Multiple Event Data Research Articles

Detailed 3D Structures of the Western Edge of the Pacific Large Low Velocity Province

AbstractLarge Low Velocity Provinces (LLVPs) are situated oppositely in the lowermost mantle beneath the Pacific Ocean and Africa. Deciphering the detailed seismic structures at the edge of LLVPs can provide key information on the composition and dynamics in the deep Earth. Here, we provide a detailed seismic image at the western edge of the Pacific LLVP by dense recordings. Differential travel time residuals and amplitude ratios between ScS and S outline the S‐wave western boundary of the Pacific LLVP, suggesting the complex structures including low/high‐velocity patches in the lowermost mantle in our study region. We determine the 3D low‐velocity structure by modeling the delayed ScS and high‐velocity D″ layer structure by modeling the anomalous Scd, with tight constraints from multiple events data. The drastically varied waveforms in azimuth suggests a sharp transitional boundary among the complex structures. After comparing the velocity structures in adjacent regions, we propose that the 3D structures of the western edge of the Pacific LLVP are strongly influenced by the vigorous mantle flow associated with the actively subducted slab.

Beyond network centrality: individual-level behavioral traits for predicting information superspreaders in social media.

Understanding the heterogeneous role of individuals in large-scale information spreading is essential to manage online behavior as well as its potential offline consequences. To this end, most existing studies from diverse research domains focus on the disproportionate role played by highly connected 'hub' individuals. However, we demonstrate here that information superspreaders in online social media are best understood and predicted by simultaneously considering two individual-level behavioral traits: influence and susceptibility. Specifically, we derive a nonlinear network-based algorithm to quantify individuals' influence and susceptibility from multiple spreading event data. By applying the algorithm to large-scale data from Twitter and Weibo, we demonstrate that individuals' estimated influence and susceptibility scores enable predictions of future superspreaders above and beyond network centrality, and reveal new insights into the network positions of the superspreaders.

An MM Algorithm for the Frailty-Based Illness Death Model with Semi-Competing Risks Data

For analyzing multiple events data, the illness death model is often used to investigate the covariate–response association for its easy and direct interpretation as well as the flexibility to accommodate the within-subject dependence. The resulting estimation and inferential procedures often depend on the subjective specification of the parametric frailty distribution. For certain frailty distributions, the computation can be challenging as the estimation involves both the nonparametric component and the parametric component. In this paper, we develop efficient computational methods for analyzing semi-competing risks data in the illness death model with the general frailty, where the Minorization–Maximization (MM) principle is employed for yielding accurate estimation and inferential procedures. Simulation studies are conducted to assess the finite-sample performance of the proposed method. An application to a real data is also provided for illustration.

A NOVEL TRUE REAL-TIME SPATIOTEMPORAL DATA STREAM PROCESSING FRAMEWORK

The ability to interpret spatiotemporal data streams in real-time is critical for a range of systems. However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks, and other sources, is a considerable challenge. The major goal of this study is to create a framework for processing and analyzing spatiotemporal data from multiple sources with irregular shapes so that researchers can focus on data analysis instead of worrying about the data sources' structure. We introduced a novel spatiotemporal data paradigm for true-real-time stream processing, which enables high-speed and low-latency real-time data processing, with these considerations in mind. A comparison of two state-of-the-art real-time process architectures was offered, as well as a full review of the various open-source technologies for real-time data stream processing, and their system topologies were also presented. Hence, this study proposed a brand-new framework that integrates Apache Kafka for spatiotemporal data ingestion, Apache flink for true real-time processing of spatiotemporal stream data, as well as machine learning for real-time predictions, and Apache Cassandra at the storage layer for distributed storage in real-time. The proposed framework was compared with others from the literature using the following features: Scalability (Sc), prediction tools (PT), data analytics (DA), multiple event types (MET), data storage (DS), Real-time (Rt), and performance evaluation (PE) stream processing (SP), and our proposed framework provided the ability to handle all of this task.

Calibrating synchronous-generator-interfaced DG models in microgrids using multiple event data

Microgrids, consisting of distributed generators (DGs), loads, and energy storage systems, play an important role in future smart grids. In order to evaluate microgrid operations and their dynamic response, it is necessary to develop accurate dynamic models of DGs in microgrids. This paper presents a systematic method to calibrate the model of a synchronous-generator-interfaced DG. Underlying parameters are categorized into two groups, namely, steady-state parameters and time constants, which are estimated in two successive stages using multiple event data. This methodology ensures that the validated model with the estimated parameters are applicable for a set of events. Moreover, assuming that power angle measurements of a synchronous-generator-interfaced DG are unavailable, a modified unscented Kalman filter (UKF) is proposed to facilitate parameter estimation. The effectiveness of the proposed method is validated using field test data of a 2600 kVA diesel generator which is part of an operational microgrid.

