rate of the process. To address this issue, we propose a Bayesian methodology that models an individual's health status as a Wiener process with subject-specific initial state and drift. Posterior inference proceeds via a Markov chain Monte Carlo methodology with data augmentation steps to sample the final health status of censored observations. We apply our method to data from melanoma patients with nonproportional hazards and find interesting differences from a similar model without random effects. In a simulation study, we show that failure to account for unmeasured covariates can lead to inaccurate estimates of survival probabilities. [ABSTRACT FROM PUBLISHER]

This paper identifies and quantifies the clay assemblage within coastal dunes of northern KwaZulu-Natal, South Africa, and proposes a model for clay distribution and abundance. Distinct dune units identified by previous authors on the basis of the sand fraction are redefined on the basis of the composition of the clay fraction. The most mature dune unit is classified as Unit 0 (250-359? ka) and is characterised by a clay assemblage rich in kaolinite and goethite. Units 1 and 2 (19-250 ka) are of intermediate maturity and comprise kaolinite, interstratified clays, quartz, and goethite. The relatively immature Units 3 and 4 (Recent to 19 ka) are characterised by calcite, quartz, vermiculite, and montmorillonite. The relative abundance of clay minerals in dune units allows weathering and postdeposition alteration processes within the dunes to be investigated. From these, a simplified weathering sequence with increasing maturity can be summarised as follows: quartz + feldspars + calcite + heavy minerals → illite + vermiculite → illite + interstratified clays → illite + montmorillonite → halloysite + interstratified clays + kaolinite → kaolinite. Past work and field studies indicate that the dune cordon acts as an open system of high permeability, resulting in carbonates and mobile ions being readily leached and lost from the system. However, when prolonged weathering has caused an accumulation of fine-grained material, permeability decreases substantially, whereby mobile cations and silica cannot be readily evacuated. This can result in the formation of montmorillonite rather than kaolinite, even when carbonates have been leached. The dominant controlling factors in determining clay assemblages are pH and leaching conditions. This observed weathering profile is complicated by reworking, the type and distribution of vegetation, and water table changes because of sea level changes. [ABSTRACT FROM AUTHOR]

Considers a multivariate survival distribution derived from an inverse Gaussian mixture of exponential distributions. Investigation of the multivariate survival distribution; Multivariate distribution based on an inverse Gaussian mixture of exponentials; Inference methods for multivariate distribution; Assessment of the model adequacy; Case examples of assessment of the model adequacy.

A general approach to the study of length of stay (LOS) for hospitalization is presented. Data on first hospitalization for schizophrenia from the Maryland Psychiatric Case Register are applied to discussions of the Life Table and seven stochastic models of the LOS process. As far as possible, prior applications of the various models to this process are reviewed, and the models are conceptualized on the individual and aggregate level. The models are the exponential, mixed exponential, type XI, Weibull, gamma, lognormal and Inverse Gaussian. The lognormal and Inverse Gaussian show the best fits to the data in terms of the maximum absolute deviation. However, the Inverse Gaussian is superior due to its attractive statistical characterization. Special attention is given to the relatively new Inverse Ganssian, and there is a brief section on LOS and theory verification. Recommendations are made for future LOS research. [ABSTRACT FROM AUTHOR]

Le test d'Ames pour la salmonelle est une méthode de bioessai utilisée couramment pour évaluer le pouvoir mutagène d'un agent cancérigène potentiel. Le test est relativement rapide et fiable, et exploite la corrélation existant entre potentiel mutagène et potentiel cancérigène. Les données pour cette étude de cas proviennent d'une étude internationale impliquant vingt laboratoires dans neuf pays. Les laboratoires ont participé à une expérience planifiée pour laquelle le pouvoir mutagène de certaines substances (préparations chimiques complexes du type rencontré dans l'environnement) fut évalué à l'aide du test d'Ames. Un protocole rigoureux fut suivi. Le but principal de l'étude était d'examiner les variations intra- et inter-laboratoire dans les résultats du test. Les données consistent des comptes de colonies de salmonelle ayant subit une mutation inverse, à six doses différentes d'une substance. Chaque laboratoire participant fournit des données pour chacune des cinq substances à tester. Les bioessais furent effectués d'après un plan d'expérience factoriel prescrit. Trois groupes d'analystes participèrent à cette étude. Il leur fut demandé de modéliser la relation dose-réponse pour deux substances, de développer un indice pour la force de cette relation et d'évaluer les variations intra- et inter-laboratoire dans les résultats des bioessais. [ABSTRACT FROM AUTHOR]

