A stochastic joint model for longitudinal and survival data with cure patients

Many medical investigations generate both repeatedly-measured (longitudinal) biomarker and survival data. One of complex issues arises when investigating the association between longitudinal and time-to-event data when there are cured patients in the population, which leads to a plateau in the survival function S(t) after sufficient follow-up. Thus, usual Cox proportional hazard model Cox (1972) is not applicable since the proportional hazard assumption is violated. An alternative is to consider survival models incorporating a cure fraction. In this paper we present a new class of joint model for univariate longitudinal and survival data in presence of cure fraction. For the longitudinal model, a stochastic Integrated Ornstein-Uhlenbeck process will be presented. For the survival model a semiparametric survival function will be considered which accommodate both zero and non-zero cure fractions of the dynamic disease progression. Moreover, we consider a Bayesian approach which is motivated by the complexity of the model. Posterior and prior specification needs to accommodate parameter constraints due to the nonnegativity of the survival function. A simulation study is presented to evaluate the performance of this joint model

Bayesian approach for joint longitudinal and time-to-event data with survival fraction

Many medical investigations generate both repeatedly-measured(longitudinal) biomarker and survival data. One of complex issue arises when investigating the association between longitudinal and time-to-event data when there are cured patients in the population, which leads to a plateau in the survival function S(t) after sufficient follow-up. Thus, usual Cox proportional hazard model [11] is not applicable since the proportional hazard assumption is violated. An alternative is to consider survival models incorporating a cure fraction. In this paper, we present a new class of joint model for univariate longitudinal and survival data in presence of cure fraction. For the longitudinal model, a stochastic Integrated Ornstein-Uhlenbeck process will present, and for the survival model a semiparametric survival function will be considered which accommodate both zero and non-zero cure fractions of the dynamic disease progression. Moreover, we consider a Bayesian approach which is motivated by the complexity of the model. Posterior and prior specification needs to accommodate parameter constraints due to the non-negativity of the survival function. A simulation study is presented to evaluate the performance of the proposed joint model

Cure fraction, modelling and estimating in a population-based cancer survival analysis

In population-based cancer studies, cure is said to occur when the mortality (hazard)rate in the diseased group of individuals returns to the same level as that expected in the general population. The optimal method for monitoring the progress of patient care across the full spectrum of provider settings is through the population-based study of cancer patient survival, which is only possible using data collected by population-based cancer registries. The probability of cure, statistical cure, is defined for a cohort of cancer patients as the percent of patients whose annual death rate equals the death rate of general cancer-free population. Recently models have been introduced, so called cure fraction models, that estimates the cure fraction as well as the survival time distribution for those uncured. The colorectal cancer survival data from the Surveillance, Epidemiology and End Results (SEER) program, USA, is used. The aim is to evaluate the cure fraction models and compare these methods to other methods used to monitor time trends in cancer patient survival, and to highlight some problems using these models

A Semiparametric Joint Model for Longitudinal and Time-to- Event Univariate Data in Presence of Cure Fraction

Many medical investigations generate both repeatedly-measured (longitudinal)biomarker and survival data. One of complex issue arises when investigating the association between longitudinal and time-to-event data when there are cured patients in the population, which leads to a plateau in the survival function S(t) after sufficient follow-up. Thus, usual Cox proportional hazard model Cox (1972) is not applicable since the proportional hazard assumption is violated. An alternative is to consider survival models incorporating a cure fraction. In this paper we present a new class of joint model for univariate longitudinal and survival data in presence of cure fraction. For the longitudinal model, a stochastic Integrated Ornstein-Uhlenbeck process will present, and for the survival model a semiparametric survival function will be considered which accommodate both zero and non-zero cure fractions of the dynamic disease progression. Moreover, we consider a Bayesian approach which is motivated by the complexity of the model. Posterior and prior specification needs to accommodate parameter constraints due to the nonnegativity of the survival function. A simulation study is presented to evaluate the performance of this joint model