Integrating Conflict Event Data

The growing multitude of sophisticated event-level data collection enables novel analyses of conflict. Even when multiple event data sets are available, researchers tend to rely on only one. We instead advocate integrating information from multiple event data sets. The advantages include facilitating analysis of relationships between different types of conflict, providing more comprehensive empirical measurement, and evaluating the relative coverage and quality of data sets. Existing integration efforts have been performed manually, with significant limitations. Therefore, we introduce Matching Event Data by Location, Time and Type (MELTT)—an automated, transparent, reproducible methodology for integrating event data sets. For the cases of Nigeria 2011, South Sudan 2015, and Libya 2014, we show that using MELTT to integrate data from four leading conflict event data sets (Uppsala Conflict Data Project–Georeferenced Event Data, Armed Conflict Location and Event Data, Social Conflict Analysis Database, and Global Terrorism Database) provides a more complete picture of conflict. We also apply multiple systems estimation to show that each of these data sets has substantial missingness in coverage.

Empirical Susceptibility, Vulnerability and Risk Analysis for Resilience Enhancement of Urban Areas to Terrorist Events

The worldwide progressing urbanization increases the need to consider threats like terrorism or natural disasters when planning or redesigning urban areas. The paper shows how to assess and mitigate terrorism risks in a systematic way using historical event data on urban plan level. Based on multiple event data, within a risk approach, quantities are derived that measure averaged susceptibilities, vulnerabilities and risks for buildings and infrastructure in the context of urban planning. The empirical approach allows for local scaling factors for frequency of events, e.g., due to physical accessibility, and for consequences, e.g., due to physical counter-measures. The paper discusses the input data necessary for statistically sound analyses, in particular, the accuracies of the quantities as well as deep uncertainties. To this end, it determines distributions for the frequencies and consequences, in particular, introducing a modified Pareto distribution. The application to a sample urban area demonstrates how the analysis results lead to a more resilient scenario using the analysis and selecting resilience improvement options. It is shown that the empirical analysis motivates more detailed additional engineering-simulative approaches for resilience improvement through averaging susceptibility, vulnerability and risk assessments by assuming multiple spatially distributed events.

A class of accelerated means regression models for multiple type recurrent event data

Recurrent events are frequently observed in biomedical studies, and often more than one type of event is of interest. In this paper, we first propose a general class of accelerated means regression models for multiple type recurrent event data. We then formulate estimating equations for the model parameters, and finally examine asymptotic properties of the parameter estimators.

Effects of Hurricanes Irene and Sandy in New Jersey: traffic patterns and highway disruptions during evacuations

This paper describes a quantitative analysis of traffic patterns and highway disruptions during Hurricane Irene and Sandy evacuations in New Jersey (NJ). This empirical study is based on multiple traffic and event data collected by various transportation agencies in NJ. In the first part of the paper, the temporal and spatial traffic patterns in NJ during Irene and Sandy evacuations were explored, and a comparative assessment of evacuation departure models was conducted based on the empirical traffic data. In the second part, we explored the frequency and geographic distribution of highway disruptions (vehicle accidents/incidents, incidents such as downed trees or road flooding caused by extreme winds and heavy rains, and highway bottlenecks) during Irene and Sandy evacuations and pre-landfall periods. The empirical patterns observed in this study can be used to improve real-world emergency response operations and evacuation models. The empirical findings may also benefit hurricane evacuation planning in areas with similar circumstances as NJ.

Non‐parametric Analysis of Gap Times for Multiple Event Data: An Overview

SummaryMultiple event data are frequently encountered in medical follow‐up, engineering and other applications when the multiple events are considered as the major outcomes. They may be repetitions of the same event (recurrent events) or may be events of different nature. Times between successive events (gap times) are often of direct interest in these applications. The stochastic‐ordering structure and within‐subject dependence of multiple events generate statistical challenges for analysing such data, including induced dependent censoring and non‐identifiability of marginal distributions. This paper provides an overview of a class of existing non‐parametric estimation methods for gap time distributions for various types of multiple event data, where sampling bias from induced dependent censoring is effectively adjusted. We discuss the statistical issues in gap time analysis, describe the estimation procedures and illustrate the methods with a comparative simulation study and a real application to an AIDS clinical trial. A comprehensive understanding of challenges and available methods for non‐parametric analysis can be useful because there is no existing standard approach to identifying an appropriate gap time method that can be used to address research question of interest. The methods discussed in this review would allow practitioners to effectively handle a variety of real‐world multiple event data.

