A bayesian model for mixed responses and skew laten variable to measure HRQoL in children

The aim of the thesi is to formulate a suitable Item Response Theory (IRT) based model to measure HRQoL (as latent variable) using a mixed responses questionnaire and relaxing the hypothesis of normal distributed latent variable. The new model is a combination of two models already presented in literature, that is, a latent trait model for mixed responses and an IRT model for Skew Normal latent variable. It is developed in a Bayesian framework, a Markov chain Monte Carlo procedure is used to generate samples of the posterior distribution of the parameters of interest. The proposed model is test on a questionnaire composed by 5 discrete items and one continuous to measure HRQoL in children, the EQ-5D-Y questionnaire. A large sample of children collected in the schools was used. In comparison with a model for only discrete responses and a model for mixed responses and normal latent variable, the new model has better performances, in term of deviance information criterion (DIC), chain convergences times and precision of the estimates.

Charakterisierung und Konvergenz mehrdimensionaler kontinuierlicher Verzweigungsprozesse

In dieser Arbeit wird eine Klasse von stochastischen Prozessen untersucht, die eine abstrakte Verzweigungseigenschaft besitzen. Die betrachteten Prozesse sind homogene Markov-Prozesse in stetiger Zeit mit Zuständen im mehrdimensionalen reellen Raum und dessen Ein-Punkt-Kompaktifizierung. Ausgehend von Minimalforderungen an die zugehörige Übergangsfunktion wird eine vollständige Charakterisierung der endlichdimensionalen Verteilungen mehrdimensionaler kontinuierlicher Verzweigungsprozesse vorgenommen. Mit Hilfe eines erweiterten Laplace-Kalküls wird gezeigt, dass jeder solche Prozess durch eine bestimmte spektral positive unendlich teilbare Verteilung eindeutig bestimmt ist. Umgekehrt wird nachgewiesen, dass zu jeder solchen unendlich teilbaren Verteilung ein zugehöriger Verzweigungsprozess konstruiert werden kann. Mit Hilfe der allgemeinen Theorie Markovscher Operatorhalbgruppen wird sichergestellt, dass jeder mehrdimensionale kontinuierliche Verzweigungsprozess eine Version mit Pfaden im Raum der cadlag-Funktionen besitzt. Ferner kann die (funktionale) schwache Konvergenz der Prozesse auf die vage Konvergenz der zugehörigen Charakterisierungen zurückgeführt werden. Hieraus folgen allgemeine Approximations- und Konvergenzsätze für die betrachtete Klasse von Prozessen. Diese allgemeinen Resultate werden auf die Unterklasse der sich verzweigenden Diffusionen angewendet. Es wird gezeigt, dass für diese Prozesse stets eine Version mit stetigen Pfaden existiert. Schließlich wird die allgemeinste Form der Fellerschen Diffusionsapproximation für mehrtypige Galton-Watson-Prozesse bewiesen.

A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

Wavelet estimation in diffusions with periodicity

Wir betrachten einen zeitlich inhomogenen Diffusionsprozess, der durch eine stochastische Differentialgleichung gegeben wird, deren Driftterm ein deterministisches T-periodisches Signal beinhaltet, dessen Periodizität bekannt ist. Dieses Signal sei in einem Besovraum enthalten. Wir schätzen es mit Hilfe eines nichtparametrischen Waveletschätzers. Unser Schätzer ist von einem Wavelet-Dichteschätzer mit Thresholding inspiriert, der 1996 in einem klassischen iid-Modell von Donoho, Johnstone, Kerkyacharian und Picard konstruiert wurde. Unter gewissen Ergodizitätsvoraussetzungen an den Prozess können wir nichtparametrische Konvergenzraten angegeben, die bis auf einen logarithmischen Term den Raten im klassischen iid-Fall entsprechen. Diese Raten werden mit Hilfe von Orakel-Ungleichungen gezeigt, die auf Ergebnissen über Markovketten in diskreter Zeit von Clémencon, 2001, beruhen. Außerdem betrachten wir einen technisch einfacheren Spezialfall und zeigen einige Computersimulationen dieses Schätzers.

Improved detection of blood stream pathogens by real-time PCR in severe sepsis

Evaluation of the technical and diagnostic feasibility of commercial multiplex real-time polymerase chain reaction (PCR) for detection of blood stream infections in a cohort of intensive care unit (ICU) patients with severe sepsis, performed in addition to conventional blood cultures.

