Sara Wade

Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics.

Proposed diagnostic criteria (international working group and National Institute on Aging and Alzheimers Association) for Alzheimers disease (AD) include markers of amyloidosis (abnormal cerebrospinal fluid [CSF] amyloid beta [Aβ]42) and neurodegeneration (hippocampal atrophy, temporo‐parietal hypometabolism on [18F]‐fluorodeoxyglucose‐positron emission tomography (FDG‐PET), and abnormal CSF tau). We aim to compare the accuracy of these biomarkers, individually and in combination, in predicting AD among mild cognitive impairment (MCI) patients.

An enriched conjugate prior for Bayesian nonparametric inference

The precision parameter plays an important role in the Dirichlet Pro- cess. When assigning a Dirichlet Process prior to the set of probability measures on R k , k > 1, this can be restrictive in the sense that the variability is determined by a single parameter. The aim of this paper is to construct an enrichment of the Dirichlet Process that is more exible with respect to the precision parameter yet still conjugate, starting from the notion of enriched conjugate priors, which have been proposed to address an analogous lack of exibility of standard conju- gate priors in a parametric setting. The resulting enriched conjugate prior allows more exibility in modelling uncertainty on the marginal and conditionals. We describe an enriched urn scheme which characterizes this process and show that it can also be obtained from the stick-breaking representation of the marginal and conditionals. For non atomic base measures, this allows global clustering of the marginal variables and local clustering of the conditional variables. Finally, we consider an application to mixture models that allows for uncertainty between homoskedasticity and heteroskedasticity.

Alzheimer Disease Biomarkers as Outcome Measures for Clinical Trials in MCI.

Background:The aim of this study was to compare the performance and power of the best-established diagnostic biological markers as outcome measures for clinical trials in patients with mild cognitive impairment (MCI). Methods:Magnetic resonance imaging, F-18 fluorodeoxyglucose positron emission tomography markers, and Alzheimer’s Disease Assessment Scale-cognitive subscale were compared in terms of effect size and statistical power over different follow-up periods in 2 MCI groups, selected from Alzheimer’s Disease Neuroimaging Initiative data set based on cerebrospinal fluid (abnormal cerebrospinal fluid A&bgr;1-42 concentration—ABETA+) or magnetic resonance imaging evidence of Alzheimer disease (positivity to hippocampal atrophy—HIPPO+). Biomarkers progression was modeled through mixed effect models. Scaled slope was chosen as measure of effect size. Biomarkers power was estimated using simulation algorithms. Results:Seventy-four ABETA+ and 51 HIPPO+ MCI patients were included in the study. Imaging biomarkers of neurodegeneration, especially MR measurements, showed highest performance. For all biomarkers and both MCI groups, power increased with increasing follow-up time, irrespective of biomarker assessment frequency. Conclusion:These findings provide information about biomarker enrichment and outcome measurements that could be employed to reduce MCI patient samples and treatment duration in future clinical trials.

Bayesian cluster analysis : point estimation and credible balls

I begin my discussion by giving an overview of the main results. Then I proceed to touch upon issues about whether the credible ball constructed can be interpreted as a confidence ball, suggestions on reducing computational costs, and posterior consistency or contraction rates.Clustering is widely studied in statistics and machine learning, with applications in a variety of fields. As opposed to popular algorithms such as agglomerative hierarchical clustering or k-means which return a single clustering solution, Bayesian nonparametric models provide a posterior over the entire space of partitions, allowing one to assess statistical properties, such as uncertainty on the number of clusters. However, an important problem is how to summarize the posterior; the huge dimension of partition space and difficulties in visualizing it add to this problem. In a Bayesian analysis, the posterior of a real-valued parameter of interest is often summarized by reporting a point estimate such as the posterior mean along with 95% credible intervals to characterize uncertainty. In this paper, we extend these ideas to develop appropriate point estimates and credible sets to summarize the posterior of the clustering structure based on decision and information theoretic techniques.Clustering is widely studied in statistics and machine learning, with applications in a variety of fields. As opposed to popular algorithms such as agglomerative hierarchical clustering or k-means which return a single clustering solution, Bayesian nonparametric models provide a posterior over the entire space of partitions, allowing one to assess statistical properties, such as uncertainty on the number of clusters. However, an important problem is how to summarize the posterior; the huge dimension of partition space and difficulties in visualizing it add to this problem. In a Bayesian analysis, the posterior of a real-valued parameter of interest is often summarized by reporting a point estimate such as the posterior mean along with 95% credible intervals to characterize uncertainty. In this paper, we extend these ideas to develop appropriate point estimates and credible sets to summarize the posterior of the clustering structure based on decision and information theoretic techniques.

A Bayesian Nonparametric Regression Model With Normalized Weights: A Study of Hippocampal Atrophy in Alzheimer's Disease

Hippocampal volume is one of the best established biomarkers for Alzheimer’s disease. However, for appropriate use in clinical trials research, the evolution of hippocampal volume needs to be well understood. Recent theoretical models propose a sigmoidal pattern for its evolution. To support this theory, the use of Bayesian nonparametric regression mixture models seems particularly suitable due to the flexibility that models of this type can achieve and the unsatisfactory predictive properties of semiparametric methods. In this article, our aim is to develop an interpretable Bayesian nonparametric regression model which allows inference with combinations of both continuous and discrete covariates, as required for a full analysis of the dataset. Simple arguments regarding the interpretation of Bayesian nonparametric regression mixtures lead naturally to regression weights based on normalized sums. Difficulty in working with the intractable normalizing constant is overcome thanks to recent advances in MCMC methods and the development of a novel auxiliary variable scheme. We apply the new model and MCMC method to study the dynamics of hippocampal volume, and our results provide statistical evidence in support of the theoretical hypothesis.

Bayesian Cluster Analysis: Point Estimation and Credible Balls (with Discussion)

I begin my discussion by giving an overview of the main results. Then I proceed to touch upon issues about whether the credible ball constructed can be interpreted as a confidence ball, suggestions on reducing computational costs, and posterior consistency or contraction rates.Clustering is widely studied in statistics and machine learning, with applications in a variety of fields. As opposed to popular algorithms such as agglomerative hierarchical clustering or k-means which return a single clustering solution, Bayesian nonparametric models provide a posterior over the entire space of partitions, allowing one to assess statistical properties, such as uncertainty on the number of clusters. However, an important problem is how to summarize the posterior; the huge dimension of partition space and difficulties in visualizing it add to this problem. In a Bayesian analysis, the posterior of a real-valued parameter of interest is often summarized by reporting a point estimate such as the posterior mean along with 95% credible intervals to characterize uncertainty. In this paper, we extend these ideas to develop appropriate point estimates and credible sets to summarize the posterior of the clustering structure based on decision and information theoretic techniques.Clustering is widely studied in statistics and machine learning, with applications in a variety of fields. As opposed to popular algorithms such as agglomerative hierarchical clustering or k-means which return a single clustering solution, Bayesian nonparametric models provide a posterior over the entire space of partitions, allowing one to assess statistical properties, such as uncertainty on the number of clusters. However, an important problem is how to summarize the posterior; the huge dimension of partition space and difficulties in visualizing it add to this problem. In a Bayesian analysis, the posterior of a real-valued parameter of interest is often summarized by reporting a point estimate such as the posterior mean along with 95% credible intervals to characterize uncertainty. In this paper, we extend these ideas to develop appropriate point estimates and credible sets to summarize the posterior of the clustering structure based on decision and information theoretic techniques.