A study of experience sharing in community engagement

Community engagement is crucial in building positive relationships within the members of a community.Community gardening can be a means of community engagement through providing opportunities for social interaction and greater community cohesions.Community engagement will take place when the community gardeners contribute something which is beneficial and visible to the communities. In creating successful community engagement, there is a need of experience sharing among community gardeners.Community gardening can be a means of providing opportunities for social interaction, greater community cohesion and community engagement.This paper aims to look into the real practices of experience sharing for community engagement in the context of community gardening in Malaysia.A comparative study was conducted by looking into two types of community. The finding shows the experience sharing helps the community to be engaged.In fact, community engaged more with the advancement of information technology

Natphoric Kansei Engineering Systems (N-KES)

Over recent years, consumers have become sophisticated and desired product that meets their desired emotional connectivity, which stands out in their eyes among the abundance choices they have from the market. Kansei Engineering (KE) has been widely attracting researchers’ attention as a technology that enables incorporation of emotion in product design. KE makes it possible to discover relationships between product design and the resulted emotional responses. Among the process in KE implementation, KE analysis is the most crucial part, and it requires a Kansei expert intervention to determine the optimum result. However, there are not many accessible Kansei experts and there is no specific rule of thumb in this process. This research proposed a web-based system comprising of Natphoric algorithm in its engine to simulate the expert intervention in a computer-aided KE implementation. The algorithm is used to imitate the expert activity to find a significant factor of emotion. It learns the process by training with datasets from previous KE research works. The Natphoric algorithm is formulated to handle the complexity in KE, and the N-KES adopted the use of one of the technique in KE Type 1 to automate the Kansei conceptualization process which requires expertise in KE. The developed N-KES has produced similar result with the manual KE implementation. This research outcome will benefit the producers and designers advocates to easily implement KE Type 1, to enable development of new and innovative product design

VR welding kit: welding training simulation in mobile virtual reality using multiple marker tracking method

Welding simulation design using virtual reality (VR) is a challenge, as numerous developments and research in the mechanical engineering fields are involved. One of the key challenges is the improvement of realism by considering a mixed system of real and virtual equipment. A conceptual design and research management framework is currently lacking which leveraging the combination of VR and marker tracking techniques. This study seeks to examine and evaluating the use of mobile VR in welding training and how multiple markers tracking methods can be incorporated to overcome the current problems in VR for welding training simulation. In this study, the VR Welding Kit application is created by utilizing the Vuforia tracking engine to provide an alternative interaction for mobile devices. The results of the experiment revealed a benchmark comparison with Oculus Quest, the high-end VR system, to investigate the efficiency of the proposed multiple marker interaction technique. Performance for both devices was recorded. The System Usability Scales (SUS) have also been used to obtain users' acceptance rates using these devices. The Simulator Sickness Questionnaire (SSQ) was used to assess the cybersickness of participants. The performance results show that mobile VR have a moderate gap completion time in seconds if compared to Oculus Quest. The SUS scored a satisfactory result which is 73.33. Besides, SSQ surveys result shows that most of the participant felt the simulation sickness was minimal

Waste mineral wool and its opportunities—a review

Massive waste rock wool was generated globally and it caused substantial environmental issues such as landfill and leaching. However, reviews on the recyclability of waste rock wool are scarce. Therefore, this study presents an in-depth review of the characterization and potential usability of waste rock wool. Waste rock wool can be characterized based on its physical properties, chemical composition, and types of contaminants. The review showed that waste rock wool from the manufacturing process is more workable to be recycled for further application than the postconsumer due to its high purity. It also revealed that the pre-treatment method—comminution is vital for achieving mixture homogeneity and enhancing the properties of recycled products. The potential application of waste rock wool is reviewed with key results emphasized to demonstrate the practicality and commercial viability of each option. With a high content of chemically inert compounds such as silicon dioxide (SiO2), calcium oxide (CaO), and aluminum oxide (Al2O3) that improve fire resistance properties, waste rock wool is mainly repurposed as fillers in composite material for construction and building materials. Furthermore, waste rock wool is potentially utilized as an oil, water pollutant, and gas absorbent. To sum up, waste rock wool could be feasibly recycled as a composite material enhancer and utilized as an absorbent for a greener environment