Multivariate Probit Models for Interval-Censored Failure Time Data

Survival analysis is an important branch of statistics that analyzes the time to event data. The events of interest can be death, disease occurrence, the failure of a machine part, etc.. One important feature of this type of data is censoring: information on time to event is not observed exactly due to loss to follow-up or non-occurrence of interested event before the trial ends. Censored data are commonly observed in clinical trials and epidemiological studies, since monitoring a person’s health over time after treatment is often required in medical or health studies. In this dissertation we focus on studying multivariate interval-censored data, a special type of survival data. By saying multivariate interval-censored data, we mean that there are multiple failure time events of interest, and these failure times are known only to lie within certain intervals instead of being observed exactly. These events of interest can be associated because of sharing some common characteristics. Multivariate interval-censored data draw more and more attention in epidemiological, social-behavioral and medical studies, in which subjects are examined multiple times and several events of interest are tested at the observation times. There are some existing methods available in literatures for analyzing multivariate interval-censored failure time data. Various models were developed for regression analysis. However, due to the complicated correlation structure between events, analyzing such type of survival data is much more difficult and new efficient methodologies are needed. Chapter 1 of this dissertation illustrates the important concepts of interval-censored data with several real data examples. A literature review of existing regression models and approaches is included as well. Chapter 2 introduces a new normal-frailty multivariate probit model for regression analysis of interval-censored failure time data and proposes an efficient Bayesian approach to get parameter estimates. Simulations and an analysis on a real data set are conducted to evaluate and illustrate the performance of this new method. This new approach is proved efficient and has accurate estimations on both the regression parameters and the baseline survival function. Several appealing properties of the model are discussed here. Chapter 3 proposes a more general multivariate probit model for multivariate interval-censored data. This new model allows arbitrary correlation among the correlated survival times. A new Gibbs sampler is proposed for the joint estimation of the regression parameters, the baseline CDF, and the correlation parameters. Chapter 4 extends the normal frailty multivariate probit model to allow arbitrary pairwise correlations. Simulation studies are conducted to explore the underlying relationship between the normal frailty multivariate probit model and the general multivariate probit model

Group lasso priors for Bayesian accelerated failure time models with left-truncated and interval-censored data

An important task in health research is to characterize time-to-event outcomes such as disease onset or mortality in terms of a potentially high-dimensional set of risk factors. For example, prospective cohort studies of Alzheimer's disease typically enroll older adults for observation over several decades to assess the long-term impact of genetic and other factors on cognitive decline and mortality. The accelerated failure time model is particularly well-suited to such studies, structuring covariate effects as `horizontal' changes to the survival quantiles that conceptually reflect shifts in the outcome distribution due to lifelong exposures. However, this modeling task is complicated by the enrollment of adults at differing ages, and intermittent followup visits leading to interval censored outcome information. Moreover, genetic and clinical risk factors are not only high-dimensional, but characterized by underlying grouping structure, such as by function or gene location. Such grouped high-dimensional covariates require shrinkage methods that directly acknowledge this structure to facilitate variable selection and estimation. In this paper, we address these considerations directly by proposing a Bayesian accelerated failure time model with a group-structured lasso penalty, designed for left-truncated and interval-censored time-to-event data. We develop a custom Markov chain Monte Carlo sampler for efficient estimation, and investigate the impact of various methods of penalty tuning and thresholding for variable selection. We present a simulation study examining the performance of this method relative to models with an ordinary lasso penalty, and apply the proposed method to identify groups of predictive genetic and clinical risk factors for Alzheimer's disease in the Religious Orders Study and Memory and Aging Project (ROSMAP) prospective cohort studies of AD and dementia

A Non-Blocking Priority Queue for the Pending Event Set

The large diffusion of shared-memory multi-core machines has impacted the way Parallel Discrete Event Simulation (PDES) engines are built. While they were originally conceived as data-partitioned platforms, where each thread is in charge of managing a subset of simulation objects, nowadays the trend is to shift towards share-everything settings. In this scenario, any thread can (in principle) take care of CPU-dispatching pending events bound to whichever simulation object, which helps to fully share the load across the available CPU-cores. Hence, a fundamental aspect to be tackled is to provide an efficient globally-shared pending events’ set from which multiple worker threads can concurrently extract events to be processed, and into which they can concurrently insert new produced events to be processed in the future. To cope with this aspect, we present the design and implementation of a concurrent non-blocking pending events’ set data structure, which can be seen as a variant of a classical calendar queue. Early experimental data collected with a synthetic stress test are reported, showing excellent scalability of our proposal on a machine equipped with 32 CPU-cores

GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP

Full detector simulation was among the largest CPU consumer in all CERN experiment software stacks for the first two runs of the Large Hadron Collider (LHC). In the early 2010's, the projections were that simulation demands would scale linearly with luminosity increase, compensated only partially by an increase of computing resources. The extension of fast simulation approaches to more use cases, covering a larger fraction of the simulation budget, is only part of the solution due to intrinsic precision limitations. The remainder corresponds to speeding-up the simulation software by several factors, which is out of reach using simple optimizations on the current code base. In this context, the GeantV R&D project was launched, aiming to redesign the legacy particle transport codes in order to make them benefit from fine-grained parallelism features such as vectorization, but also from increased code and data locality. This paper presents extensively the results and achievements of this R&D, as well as the conclusions and lessons learnt from the beta prototype.Comment: 34 pages, 26 figures, 24 table

kmos: A lattice kinetic Monte Carlo framework

Kinetic Monte Carlo (kMC) simulations have emerged as a key tool for microkinetic modeling in heterogeneous catalysis and other materials applications. Systems, where site-specificity of all elementary reactions allows a mapping onto a lattice of discrete active sites, can be addressed within the particularly efficient lattice kMC approach. To this end we describe the versatile kmos software package, which offers a most user-friendly implementation, execution, and evaluation of lattice kMC models of arbitrary complexity in one- to three-dimensional lattice systems, involving multiple active sites in periodic or aperiodic arrangements, as well as site-resolved pairwise and higher-order lateral interactions. Conceptually, kmos achieves a maximum runtime performance which is essentially independent of lattice size by generating code for the efficiency-determining local update of available events that is optimized for a defined kMC model. For this model definition and the control of all runtime and evaluation aspects kmos offers a high-level application programming interface. Usage proceeds interactively, via scripts, or a graphical user interface, which visualizes the model geometry, the lattice occupations and rates of selected elementary reactions, while allowing on-the-fly changes of simulation parameters. We demonstrate the performance and scaling of kmos with the application to kMC models for surface catalytic processes, where for given operation conditions (temperature and partial pressures of all reactants) central simulation outcomes are catalytic activity and selectivities, surface composition, and mechanistic insight into the occurrence of individual elementary processes in the reaction network.Comment: 21 pages, 12 figure

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

Design of robust service operations using cybernetic principles and simulation

Information flows in a service organisation allow business units to co-ordinate their response to changes in the operating environment. Processes and interactions can be designed so that the right information flows to the right people, at the right time to make effective decisions regarding job priorities and allocation of limited resource. This paper develops an analysis framework and simulation approach to identify the internal information flows an organisation needs to “tune itself” for changing conditions, thus making itself more robust to uncertainties. The ideas are developed and illustrated through a case study with a major telecoms company.International Design Conference - DESIGN 201