Free-Ranging Wild Boar: A Disease Threat to Domestic Pigs in Switzerland?

The risk of transmission of pathogens from free-ranging wild boars (Sus scrofa scrofa) to outdoor domestic pigs (S. scrofa domesticus) is of increasing concern in many European countries. We assess this risk, using Switzerland as an example. We estimated 1) the prevalence of important pathogens in wild boars and 2) the risk of interactions between wild boars and outdoor pigs. First, we tested 252 wild boars from selected areas between 2008 and 2010 for infection with Brucella spp. Bacterial prevalence was estimated to 28.8% (confidence interval [CI] 23.0-34.0) when using bacterial culture (B. suis Biovar 2) and real-time polymerase chain reaction. Antibody prevalence was 35.8% (CI 30.0-42.0), which was significantly higher than in previous studies in Switzerland. We also tested 233 wild boars for porcine reproductive and respiratory syndrome virus (PRRSV). Antibody prevalence was 0.43% (CI 0.01-2.4) for EU-PRRSV and real-time reverse transcription polymerase chain reaction results were negative. These findings suggest that B. suis is increasingly widespread in wild boars and PRRSV is currently not of concern. Second, we documented the spatial overlap between free-ranging wild boars and outdoor piggeries by mapping data on their respective occurrence. Wild boars are most widespread in the mountain range along the western and northern Swiss borders, while most piggeries are located in central lowlands. A risk of interaction is mainly expected at the junction between these two bioregions. This risk may increase if wild boars expand eastward and southward beyond anthropogenic barriers believed to limit their range. Therefore, we evaluated the potential of expansion of the wild boar population. Population trends suggest a continuous increase of wild boars for the past 15 yr. Surveillance of selected wildlife passages using cameras on highways and main roads indicates that these barriers are permeable (average of up to 13 wild boar crossings per 100 days). Thus an increase of wild boar range should be considered. There may be a risk of B. suis spillover from wild boars in Switzerland, which could increase in the future. Data on the occurrence of interactions between pigs and wild boars are needed to assess this risk.

Development and validation of a real-time quantitative PCR assay for rapid identification of Bacillus anthracis in environmental samples

A real-time polymerase chain reaction (PCR) assay was developed for rapid identification of Bacillus anthracis in environmental samples. These samples often harbor Bacillus cereus bacteria closely related to B. anthracis, which may hinder its specific identification by resulting in false positive signals. The assay consists of two duplex real-time PCR: the first PCR allows amplification of a sequence specific of the B. cereus group (B. anthracis, B. cereus, Bacillus thuringiensis, Bacillus weihenstephanensis, Bacillus pseudomycoides, and Bacillus mycoides) within the phosphoenolpyruvate/sugar phosphotransferase system I gene and a B. anthracis specific single nucleotide polymorphism within the adenylosuccinate synthetase gene. The second real-time PCR assay targets the lethal factor gene from virulence plasmid pXO1 and the capsule synthesis gene from virulence plasmid pXO2. Specificity of the assay is enhanced by the use of minor groove binding probes and/or locked nucleic acids probes. The assay was validated on 304 bacterial strains including 37 B. anthracis, 67 B. cereus group, 54 strains of non-cereus group Bacillus, and 146 Gram-positive and Gram-negative bacteria strains. The assay was performed on various environmental samples spiked with B. anthracis or B. cereus spores. The assay allowed an accurate identification of B. anthracis in environmental samples. This study provides a rapid and reliable method for improving rapid identification of B. anthracis in field operational conditions.

Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

Development of two real-time PCR assays for the detection of Mycoplasma hyopneumoniae in clinical samples

In order to improve the diagnosis of enzootic pneumonia (EP) in pigs two real-time polymerase chain reaction (rtPCR) assays for the detection of Mycoplasma hyopneumoniae in bronchial swabs from lung necropsies were established and validated in parallel. As a gold standard, the current "mosaic diagnosis" was taken, including epidemiological tracing, clinical signs, macro- and histopathological lesions of the lungs and immunofluorescence. One rtPCR is targeting a repeated DNA element of the M. hyopneumoniae genome (REP assay), the other a putative ABC transporter gene (ABC assay). Both assays were shown to be specific for M. hyopneumoniae and did not cross react with other bacteria and mollicutes from pig. With material from pigs of defined EP-negative farms the two assays showed to be 100% specific. When testing lungs from pig farms with EP, the REP assay detected 50% and the ABC assay 90% of the farms as positive. Both tests together detected all positive farms. Within a positive herd the two assays tested similarly with on average over 90% of the lung samples analysed from a single farm showing positive scores. A series of samples with suspicion of EP and samples from pigs with diseases other than respiratory taken from current routine diagnostic was assayed. None of the assays showed false positive results. The sensitivities in this sample group were 50% for the REP and 70% for the ABC assays and for both assays together 85%. The two assays run in parallel are therefore a valuable tool for the improvement of the current diagnosis of EP.

