1,721,070 research outputs found
Tree-ring based climate reconstruction using a hierarchical Bayesian model
No full textA hierarchical Bayesian model for paleoclimate reconstruction is illustrated along with
an application to an Italian site. Climate is represented through temperature and moisture variables, while the
reconstruction is based on tree-ring widths only
Bayesian nonparametric estimation of targeted agent effects on biomarker change to predict clinical outcome
No full textThe effect of a targeted agent on a cancer patients clinical outcome putatively is mediated through the agents effect on one or more early biological events. This is motivated by pre-clinical experiments with cells or animals that identify such events, represented by binary or quantitative biomarkers. When evaluating targeted agents in humans, central questions are whether the distribution of a targeted biomarker changes following treatment, the nature and magnitude of this change, and whether it is associated with clinical outcome. Major difficulties in estimating these effects are that a biomarkers distribution may be complex, vary substantially between patients, and have complicated relationships with clinical outcomes. We present a probabilistically coherent framework for modeling and estimation in this setting, including a hierarchical Bayesian nonparametric mixture model for biomarkers that we use to define a functional profile of pre-versus-post-treatment biomarker distribution change. The functional is similar to the receiver operating characteristic used in diagnostic testing. The hierarchical
model yields clusters of individual patient biomarker profile functionals, and we use the profile as a covariate in a regression model for clinical outcome. The methodology is illustrated by analysis of a dataset from a clinical trial in prostate cancer using imatinib to target platelet-derived growth factor, with the clinical aim to improve progression-free survival time
BAYESIAN NON PARAMETRIC IDENTIFICATION OF PRE-VERSUS-POST TREATMENT BIOMARKER EFFECTS ON PROGRESSION FREE SURVIVAL
No full textTargeted cancer therapies require the assessment of the effect of a given treatment on different populations of
patients. One important issue is that the distribution of many biomarkers directly targeted by the treatment may also vary
widely across patients, as well as before and after therapy. In this paper, we discuss a probabilistically coherent and unified
framework for quantifying how a targeted therapy affects the distribution of a given biomarker and, a fortiori, the individual
survival outcomes. In particular, we adopt a Bayesian nonparametric perspective and nest the assessment of the complete individual biomarker profiles in a general survival model. We exemplify our methods on a known clinical dataset, that reports the results of a therapeutic clinical trial targeting plateled-derived growth factor levels in prostate cancer subjects
Diagnostic Techniques for Regression Rate of Paraffin-Based Fuels in a VFP Hybrid Rocket Engine
No full text
Galton-Watson process: a non parametric prior for the offspring distribution
No full textIn this article we propose a non parametric prior for the probabilities of the Galton- Watson process based on the Dirichlet Process. After recalling the main properties of the Galton-Watson process and presenting the estimation methods already present in the lit- erature, such as maximum likelihood and Bayesian conjugate analysis, we define the new prior by pointing out how it is more general than the Dirichlet prior used in the conjugate analysis, which is a special case of our extension. Finally, we show the results of a simula- tion study illustrating how our analysis leads to a more accurate classification of the process
A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data
Full text linkThe use of high-dimensional data for targeted therapeutic interventions
requires new ways to characterize the heterogeneity observed across subgroups
of a specific population. In particular, models for partially exchangeable data
are needed for inference on nested datasets, where the observations are assumed
to be organized in different units and some sharing of information is required
to learn distinctive features of the units. In this manuscript, we propose a
nested Common Atoms Model (CAM) that is particularly suited for the analysis of
nested datasets where the distributions of the units are expected to differ
only over a small fraction of the observations sampled from each unit. The
proposed CAM allows a two-layered clustering at the distributional and
observational level and is amenable to scalable posterior inference through the
use of a computationally efficient nested slice-sampler algorithm. We further
discuss how to extend the proposed modeling framework to handle discrete
measurements, and we conduct posterior inference on a real microbiome dataset
from a diet swap study to investigate how the alterations in intestinal
microbiota composition are associated with different eating habits. We further
investigate the performance of our model in capturing true distributional
structures in the population by means of a simulation study
Multiple hypothesis screening using mixtures of non‐local distributions with applications to genomic studies
Full text linkThe analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two competing densities, the null and the alternative distributions. We investigate the use of weighted densities and, in particular, non-local densities as working alternative distributions, to enforce separation from the null and thus refine the screening procedure. We show how these weighted alternatives improve various operating characteristics, such as the Bayesian false discovery rate, of the resulting tests for a fixed mixture proportion with respect to a local, unweighted likelihood approach. Parametric and nonparametric model specifications are proposed, along with efficient samplers for posterior inference. By means of a simulation study, we exhibit how our model compares with both well-established and state-of-the-art alternatives in terms of various operating characteristics. Finally, to illustrate the versatility of our method, we conduct three differential expression analyses with publicly-available datasets from genomic studies of heterogeneous nature
Two‐group Poisson‐Dirichlet mixtures for multiple testing
No full textThe simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed in Efron (2004), identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literature, many approaches have suggested using mixtures of Dirichlet Processes in the two group model framework. Here, we investigate employing instead mixtures of two-parameter Poisson Dirichlet Processes (2PPD), and show how they provide a more flexible and effective tool for large-scale hypothesis testing. Our model further employs non-local prior densities to allow separation between the two mixture components. We obtain a closed form expression for the exchangeable partition probability function of the two-group model, which leads to a straightforward MCMC implementation. We compare the performances of our method for large-scale inference in a simulation study and illustrate its use on both a prostate cancer dataset and a case-control microbiome study of the gastrointestinal tracts in children from underdeveloped countries who have been recently diagnosed with moderate to severe diarrhe
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