520 research outputs found
Smoothed landmark estimators of the transition probabilities
One important goal in clinical applications of multi-state models is the estimation of transition probabilities. Recently, landmark estimators were proposed to estimate these quantities, and their superiority with respect to the competing estimators has been proved in situations in which the Markov condition is violated. As a weakness, it provides large standard errors in estimation in some circumstances. In this article, we propose two approaches that can be used to reduce the variability of the proposed estimator. Simulations show that the proposed estimators may be much more efficient than the unsmoothed estimator. A real data illustration is included
Presmoothed Landmark estimators of the transition probabilities
Multi-state models can be successfully used to model complicated event history data, for example, describing stages in the disease progression of a patient. In these models one important goal is the estimation of the transition probabilities since they allow for long term prediction of the process. There have been several recent contributions for the estimation of the transition probabilities. Recently, de Uña- Ălvarez and Meira-Machado (2015) proposed new estimators for these quantities, and their superiority with respect to the competing estimators has been proved in situations in which the Markov condition is violated. In this paper, we propose a modification of the estimator proposed by de Uña-Ălvarez and Meira-Machado based on presmoothing. Simulations show that the presmoothed estimators may be much more efficient than the completely nonparametric estimator.This project was funded by FEDER Funds through âPrograma Operacional Factores de Competitividade - COMPETEâ and by Portuguese Funds through FCT - âFundação para a CiĂȘncia e a Tecnologiaâ, in the form of grant PEst-OE/MAT/UI0013/2014.info:eu-repo/semantics/publishedVersio
p3state.msm: Analyzing Survival Data from an Illness-Death Model
In longitudinal studies of disease, patients can experience several events across a followup period. Analysis of such studies can be successfully performed by multi-state models. In the multi-state framework, issues of interest include the study of the relationship between covariates and disease evolution, estimation of transition probabilities, and survival rates. This paper introduces p3state.msm, a software application for R which performs inference in an illness-death model. It describes the capabilities of the program for estimating semi-parametric regression models and for implementing nonparametric estimators for several quantities. The main feature of the package is its ability for obtaining nonMarkov estimates for the transition probabilities. Moreover, the methods can also be used in progressive three-state models. In such a model, estimators for other quantities, such as the bivariate distribution function (for sequentially ordered events), are also given. The software is illustrated using data from the Stanford Heart Transplant Study.
Essays on the impacts of credit ratings
This thesis studies the impacts of credit ratings in several dimensions. The firs paper studies the impact of the sovereign ceiling policies in the Municipal Bond markets. Firms at the sovereign ceiling are relatively more affected following a sovereign downgrade. The second paper quantifies the impact of stringent rating criteria from credit rating agencies in investment grade and speculative grade firms. The former is significantly more affected by changes in criteria. The third paper access the impact in firmâs leverage, debt issuance and cash holdings from past changes in credit ratings. Unpredicted changes in credit ratings drive most firmâs reactions
Estimation of multivariate distributions for recurrent event data
In many longitudinal studies information is collected on the times of
different kinds of events. Some of these studies involve repeated events, where a
subject or sample unit may experience a well-defined event several times along
his history. Such events are called recurrent events. In this work we consider
the estimation of the marginal and joint distribution functions of two gap times
under univariate random right censoring. We also consider the estimation of the
bivariate survival function.This research was financed by Portuguese Funds through
FCT - âFundažcËao para a CiËencia e a Tecnologiaâ, within Project
UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio
Methods for checking the Markov condition in multi-state survival data
The inference in multi-state models is traditionally performed under a Markov assumption that claims that past and future of the process are independent given the present state. This assumption has an important role in the estimation of the transition probabilities. When the multi-state model is Markovian, the Aalen-Johansen estimator gives consistent estimators of the transition probabilities but this is no longer the case when the process is non-Markovian. Usually, this assumption is checked including covariates depending on the history. Since the landmark methods of the transition probabilities are free of the Markov assumption, they can also be used to introduce such tests by measuring their discrepancy to Markovian estimators. In this paper, we
introduce tests for the Markov assumption and compare them with the usual approach based on the analysis of covariates depending on history through simulations. The methods are also compared with more recent and competitive approaches. Three real data examples are included for illustration of the proposed methods.This research was financed by Portuguese Funds through FCT - âFundação para a Ciencia e a Tecnologiaâ, within the research grants PTDC/MAT-STA/28248/2017 and PD/BD/142887/2018
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 CiĂȘncia e a Tecnologia", within Projects projects UIDB/00013/2020, UIDP/00013/2020 and the research grant PD/BD/142887/2018
Analysis of complex survival data: a tutorial using the Shiny MSM.app application
The development of applications for obtaining interpretable results in a simple and summarized manner in multi-state models is a research field with great potential, namely in terms of using open source tools that can be easily implemented in biomedical applications. In this tutorial, we introduce MSM.app, an interactive web application using the Shiny package for the R language. In the following sections, we present the main functionalities of the MSM.app and an explanation of the outputs obtained for better understanding, independent of the statistical knowledge of users.This research was financed within the research grants PTDC/MAT-STA/28248/2017 and PD/BD/142887/2018
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