Empirical comparison of methods for analyzing multiple time-to-event outcomes in a non-inferiority trial: a breast cancer study

BackgroundSubjects with breast cancer enrolled in trials may experience multiple events such as local recurrence, distant recurrence or death. These events are not independent; the occurrence of one may increase the risk of another, or prevent another from occurring. The most commonly used Cox proportional hazards (Cox-PH) model ignores the relationships between events, resulting in a potential impact on the treatment effect and conclusions. The use of statistical methods to analyze multiple time-to-event events has mainly been focused on superiority trials. However, their application to non-inferiority trials is limited. We evaluate four statistical methods for multiple time-to-event endpoints in the context of a non-inferiority trial.MethodsThree methods for analyzing multiple events data, namely, i) the competing risks (CR) model, ii) the marginal model, and iii) the frailty model were compared with the Cox-PH model using data from a previously-reported non-inferiority trial comparing hypofractionated radiotherapy with conventional radiotherapy for the prevention of local recurrence in patients with early stage breast cancer who had undergone breast conserving surgery. These methods were also compared using two simulated examples, scenario A where the hazards for distant recurrence and death were higher in the control group, and scenario B. where the hazards of distant recurrence and death were higher in the experimental group. Both scenarios were designed to have a non-inferiority margin of 1.50.ResultsIn the breast cancer trial, the methods produced primary outcome results similar to those using the Cox-PH model: namely, a local recurrence hazard ratio (HR) of 0.95 and a 95% confidence interval (CI) of 0.62 to 1.46. In Scenario A, non-inferiority was observed with the Cox-PH model (HR = 1.04; CI of 0.80 to 1.35), but not with the CR model (HR = 1.37; CI of 1.06 to 1.79), and the average marginal and frailty model showed a positive effect of the experimental treatment. The results in Scenario A contrasted with Scenario B with non-inferiority being observed with the CR model (HR = 1.10; CI of 0.87 to 1.39), but not with the Cox-PH model (HR = 1.46; CI of 1.15 to 1.85), and the marginal and frailty model showed a negative effect of the experimental treatment.ConclusionWhen subjects are at risk for multiple events in non-inferiority trials, researchers need to consider using the CR, marginal and frailty models in addition to the Cox-PH model in order to provide additional information in describing the disease process and to assess the robustness of the results. In the presence of competing risks, the Cox-PH model is appropriate for investigating the biologic effect of treatment, whereas the CR models yields the actual effect of treatment in the study.

Empirical likelihood method for the multivariate accelerated failure time models

In applications, multivariate failure time data appears when each study subject may potentially experience several types of failures or recurrences of a certain phenomenon, or failure times may be clustered. Three types of marginal accelerated failure time models dealing with multiple events data, recurrent events data and clustered events data are considered. We propose a unified empirical likelihood inferential procedure for the three types of models based on rank estimation method. The resulting log-empirical likelihood ratios are shown to possess chi-squared limiting distributions. The properties can be applied to do tests and construct confidence regions without the need to solve the rank estimating equations nor to estimate the limiting variance–covariance matrices. The related computation is easy to implement. The proposed method is illustrated by extensive simulation studies and a real example.

Marginal Regression Analysis for Semi‐Competing Risks Data Under Dependent Censoring

Abstract. Multiple events data are commonly seen in medical applications. There are two types of events, namely terminal and non‐terminal. Statistical analysis for non‐terminal events is complicated due to dependent censoring. Consequently, joint modelling and inference are often needed to avoid the problem of non‐identifiability. This article considers regression analysis for multiple events data with major interest in a non‐terminal event such as disease progression. We generalize the technique of artificial censoring, which is a popular way to handle dependent censoring, under flexible model assumptions on the two types of events. The proposed method is applied to analyse a data set of bone marrow transplantation.

