markovMSM: An R Package for Checking the Markov Condition in Multi-State Survival Data

Multi-state models can be used to describe processes in which an individual moves through a finite number of states in continuous time. These models allow a detailed view of the evolution or recovery of the process and can be used to study the effect of a vector of explanatory variables on the transition intensities or to obtain prediction probabilities of future events after a given event history. In both cases, before using these models, we have to evaluate whether the Markov assumption is tenable. This paper introduces the markovMSM package, a software application for R, which considers tests of the Markov assumption that are applicable to general multi-state models. Three approaches using existing methodology are considered: a simple method based on including covariates depending on the history; methods based on measuring the discrepancy of the non-Markov estimators of the transition probabilities to the Markovian Aalen-Johansen estimators; and, finally, methods that were developed by considering summaries from families of log-rank statistics where individuals are grouped by the state occupied by the process at a particular time point. The main functionalities of the markovMSM package are illustrated using real data examples.This research was financed by Portuguese Funds through FCT - "Fundação para a Ciência e a Tecnologia", within Projects projects UIDB/00013/2020, UIDP/00013/2020 and the research grant PD/BD/142887/2018

Rocket measurements of upper atmospheric nitric oxide and their consequences to the lower ionosphere

Rocket measurements of nitric oxide density profile in upper atmosphere with scanning ultraviolet spectrometers aboard Nike-Apache rocket vehicl

Controle químico da Mancha de Ramulária do algodoeiro, causada por Ramulária areola, em função da idade da planta e da severidade da doença - safra 2003-2004.

bitstream/CNPA/19718/1/BOLETIM58.pd

Analysis of Survival Data with Multiple Events

An important aim in biomedical studies is to study how an intermediate event and prognostic factors influence the course of disease of a patient. In most cases, the effect of the intermediate event is considered a timedependent covariate and studied using extensions of the Cox proportional hazards model. Additionally, many of these studies often involve several endpoints, making the traditional approaches much more complicated. In such cases, multi-state models provide a useful tool to describe the survival process. This article aims to illustrate how multi-state models can be used as an alternative to traditional approaches. It also aims to offer guidelines for the correct use of these approaches through the analysis of survival data of patients with breast cancer. Several analyses were performed, and methods to evaluate the effect of covariates on transition intensities and to test some usual assumptions are discussed. Tree-based survival models, like the Cox proportional hazards models, are popular methods for constructing a prediction model in the field of medical research. We also present the results obtained by applying some tree-based models to the breast cancer data while showing their interpretation and utility. An overview of available software and software developed by the authors is provided to aid researchers in choosing an appropriate software tool for their purposes. © 2022 World Scientific and Engineering Academy and Society. All rights reserved.Acknowledgements: This work received financial support from the Portuguese Foundation for Science and Technology (“Fundação para a Ciên-cia e a Tecnologia”), references UIDB/00013/2020, UIDP/00013/2020 and EXPL/MAT-STA/0956/2021

Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model

One important goal in multi-state modeling is the estimation of transition probabilities. In longitudinal medical studies these quantities are particularly of interest since they allow for long-term predictions of the process. In recent years signi ficant contributions have been made regarding this topic. However, most of the approaches assume independent censoring and do not account for the influence of covariates. The goal of the paper is to introduce feasible estimation methods for the transition probabilities in an illness-death model conditionally on current or past covariate measures. All approaches are evaluated through a simulation study, leading to a comparison of two di erent estimators. The proposed methods are illustrated using real a colon cancer data set.This research was nanced by FEDER Funds through Programa Operacional Factores de Competitividade COMPETE and by Portuguese Funds through FCT - Funda ção para a Cência e a Tecnologia, within Projects Est-C/MAT/UI0013/2011 and PTDC/MAT/104879/2008. We also acknowledge nancial support from the project Grants MTM2008-03129 and MTM2011-23204 (FEDER support included) of the Spanish Ministerio de Ciencia e Innovaci on and 10PXIB300068PR of the Xunta de Galicia. Partial support from a grant from the US National Security Agency (H98230-11-1-0168) is greatly appreciated

Reconfiguration and regulation of supply chains and HRM in times of economic crisis

This chapter reviews existing evidence on the reconfiguration and regulation of supply chains and employment relations during times of economic crisis. On the one hand, literature has highlighted pressures towards a degradation of standards as firms seek short-term cost advantages. This view is informed by various perspectives from organization studies to political economy but is united by the idea that structural changes in global capitalism drive how firms relate to their suppliers. On the other hand, it has been argued that counter-pressures range from consumer backlashes to the extension of formal and informal regulation across national boundaries. This undeniably heterogeneous literature has common themes suggesting that global forces may be mediated by existing embedded institutional arrangements at transnational, national and local level, and that there are still open-ended possibilities for social action. This chapter synthesizes and evaluates these two streams and identifies agendas for future research

New recycling approaches for thermoset polymeric composite wastes – an experimental study on polyester based concrete materials filled with fibre reinforced plastic recyclates

In this study, a new waste management solution for thermoset glass fibre reinforced polymer (GFRP) based products was assessed. Mechanical recycling approach, with reduction of GFRP waste to powdered and fibrous materials was applied, and the prospective added-value of obtained recyclates was experimentally investigated as raw material for polyester based mortars. Different GFRP waste admixed mortar formulations were analyzed varying the content, between 4% up to 12% in weight, of GFRP powder and fibre mix waste. The effect of incorporation of a silane coupling agent was also assessed. Design of experiments and data treatment was accomplished through implementation of full factorial design and analysis of variance ANOVA. Added value of potential recycling solution was assessed by means of flexural and compressive loading capacity of GFRP waste admixed mortars with regard to unmodified polymer mortars. The key findings of this study showed a viable technological option for improving the quality of polyester based mortars and highlight a potential cost-effective waste management solution for thermoset composite materials in the production of sustainable concrete-polymer based products