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Joint Modelling (joint + modelling)

Distribution by Scientific Domains
Mathematics and Statistics83%

Selected Abstracts

Joint Modelling of Repeated Transitions in Follow-up Data , A Case Study on Breast Cancer Data

BIOMETRICAL JOURNAL, Issue 3 2005
B. Genser
Abstract In longitudinal studies where time to a final event is the ultimate outcome often information is available about intermediate events the individuals may experience during the observation period. Even though many extensions of the Cox proportional hazards model have been proposed to model such multivariate time-to-event data these approaches are still very rarely applied to real datasets. The aim of this paper is to illustrate the application of extended Cox models for multiple time-to-event data and to show their implementation in popular statistical software packages. We demonstrate a systematic way of jointly modelling similar or repeated transitions in follow-up data by analysing an event-history dataset consisting of 270 breast cancer patients, that were followed-up for different clinical events during treatment in metastatic disease. First, we show how this methodology can also be applied to non Markovian stochastic processes by representing these processes as "conditional" Markov processes. Secondly, we compare the application of different Cox-related approaches to the breast cancer data by varying their key model components (i.e. analysis time scale, risk set and baseline hazard function). Our study showed that extended Cox models are a powerful tool for analysing complex event history datasets since the approach can address many dynamic data features such as multiple time scales, dynamic risk sets, time-varying covariates, transition by covariate interactions, autoregressive dependence or intra-subject correlation. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

Age,period,cohort models and disease mapping

ENVIRONMETRICS, Issue 5 2003
Corrado Lagazio
Abstract Joint modelling of space and time variation of the risk of disease is an important topic in descriptive epidemiology. Most of the proposals in this field deal with at most two time scales (age,period or age,cohort). We propose a hierarchical Bayesian model that can be used as a general framework to jointly study the evolution in time and the spatial pattern of the risk of disease. The rates are modelled as a function of purely spatial terms (local effects of risk factors that do not vary in time), time effects (on the three time axes: age, calendar period and birth cohort) and space,time interactions that describe area specific time patterns. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Maximum likelihood estimation in semiparametric regression models with censored data

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 4 2007
D. Zeng
Summary., Semiparametric regression models play a central role in formulating the effects of covariates on potentially censored failure times and in the joint modelling of incomplete repeated measures and failure times in longitudinal studies. The presence of infinite dimensional parameters poses considerable theoretical and computational challenges in the statistical analysis of such models. We present several classes of semiparametric regression models, which extend the existing models in important directions. We construct appropriate likelihood functions involving both finite dimensional and infinite dimensional parameters. The maximum likelihood estimators are consistent and asymptotically normal with efficient variances. We develop simple and stable numerical techniques to implement the corresponding inference procedures. Extensive simulation experiments demonstrate that the inferential and computational methods proposed perform well in practical settings. Applications to three medical studies yield important new insights. We conclude that there is no reason, theoretical or numerical, not to use maximum likelihood estimation for semiparametric regression models. We discuss several areas that need further research. [source]

Pricing credit derivatives under stochastic recovery in a hybrid model

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 3 2010
Stephan Höcht
Abstract In this article, a framework for the joint modelling of default and recovery risk is presented. The model accounts for typical characteristics known from empirical studies, e.g. negative correlation between recovery-rate process and default intensity, as well as between default intensity and state of the economy, and a positive dependence of recovery rates on the economic environment. Within this framework analytically tractable pricing formulas for credit derivatives are derived. The stochastic model for the recovery process allows for the pricing of credit derivatives with payoffs that are directly linked to the recovery rate at default, e.g. recovery locks. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Electrical and magnetic response of archaeological features at the early neolithic site of Movila lui Deciov, western Romania

ARCHAEOLOGICAL PROSPECTION, Issue 4 2004
J. M. Maillol
Abstract An archaeological geophysics survey was conducted on the early neolithic site of Movila lui Deciov, in the Province of Banat, Romania. Magnetometry and electromagnetic terrain conductivity were used for the main prospection effort, and a test of electrical resistivity imaging was conducted on a selected profile. In addition, magnetic susceptibility measurements were obtained from excavation pit samples. The magnetic survey was successful in determining the extent of the site, in delimiting zones rich in structures and artefacts, and in confirming the presence of a ditched enclosure that could be the earliest known in the region. The electromagnetic survey was limited by a lack of resolution of electrical property contrast. Detailed joint modelling of the magnetic and electrical response of the subsurface was used to confirm that electrical resistivity imaging can provide depth information to complement magnetic mapping. One of very few reported in Romania, this survey paves the way for an increased use of geophysical techniques in the cultural heritage management of this country. From a methodological viewpoint, this work further demonstrates the potential of electrical resistivity imaging in archaeology Copyright © 2004 John Wiley & Sons, Ltd. [source]

A Semi-Parametric Shared Parameter Model to Handle Nonmonotone Nonignorable Missingness

BIOMETRICS, Issue 1 2009
Roula Tsonaka
Summary Longitudinal studies often generate incomplete response patterns according to a missing not at random mechanism. Shared parameter models provide an appealing framework for the joint modelling of the measurement and missingness processes, especially in the nonmonotone missingness case, and assume a set of random effects to induce the interdependence. Parametric assumptions are typically made for the random effects distribution, violation of which leads to model misspecification with a potential effect on the parameter estimates and standard errors. In this article we avoid any parametric assumption for the random effects distribution and leave it completely unspecified. The estimation of the model is then made using a semi-parametric maximum likelihood method. Our proposal is illustrated on a randomized longitudinal study on patients with rheumatoid arthritis exhibiting nonmonotone missingness. [source]