High energy emission and polarisation limits for the INTEGRAL burst GRB 061122

(Abridged) GRB 061122 is one of the brightest GRBs detected within INTEGRAL's field of view to date. The two gamma-ray detectors on INTEGRAL were used to investigate the spectral characteristics of GRB 061122. A search for linear polarisation in the prompt emission was carried out using the SPI multiple event data in the energy range 100 keV-1 MeV. The prompt spectrum was best fit by a combination of a blackbody and a power--law model (the quasithermal model), with evidence for high energy emission continuing above 8 MeV. A pseudo-redshift value of pz = 0.95 +/- 0.18 was determined using the spectral fit parameters. The isotropic energy at this pseudo-redshift is 8.5 x 10^{52} erg. The jet opening angle was estimated to be smaller than 2.8 deg or larger than 11.9 deg from the X-ray lightcurve. An upper limit of 60% polarisation was determined for the prompt emission of GRB 061122, using the multiple event data. The high energy emission observed in the spectrum may be due to the reverse shock interacting with the GRB ejecta when it is decelerated by the circumburst medium. This behaviour has been observed in a small fraction of GRBs to date, but is expected to be more commonly observed by the Fermi Gamma-ray Space Telescope. The conditions for polarisation are met if the jet opening angle is less than 2.8 deg, but further constraints on the level of polarisation are not possible.

Regression splines in the time-dependent coefficient rates model for recurrent event data.

Many epidemiologic studies involve the occurrence of recurrent events and much attention has been given for the development of modeling techniques that take into account the dependence structure of multiple event data. This paper presents a time-dependent coefficient rates model that incorporates regression splines in its estimation procedure. Such methods would be appropriate in situations where the effect of an exposure or covariates changes over time in recurrent event data settings. The finite sample properties of the estimators are studied via simulation. Using data from a randomized community trial that was designed to evaluate the effect of vitamin A supplementation on recurrent diarrheal episodes in small children, we model the functional form of the treatment effect on the time to the occurrence of diarrhea. The results describe how this effect varies over time. In summary, we observed a major impact of the vitamin A supplementation on diarrhea after 2 months of the dosage, with the effect diminishing after the third dosage. The proposed method can be viewed as a flexible alternative to the marginal rates model with constant effect in situations where the effect of interest may vary over time.

Polarisation studies of the prompt gamma-ray emission from GRB 041219a using the spectrometer aboard INTEGRAL

Context. Linear polarisation in gamma-ray burst prompt emission is an important diagnostic with the potential to significantly constrain models. The spectrometer aboard INTEGRAL , SPI, has the capability to detect the signature of polarised emission from a bright γ -ray source. GRB 041219a is the most intense burst localised by INTEGRAL with a fluence of 5.7 $\times 10^{-4}$ erg cm -2 over the energy range 20 keV–8 MeV and is an ideal candidate for such a study. Aims. Polarisation can be measured using multiple events scattered into adjacent detectors because the Compton scatter angle depends on the polarisation of the incoming photon. A search for linear polarisation in the most intense pulse of duration 66 seconds and in the brightest 12 seconds of GRB 041219a was performed in the 100–350 keV, 100–500 keV and 100 keV–1 MeV energy ranges. It was possible to divide the events into six directions in the energy ranges of 100–350 keV and 100–500 keV using the kinematics of the Compton scatter interactions. Methods. The multiple event data from the spectrometer was analysed and compared with the predicted instrument response obtained from Monte-Carlo simulations using the GEANT 4 INTEGRAL mass model. The $\chi^2$ distribution between the real and simulated data as a function of the percentage polarisation and polarisation angle was calculated for all three energy ranges. The degree and angle of polarisation were obtained from the best-fit value of $\chi^2$. Results. A weak signal consistent with polarisation was found throughout the analyses. The degree of linear polarisation in the brightest pulse of duration 66 s was found to be $63^{+31}_{-30}$% at an angle of $70^{+14}_{-11}$ degrees in the 100–350 keV energy range. The degree of polarisation was also constrained in the brightest 12 s of the GRB and a polarisation fraction of $96^{+39}_{-40}$% at an angle of $60^{+12}_{-14}$ degrees was determined over the same energy range. However, despite extensive analysis and simulations, a systematic effect that could mimic the weak polarisation signal could not be definitively excluded. Conclusions. Our results over several energy ranges and time intervals are consistent with a polarisation signal of about 60% but at a low level of significance (~$2\sigma$). The polarisation results are compared with predictions from the synchrotron and Compton drag processes. The spectrum of this GRB can also be well fit by a combined black body and power law model which could arise from a combination of the Compton and synchrotron processes, with different degrees of polarisation. We therefore conclude that the procedure described here demonstrates the effectiveness of using SPI as a polarimeter, and is a viable method of measuring polarisation levels in intense gamma-ray bursts.