Les brasseurs aiment connaǐtre la relation entre les caractéristiques chimiques de leurs bières et les préférences exprimées par les consommateurs. Deux ensembles de données furent fournis aux analystes. Le premier comprenait une série de caractéristiques chimiques mesurées sur 91 bières ainsi que des mesures de préférence, pour les měmes bières, recueillies lors de tests où les types de bières ne sont pas connus des dégustateurs. On demanda aux analystes d'utiliser ces données afin d'élaborer un modèle statistique permettant de relier les caractéristiques chimiques d'une bière aux préférences des consommateurs. Le deuxième ensemble de données était constitué de mesures sur 37 bières non incluses dans le premier ensemble. Les analystes devaient utiliser le modèle proposé afin de prédire la cote de préférence des consommateurs pour ces bières. On compare les performances des différents modèles proposés. [ABSTRACT FROM AUTHOR]

The article presents comments on the relationship between the mean preference rating of a beer and the chemical and other variables for each beer and 90% prediction intervals for the sample of 37 beers with withheld preference ratings. The CDR and LP teams reportedly used a linear regression model for the mean preference rating in the form Yij = Ai + xijB + Eij. It is shown that in the two models that include the variables COLOUR, BU and 2-METHYL-1-BUTANOL only as main effects, the regression coefficients agree in sign but differ in magnitude.

L'estimation des vents extrêcmes est essentielle aux travaux de génie mais pour obtenir de telles estimations, on doit tenir compte de considérations d'ordre statistique majeures. Dans I'étude de cas que nous présentons, on a fourni aux analystes des lectures à toutes les heures de la vitesse du vent, de sa direction et de la pression barométrique, mesures qui furent prélevées à cinq stations canadiennes durant des périodes d'observation couvrant plusieurs décennies. Leur tâche était de construire des estimations ponctuelles et par intervalle des vitesses de vent extrêmes déjà rencontrées à ces stations sur des périodes de 10, 20, 50 et 100 ans (il s'agit de quantiles). [ABSTRACT FROM AUTHOR]

Statistique Canada compile des données détaillées sur tous les homicides rapportés à la police à travers le pays. Dans cette étude, trois équipes d'analystes cherchent indépendamment à déceler une saisonnalité ou toute autre tendance temporelle dans le taux et la fréquence des homicides commis au Canada entre 1961 et 1980. [ABSTRACT FROM AUTHOR]

The article presents a study that deals with iceberg movements, weather, and ocean conditions in the vicinity of an oil-drilling platform in the South Labrador Sea. The study constructs a probabilistic forecasting model of iceberg motion. It aims to provide a platform operator with an estimate of the collision risk posed to the platform by a nearby iceberg.

The article describes a case study that illustrated the diversity of statistical analyses of the same empirical evidence. Two sets of analysts examine health data gathered from firefighters who were exposed to fumes and explosions from the Mississauga, Ontario train derailment in November 1979 to determine whether exposure to hazardous chemicals had lasting effects on the lung function of the Mississauga firefighters. Analysts were suggested to look for predictive values for some lung tests on healthy people.