A longitudinal study on delirium in nursing homes

OBJECTIVE To investigate the evolution of delirium of nursing home (NH) residents and their possible predictors. DESIGN Post-hoc analysis of a prospective cohort assessment. SETTING Ninety NHs in Switzerland. PARTICIPANTS Included 14,771 NH residents. MEASUREMENTS The Resident Assessment Instrument Minimum Data Set and the Nursing Home Confusion Assessment Method were used to determine follow-up of subsyndromal or full delirium in NH residents using discrete Markov chain modeling to describe long-term trajectories and multiple logistic regression analyses to determine predictors of the trajectories. RESULTS We identified four major types of delirium time courses in NH. Increasing severity of cognitive impairment and of depressive symptoms at the initial assessment predicted the different delirium time courses. CONCLUSION More pronounced cognitive impairment and depressive symptoms at the initial assessment are associated with different subsequent evolutions of delirium. The presence and evolution of delirium in the first year after NH admission predicted the subsequent course of delirium until death.

Characterisation of flash floods in small ungauged mountain basins of Central Spain using an integrated approach

One of the main problems of flood hazard assessment in ungauged or poorly gauged basins is the lack of runoff data. In an attempt to overcome this problem we have combined archival records, dendrogeomorphic time series and instrumental data (daily rainfall and discharge) from four ungauged and poorly gauged mountain basins in Central Spain with the aim of reconstructing and compiling information on 41 flash flood events since the end of the 19th century. Estimation of historical discharge and the incorporation of uncertainty for the at-site and regional flood frequency analysis were performed with an empirical rainfall–runoff assessment as well as stochastic and Bayesian Markov Chain Monte Carlo (MCMC) approaches. Results for each of the ungauged basins include flood frequency, severity, seasonality and triggers (synoptic meteorological situations). The reconstructed data series clearly demonstrates how uncertainty can be reduced by including historical information, but also points to the considerable influence of different approaches on quantile estimation. This uncertainty should be taken into account when these data are used for flood risk management.

A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

Simulation study of joint transition and Weibull survival model with shared parameters

This study investigates a theoretical model where a longitudinal process, that is a stationary Markov-Chain, and a Weibull survival process share a bivariate random effect. Furthermore, a Quality-of-Life adjusted survival is calculated as the weighted sum of survival time. Theoretical values of population mean adjusted survival of the described model are computed numerically. The parameters of the bivariate random effect do significantly affect theoretical values of population mean. Maximum-Likelihood and Bayesian methods are applied on simulated data to estimate the model parameters. Based on the parameter estimates, predicated population mean adjusted survival can then be calculated numerically and compared with the theoretical values. Bayesian method and Maximum-Likelihood method provide parameter estimations and population mean prediction with comparable accuracy; however Bayesian method suffers from poor convergence due to autocorrelation and inter-variable correlation. ^

A multivariate frailty model for disease recurrences and survival

A multivariate frailty hazard model is developed for joint-modeling of three correlated time-to-event outcomes: (1) local recurrence, (2) distant recurrence, and (3) overall survival. The term frailty is introduced to model population heterogeneity. The dependence is modeled by conditioning on a shared frailty that is included in the three hazard functions. Independent variables can be included in the model as covariates. The Markov chain Monte Carlo methods are used to estimate the posterior distributions of model parameters. The algorithm used in present application is the hybrid Metropolis-Hastings algorithm, which simultaneously updates all parameters with evaluations of gradient of log posterior density. The performance of this approach is examined based on simulation studies using Exponential and Weibull distributions. We apply the proposed methods to a study of patients with soft tissue sarcoma, which motivated this research. Our results indicate that patients with chemotherapy had better overall survival with hazard ratio of 0.242 (95% CI: 0.094 - 0.564) and lower risk of distant recurrence with hazard ratio of 0.636 (95% CI: 0.487 - 0.860), but not significantly better in local recurrence with hazard ratio of 0.799 (95% CI: 0.575 - 1.054). The advantages and limitations of the proposed models, and future research directions are discussed. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^