Analyzing Plasma HIV-1 RNA Measurements as Multiple Recurrent Events in Clinical Trials

Primary endpoints based on levels of HIV-1 RNA are currently used to evaluate efficacy of antiretroviral therapy in AIDS/HIV clinical trials. These endpoints, however, are generally based on few HIV-1 RNA measurements. More specifically, when the endpoint is the time to virologic failure (usually defined as two consecutive HIV-1 RNA levels above a threshold value), viral load measurements recorded after failure are simply ignored in the analysis. Instead of one virologic failure used in a classical survival analysis, we suggest that a failure be defined as any plasma HIV-1 RNA level above a predefined threshold value. This then becomes an analysis of multiple events of the same type, that is, recurrent virologic failures. Many survival models based on the Cox proportional hazards model have been proposed to handle multiple event data. The two distinct classes of models (marginal and conditional) answer two distinct types of clinical question and should be considered as complementary rather than competing models. We applied the different models to data from a clinical trial in HIV-1-infected patients.

Marginal Regression of Multivariate Event Times Based on Linear Transformation Models

Multivariate event time data are common in medical studies and have received much attention recently. In such data, each study subject may potentially experience several types of events or recurrences of the same type of event, or event times may be clustered. Marginal distributions are specified for the multivariate event times in multiple events and clustered events data, and for the gap times in recurrent events data, using the semiparametric linear transformation models while leaving the dependence structures for related events unspecified. We propose several estimating equations for simultaneous estimation of the regression parameters and the transformation function. It is shown that the resulting regression estimators are asymptotically normal, with variance-covariance matrix that has a closed form and can be consistently estimated by the usual plug-in method. Simulation studies show that the proposed approach is appropriate for practical use. An application to the well-known bladder cancer tumor recurrences data is also given to illustrate the methodology.

Factors associated with readmission to a general hospital in Brazil

The objective of this study was to compare different modeling strategies to identify individual and admissions characteristics associated with readmission to a general hospital. Routine data recorded in the Hospital Information System on all admissions to the Regional Public Hospital of Betim, Minas Gerais State, Brazil, from July 1996 to June 2000 were analyzed. Cox proportional hazards model and variants designed to deal with multiple-events data, like Andersen-Gill (AG), Prentice, Williams and Peterson (PWP), and random effects models were fitted to time between hospital admissions or censoring. For comparison purposes, a Poisson model was fitted to the total number of readmissions, using the same covariates. We analyzed 31,648 admissions of 26,198 patients, including 17,096 adults and 9,102 children. Estimates for the PWP and frailty models were very similar, and both approaches should be fitted and compared. If clinical characteristics are available, the PWP model should be used. Otherwise the random effects model can account for unmeasured differences, particularly some related to severity of the disease. These methodologies can help focus on various related readmission aspects such as diagnostic groups or medical specialties.

Data Mapping and the Prediction of Common Cause Failure Probability

General failure event data from various sources are often used to estimate the failure probability for the system of interest, especially when s-dependence exists among component failures, where common cause failure plays an important role. Failure event data from different sources must be reasonably explained, and correctly applied, so that the information about load environment, and component/system property can be used correctly. In estimating the probability for s-dependent system failure, both the load distribution, and component strength distribution are much more important than component failure probability index. Based on the relationship among different multiple failures, this paper presents a data mapping approach to estimating dependent system failure probability through multiple failure event data of other systems with different sizes. The underlying assumption on data mapping is that failures of different multiples (including single) are correlated with each other for a group of components if they are subjected to the same or correlated random load (loads). Taking the situation of a group of s-independent components operating under the same random load as an example, the likelihood of a component failure at a trial depends not only on the strength of the individual component but also on the realization of the random load. The likelihood of a specific multiple failure at a trial is also determined by both the component strengths, and the realization of the random load. Furthermore, if a larger load sample appears, the likelihoods for failure are higher. Conversely, if a smaller load sample appears, the likelihoods of failure are lower. We emphasized in this paper that system failure event data should be interpreted & applied under the principle that various multiple failures are distinguished by their respective failure multiplicity and/or system size, and are inherently interrelated through correlated load environments. The approach starts with determining the load parameter, and component strength parameter according to multiple (including single) failure event data available. Then, these parameters are used to calculate the probability of multiple failures for systems of different sizes. This approach is applicable to predict high multiple failure probability based on low multiple failure event data. Examples of estimating multiple failure probabilities of EDG (emergency diesel generators) with mapped data illustrate that the proposed approach is desirable.