Cet article étudie les méthodes de régression multiple applicables aux données progressivement censurées à droite et provenant d'une distribution gaussienne inverse. On utilise l'algorithme EM qui permet d'obtenir des estimateurs à maximum de vraisemblance et on présente ensuite les propriétés inhérentes à leur distribution asymptotique. La méthodologie est illustrée à l'aide de deux études de cas, dont une a trait à la caractéristique défectueuse de la distribution gaussienne inverse. Finalement, on tente d'établir un paralléle entre la methodologie utilisée dans les méthodes de régression pour échantillons complets à distribution gaussienne inverse et celle utilistée dans les autres méthodes de régression pour données de survie censurées. [ABSTRACT FROM AUTHOR]

The author comments on a case study that analyzed extreme wind speed in five Canadian locations. According to the author, although different estimation methods were used by the investigators, the fact that both arrived on the same estimates on the same data is reassuring. The author adds that the task in this case is the estimation of extreme fractile such as extreme winds associated with tornadoes which was omitted, and a valid prediction interval would require that this source be taken into account by some means.

The article presents a case study that deals with the potential of statistical modelling and ARIMA methodology to describe and forecast iceberg paths. The models were applied using virtual real-time implementation for platform operator to use them on site and without processing delays. It questions the accuracy of the forecasts and risk assessments provided by the models to win the confidence of platform operators. It calls for settling other issues before determining the usefulness of such models.

A first-hitting-time (FHT) survival model postulates a health status process for a patient that gradually declines until the patient dies when the level first reaches a critical threshold. Threshold regression (TR) is a new regression methodology that incorporates the effects of covariates on the threshold and process parameters of this FHT model. In this study, we use TR to analyze data from a randomized clinical trial of treatment for multiple myeloma. The trial compares VELCADE and high-dose Dexamethasone, the former a new therapy and the latter an established therapy for this disease. Patients are switched between the two drugs based on patient response. The novel contribution of this work is the modeling of this clinical trial design using a mixture of TR models. Specifically, we propose a mixture FHT model to fit the survival distribution. The model includes a composite time scale that differentiates the rate of disease progression before and after switching. The analysis shows significant benefit from initial treatment by VELCADE. A comparison is made with a Cox proportional hazards regression analysis of the same data. [ABSTRACT FROM AUTHOR]

This paper describes a general methodology for the analysis of differential gene expression based on microarray data. First, we characterize the data by a linear statistical model that accounts for relevant sources of variation in the data and then we consider estimation of the model parameters. Because microarray studies typically involve thousands of genes, we propose a two-stage method for parameter estimation. The interaction terms for genes and experimental conditions in this model capture all relevant information about differential gene expression in the microarray data. We propose a mixture distribution model for a summary statistic of differential expression that consists of null and alternative component distributions. The mixture model suggests two methods for identifying genes exhibiting differential expression. One is a frequentist method that identifies distinguished genes and the other an empirical Bayes procedure that yields estimated posterior probabilities of differential expression, conditional on observed microarray readings. An extensive case application involving juvenile cystic kidney disease in mice is used to illustrate the methodology. The application controls for variation arising from array, color channel, experimental condition (tissue type), and gene, with the analysis of variance (ANOVA) model including both main effects to normalize the expression data and all interaction terms involving genes. The gene expression profile is found to vary by tissue type as expected, but also by color channel, which was less expected. A concluding section discusses some outstanding research questions related to the analysis of microarray data. [ABSTRACT FROM AUTHOR]

We develop an improved empirical Bayes estimation methodology for the analysis of two-state Markov chains observed from heterogeneous individuals. First, the two transition probabilities corresponding to each chain are assumed to be drawn from a common, bivariate distribution that has beta marginals. Second, randomly missing observations are incorporated into the likelihood for the hyperparameters by efficiently summing over all possible values for the missing observations. A likelihood ratio test is used to test for dependence between the transition probabilities. Posterior distributions for the transition probabilities are also derived, as is an approximation for the equilibrium probabilities. The proposed procedures are illustrated in a numerical example and in an analysis of longitudinal store display data. [ABSTRACT FROM AUTHOR]