Analyzing Bivariate Survival Data with Interval Sampling and Application to Cancer Epidemiology

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To analyze this type of bivariate failure time data, it is important to recognize the presence of bias arising due to interval sampling. In this paper, nonparametric and semiparametric methods are developed to analyze the bivariate survival data with interval sampling under stationary and semi-stationary conditions. Numerical studies demonstrate the proposed estimating approaches perform well with practical sample sizes in different simulated models. We apply the proposed methods to SEER ovarian cancer registry data for illustration of the methods and theory

Complex Agent Networks explaining the HIV epidemic among homosexual men in Amsterdam

Simulating the evolution of the Human Immunodeficiency Virus (HIV) epidemic requires a detailed description of the population network, especially for small populations in which individuals can be represented in detail and accuracy. In this paper, we introduce the concept of a Complex Agent Network(CAN) to model the HIV epidemics by combining agent-based modelling and complex networks, in which agents represent individuals that have sexual interactions. The applicability of CANs is demonstrated by constructing and executing a detailed HIV epidemic model for men who have sex with men (MSM) in Amsterdam, including a distinction between steady and casual relationships. We focus on MSM contacts because they play an important role in HIV epidemics and have been tracked in Amsterdam for a long time. Our experiments show good correspondence between the historical data of the Amsterdam cohort and the simulation results.Comment: 21 pages, 4 figures, Mathematics and Computers in Simulation, added reference

Hybrid simulation of structural systems with online updating of concrete constitutive law parameters by unscented Kalman filter

Summary Online model updating in hybrid simulation (HS) can represent an effective technique to reduce modeling errors of parts numerically simulated, that is, numerical substructures, especially when only a few critical components of a large system can be tested, that is, physical substructures. As a result, in an enhanced HS with online model updating, parameters of constitutive relationship can be identified based on experimental data provided by physical substructures and updated in numerical substructures. This paper proposes a novel method to identify constitutive parameters of concrete laws with unscented Kalman filter (UKF). In order to implement UKF, parts of the source codes of the OpenSEES software were modified to compute estimated measurements. Prior to experimental HS, a parametric study of UKF constitutive law parameters was conducted. As a result, the effectiveness of the UKF combined with OpenSEES was validated through numerical simulations, a monotonic loading test on a concrete column and real-time HSs of a reinforced concrete frame run with both standard and model-updating techniques based on UKF