The R Package JMbayes for Fitting Joint Models for Longitudinal and Time-to-Event Data using MCMC

Joint models for longitudinal and time-to-event data constitute an attractive modeling framework that has received a lot of interest in the recent years. This paper presents the capabilities of the R package JMbayes for fitting these models under a Bayesian approach using Markon chain Monte Carlo algorithms. JMbayes can fit a wide range of joint models, including among others joint models for continuous and categorical longitudinal responses, and provides several options for modeling the association structure between the two outcomes. In addition, this package can be used to derive dynamic predictions for both outcomes, and offers several tools to validate these predictions in terms of discrimination and calibration. All these features are illustrated using a real data example on patients with primary biliary cirrhosis.Comment: 42 pages, 6 figure

ltm: An R Package for Latent Variable Modeling and Item Response Analysis

The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum's Three-Parameter models have been implemented, whereas for polytomous data Semejima's Graded Response model is available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule. The capabilities and features of the package are illustrated using two real data examples.

JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data

In longitudinal studies measurements are often collected on different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death, development of a disease or dropout from the study). These outcomes are often separately analyzed; however, in many instances, a joint modeling approach is either required or may produce a better insight into the mechanisms that underlie the phenomenon under study. In this paper we present the R package JM that fits joint models for longitudinal and time-to-event data.

Measuring the Institutional Change of the Monetary Regime in a Political Economy Perspective (Groups of interest and monetary variables during the Currency Board introduction in Bulgaria)

The paper explores the possibilities to measure the institutional change in the monetary field. A political economy theoretical framework is built up, where the change of the monetary regime is analyzed as the outcome of the debtors - creditors interactions. In this perspective, the value of some traditional monetary variables during the period before and after the introduction of the Currency Board in Bulgaria, in 1997, reveals the main actors' evolving relative positions.http://deepblue.lib.umich.edu/bitstream/2027.42/40118/3/wp732.pd

Measuring the Institutional Change of the Monetary Regime in a Political Economy Perspective (Groups of interest and monetary variables during the Currency Board introduction in Bulgaria)

The paper explores the possibilities to measure the institutional change in the monetary field. A political economy theoretical framework is built up, where the change of the monetary regime is analyzed as the outcome of the debtors - creditors interactions. In this perspective, the value of some traditional monetary variables during the period before and after the introduction of the Currency Board in Bulgaria, in 1997, reveals the main actors' evolving relative positions.institutional change, monetary regime, Currency Board, transition, Bulgar

Approximate likelihood inference in generalized linear latent variable models based on integral dimension reduction

Latent variable models represent a useful tool for the analysis of complex data when the constructs of interest are not observable. A problem related to these models is that the integrals involved in the likelihood function cannot be solved analytically. We propose a computational approach, referred to as Dimension Reduction Method (DRM), that consists of a dimension reduction of the multidimensional integral that makes the computation feasible in situations in which the quadrature based methods are not applicable. We discuss the advantages of DRM compared with other existing approximation procedures in terms of both computational feasibility of the method and asymptotic properties of the resulting estimators.Comment: 28 pages, 3 figures, 7 table

JM: An R package for the joint modelling of longitudinal and time-to-event data

In longitudinal studies measurements are often collected on different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death, development of a disease or dropout from the study). These outcomes are often separately analyzed; however, in many instances, a joint modeling approach is either required or may produce a better insight into the mechanisms that underlie the phenomenon under study. In this paper we present the R package JM that fits joint models for longitudinal a