The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform
David Taylorrobinson - One of the best experts on this subject based on the ideXlab platform.
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joint modelling of repeated measurements and time to Event outcomes flexible model specification and exact likelihood inference
Journal of The Royal Statistical Society Series B-statistical Methodology, 2015Co-Authors: Jessica K Barrett, Robin Henderson, Peter J. Diggle, David TaylorrobinsonAbstract:Random effects or shared parameter models are commonly advocated for the analysis of combined repeated measurement and Event History Data, including dropout from longitudinal trials. Their use in practical applications has generally been limited by computational cost and complexity, meaning that only simple special cases can be fitted by using readily available software. We propose a new approach that exploits recent distributional results for the extended skew normal family to allow exact likelihood inference for a flexible class of random-effects models. The method uses a discretization of the timescale for the time-to-Event outcome, which is often unavoidable in any case when Events correspond to dropout. We place no restriction on the times at which repeated measurements are made. An analysis of repeated lung function measurements in a cystic fibrosis cohort is used to illustrate the method.
Peter J. Diggle - One of the best experts on this subject based on the ideXlab platform.
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joint modelling of repeated measurements and time to Event outcomes flexible model specification and exact likelihood inference
Journal of The Royal Statistical Society Series B-statistical Methodology, 2015Co-Authors: Jessica K Barrett, Robin Henderson, Peter J. Diggle, David TaylorrobinsonAbstract:Random effects or shared parameter models are commonly advocated for the analysis of combined repeated measurement and Event History Data, including dropout from longitudinal trials. Their use in practical applications has generally been limited by computational cost and complexity, meaning that only simple special cases can be fitted by using readily available software. We propose a new approach that exploits recent distributional results for the extended skew normal family to allow exact likelihood inference for a flexible class of random-effects models. The method uses a discretization of the timescale for the time-to-Event outcome, which is often unavoidable in any case when Events correspond to dropout. We place no restriction on the times at which repeated measurements are made. An analysis of repeated lung function measurements in a cystic fibrosis cohort is used to illustrate the method.
Thomas Kneib - One of the best experts on this subject based on the ideXlab platform.
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bayesian smoothing and regression for longitudinal spatial and Event History Data
2011Co-Authors: Ludwig Fahrmeir, Thomas KneibAbstract:1. Introduction: Scope of the Book and Applications 2. Basic Concepts for Smoothing and Semiparametric Regression 3. Generalised Linear Mixed Models 4. Semiparametric Mixed Models for Longitudinal Data 5. Spatial Smothing, Interactions and Geoadditive Regression 6. Event History Data
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Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data
2011Co-Authors: Ludwig Fahrmeir, Thomas KneibAbstract:Several recent advances in smoothing and semiparametric regression are presented in this book from a unifying, Bayesian perspective. Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) inference, as well as empirical Bayes procedures closely related to penalized likelihood estimation and mixed models, are considered here. Throughout, the focus is on semiparametric regression and smoothing based on basis expansions of unknown functions and effects in combination with smoothness priors for the basis coefficients. Beginning with a review of basic methods for smoothing and mixed models, longitudinal Data, spatial Data and Event History Data are treated in separate chapters. Worked examples from various fields such as forestry, development economics, medicine and marketing are used to illustrate the statistical methods covered in this book. Most of these examples have been analysed using implementations in the Bayesian software, BayesX, and some with R Codes. These, as well as some of the Data sets, are made publicly available on the website accompanying this book.
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flexible hazard ratio curves for continuous predictors in multi state models an application to breast cancer Data
Statistical Modelling, 2010Co-Authors: Carmen Cadarsosuarez, Luis Meiramachado, Thomas Kneib, Francisco GudeAbstract:Multi-state models (MSMs) are very useful for describing complicated Event History Data. These models may be considered as a generalization of survival analysis where survival is the ultimate outcome of interest but where intermediate (transient) states are identified. One major goal in clinical applications of MSMs is to study the relationship between the different covariates and disease evolution. Usually, MSMs are assumed to be parametric, and the effects of continuous predictors on log-hazards are modelled linearly. In practice, however, the effect of a given continuous predictor can be unknown, and its form may be different in all transitions. In this paper,we propose a P-spline approach that allows for non-linear relationships between continuous predictors and survival in the multi-state framework. To better understand the effects at each transition, results are expressed in terms of hazard ratio curves, taking a specific covariate value as reference. Confidence bands for these curves are also deriv...
Mark A Collinson - One of the best experts on this subject based on the ideXlab platform.
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a training manual for Event History Data management using health and demographic surveillance system Data
BMC Research Notes, 2017Co-Authors: Philippe Bocquier, Carren Ginsburg, Kobus Herbst, Osman Sankoh, Mark A CollinsonAbstract:The objective of this research note is to introduce a training manual for Event History Data management. The manual provides a first comprehensive guide to longitudinal Health and Demographic Surveillance System (HDSS) Data management that allows for a step-by-step description of the process of structuring and preparing a Dataset for the calculation of demographic rates and Event History analysis. The research note provides some background information on the INDEPTH Network, and the iShare Data repository and describes the need for a manual to guide users as to how to correctly handle HDSS Datasets. The approach outlined in the manual is flexible and can be applied to other longitudinal Data sources. It facilitates the development of standardised longitudinal Data management and harmonization of Datasets to produce a comparative set of results.
Jessica K Barrett - One of the best experts on this subject based on the ideXlab platform.
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joint modelling of repeated measurements and time to Event outcomes flexible model specification and exact likelihood inference
Journal of The Royal Statistical Society Series B-statistical Methodology, 2015Co-Authors: Jessica K Barrett, Robin Henderson, Peter J. Diggle, David TaylorrobinsonAbstract:Random effects or shared parameter models are commonly advocated for the analysis of combined repeated measurement and Event History Data, including dropout from longitudinal trials. Their use in practical applications has generally been limited by computational cost and complexity, meaning that only simple special cases can be fitted by using readily available software. We propose a new approach that exploits recent distributional results for the extended skew normal family to allow exact likelihood inference for a flexible class of random-effects models. The method uses a discretization of the timescale for the time-to-Event outcome, which is often unavoidable in any case when Events correspond to dropout. We place no restriction on the times at which repeated measurements are made. An analysis of repeated lung function measurements in a cystic fibrosis cohort is used to illustrate the method.