1,721,256 research outputs found
Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature
Full text linkWe propose a new method to perform approximate likelihood inference in latent variable models. Our approach provides an approximation of the integrals involved in the likelihood function through a reduction of their dimension that makes the computation feasible in situations in which classical and adaptive quadrature based methods are not applicable. We derive new theoretical results on the accuracy of the obtained estimators. We show that the proposed approximation outperforms several existing methods in simulations, and it can be successfully applied in presence of multidimensional longitudinal data when standard techniques are not applicable or feasible
A flexible joint-modelling framework for longitudinal and time-to-event data with overdispersion
No full textWe combine conjugate and normal random effects in a joint model for outcomes, at least one of which is non-Gaussian, with particular emphasis on cases in which one of the outcomes is of survival type. Conjugate random effects are used to relax the often-restrictive mean-variance prescription in the non-Gaussian outcome, while normal random effects account for not only the correlation induced by repeated measurements from the same subject but also the association between the different outcomes. Using a case study in chronic heart failure, we show that model fit can be improved, even resulting in impact on significance tests, by switching to our extended framework. By first taking advantage of the ease of analytical integration over conjugate random effects, we easily estimate our framework, by maximum likelihood, in standard software.sponsorship: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We gratefully acknowledge support from IAP research Network P6/03 of the Belgian Government (Belgian Science Policy). (IAP research Network of the Belgian Government (Belgian Science Policy)|P6/03)status: Publishe
Joint Models for Longitudinal and Time-to-Event Data: With Applications in R
No full textIn longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, bu
Bayesian Imputation of Missing Covariates
Full text linkMissing values are a pervasive problem in almost all kinds of studies. In large cohort studies, the type of study most often conducted in the field of epidemiology, missing observations in covariates pose the major challenge. Since measurements are taken in an uncontrolled environment, typically many covariates need to be considered as potential confounders to filter out unwanted influences that environmental factors may have on the estimates of interest. Due to the large number of variables measured and the fact that measurement often relies on participants recalling and reporting detailed information, large proportions of missing data are common in these types of studies. In light of the above, the research that forms this thesis focuses on the analysis of incomplete cohort study data where missingness is in the covariates.
We describe a fully Bayesian approach to analyse and impute data in this setting and discuss a number of naive and more sophisticated approaches to impute such data using multiple imputation with chained equations (MICE). The fully Bayesian approach is applied to multiple applications from the field of Epidemiology, and is further extended to settings with time-varying covariates, in which additional challenges, such as the functional form of the association between outcome and covariate and potential endogeneity arise.
Moreover, the implementation of the fully Bayesian approach in the R package JointAI is described and illustrated by means of various examples
Extensions to joint models for longitudinal and survival outcomes
Full text linkThe feasibility of a progressive resistance-exercise training program in adults with intellectual disabilities with cardiovascular risk factor
Applications and Extensions of Joint Models in Clinical Trials: estimating intervention effects
Full text linkIn this thesis, we develop and apply statistical methodology to estimate and understand different types of intervention effects in longitudinal clinical trial data. To this end, we use the framework of joint models for longitudinal and survival (i.e.,) data, which couples the longitudinal measurements and the survival data into a single statistical model
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Bayesian shrinkage approach for a joint model of longitudinal and survival outcomes assuming different association structures
No full textThe joint modeling of longitudinal and survival data has recently received much attention. Several extensions of the standard joint model that consists of one longitudinal and one survival outcome have been proposed including the use of different association structures between the longitudinal and the survival outcomes. However, in general, relatively little attention has been given to the selection of the most appropriate functional form to link the two outcomes. In common practice, it is assumed that the underlying value of the longitudinal outcome is associated with the survival outcome. However, it could be that different characteristics of the patients' longitudinal profiles influence the hazard. For example, not only the current value but also the slope or the area under the curve of the longitudinal outcome. The choice of which functional form to use is an important decision that needs to be investigated because it could influence the results. In this paper, we use a Bayesian shrinkage approach in order to determine the most appropriate functional forms. We propose a joint model that includes different association structures of different biomarkers and assume informative priors for the regression coefficients that correspond to the terms of the longitudinal process. Specifically, we assume Bayesian lasso, Bayesian ridge, Bayesian elastic net, and horseshoe. These methods are applied to a dataset consisting of patients with a chronic liver disease, where it is important to investigate which characteristics of the biomarkers have an influence on survival. Copyright (C) 2016 John Wiley & Sons, Ltd
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