PAM: Plaid Atoms Model for Bayesian Nonparametric Analysis of Grouped Data

We consider dependent clustering of observations in groups. The proposed model, called the plaid atoms model (PAM), estimates a set of clusters for each group and allows some clusters to be either shared with other groups or uniquely possessed by the group. PAM is based on an extension to the well-known stick-breaking process by adding zero as a possible value for the cluster weights, resulting in a zero-augmented beta (ZAB) distribution in the model. As a result, ZAB allows some cluster weights to be exactly zero in multiple groups, thereby enabling shared and unique atoms across groups. We explore theoretical properties of PAM and show its connection to known Bayesian nonparametric models. We propose an efficient slice sampler for posterior inference. Minor extensions of the proposed model for multivariate or count data are presented. Simulation studies and applications using real-world datasets illustrate the model's desirable performance

PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data

It is highly desirable to borrow information from external data to augment a control arm in a randomized clinical trial, especially in settings where the sample size for the control arm is limited. However, a main challenge in borrowing information from external data is to accommodate potential heterogeneous subpopulations across the external and trial data. We apply a Bayesian nonparametric model called Plaid Atoms Model (PAM) to identify overlapping and unique subpopulations across datasets, with which we restrict the information borrowing to the common subpopulations. This forms a hybrid control (HC) that leads to more precise estimation of treatment effects Simulation studies demonstrate the robustness of the new method, and an application to an Atopic Dermatitis dataset shows improved treatment effect estimation

Distribution of fast radio burst dispersion measures in CHIME/FRB Catalog 1: implications on the origin of FRBs

Recently, CHIME/FRB project published its first fast radio burst (FRB) catalog (hereafter, Catalog 1), which totally contains 536 unique bursts. With the help of the latest set of FRBs in this large-size catalog, we aim to investigate the dispersion measure (DM) or redshift ($z$) distribution of the FRB population, and solution of this problem could be used to clarify the question of FRB origin. In this study, we adopted the M\&E 2018 model, to fit the observed $z$ distribution of FRBs in Catalog 1. In the M\&E 2018 model, we are mostly interested in the $\Phi(z)$ function, i.e., number of bursts per proper time per comoving volume, which is represented by the star formation rate (SFR) with a power-law index $n$. Our estimated value of $n$ is $0.0_{-0.0}^{+0.6}$ ($0.0_{-0.0}^{+2.1}$) at the 68 (95) per cent confidence level, implying that the FRB population evolves with redshift consistent with, or faster than, the SFR. Specially, the consistency of the $n$ values estimated by this study and the SFR provides a potential support for the hypothesis of FRBs originating from young magnetars.Comment: 7 pages, 2 figures, accepted for publication in Astronomy Report

Bayesian Parametric and Nonparametric Models for Clinical Trial

With the advancement of computer-based technology, progress in computation has enabled effective real-life application of sampling methods. This has led to the adoption of Bayesian models in clinical trials. To this end, this dissertation comprises three papers that develop and apply Bayesian parametric and nonparametric models for the planning and analysis of clinical trials. The first paper focuses on developing a statistical clustering method that clusters subjects across multiple groups through Bayesian nonparametric modeling. This method, named the Plaid Atoms Model (PAM), is built on the concept of “atom-skipping", which allows the model to stochastically assign zero weights to atoms in an infinite mixture. By implementing atom-skipping across different groups, PAM establishes a dependent clustering pattern, identifying both common and unique clusters among these groups. This approach furtherprovides interpretable posterior inference such as the posterior probability of cluster being unique to a single group or common across a subset of groups. The paper also discusses the theoretical properties of the proposed and related models. Minor extensions of the model for multivariate or count data are presented. Simulation studies and applications using real-world datasets illustrate the performance of the new models with comparison to existing models. The second paper delves into leveraging information from external data to augment the control arm of a current randomized clinical trial (RCT), aiming to borrow information while addressing potential heterogeneity in subpopulations between the external data and the current trial. To achieve this, we employ the PAM model introduced in the first paper. This method is used to identify overlapping and unique subpopulations across datasets, enabling us to limit information borrowing to those subpopulations common to both the external data and the current trial. This strategy establishes a Hybrid Control (HC) that results in a more precise estimation of treatment effects. Through simulation studies, we validate the robustness of the proposed method. Additionally, its application to an Atopic Dermatitis dataset shows the method’s improved treatment effect estimation. The third paper introduces a Bayesian Estimator of Sample Size (BESS) method and its application in oncology dose optimization clinical trials. BESS seeks a balance among three factors: Sample size, Evidence from observed data, and Confidence in posterior inference. It uses a simple logic of "given the evidence from data, with a specific sample size one is guaranteed to achieve a degree of confidence in the posterior inference." This approach contrasts with traditional sample size estimation (SSE), which typically relies on frequentist inference: BESS assumes a possible outcome from the observed data rather than utilizing the true parameters values in SSE method’s sample size calculation. As a result, BESS does not calibration sample size based on type I or II error rates but on posterior probabilities, offering a more interpretable statement for investigators. The proposed method can easily accommodates sample size re-estimation and the incorporation of prior information. We demonstrate its performance through case studies via oncology optimization trials. However, BESS can be applied in general hypothesis tests which we discuss at the end

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression

Highly Solar-Reflective Litchis-Like Core-Shell HGM/TiO2 Microspheres Synthesized by Controllable Heterogeneous Precipitation Method

Hollow glass microsphere (HGM)TiO2 core-shell structural composites have promising applications in the field of energy efficient solar-reflective paints. In this work, after pretreated with saturated Ca(OH)(2) solutions, litchis-like TiO2 shells have been successfully synthesized on HGMs via a controllably heterogeneous precipitation method with Titanium (IV) sulfate (Ti(SO4)(2)) and urea as reaction precursors. It is emphasized that the use of urea as the precipitating agent is essential for the heterogeneous nucleation and growth of Ti(OH)(4) on HGMs, while the Ca(OH)(2) pretreatment provides the heterogeneous nucleation sites on HGMs which promotes the nucleation and growth of Ti(OH)(4), and gives rise to large secondary Ti(OH)(4) particles, leading to the formation of litchis-like TiO2 shells. The resulted core-shell structural HGM/TiO2 microspheres exhibited highest solar reflectance of similar to 83%

Effect of post-deposition heat treatment on laser-TIG hybrid additive manufactured Al-Cu alloy

After solution + artificial aging treatment (T6 heat treatment) of 2219 aluminum alloy fabricated by laser-tungsten inert gas (TIG) hybrid method, more interestingly, we found that both the strength and elongation were improved. The strengthening mechanism has been analysed in details. Results showed that each layer was divided into the arc zone (AZ) and laser zone (LZ) before and after heat treatment. After T6 heat treatment, the columnar crystal grain morphologies remained the same as the as-deposited condition, while the microstructure presented a strong {001} texture along the building direction. Moreover, the high density of the needle-shaped θ″ phases were uniformly precipitated after artificial aging. Distinct grain morphology, increased the mass fraction of Cu in the Al matrix, and nano-precipitates in the AZ and LZ improved the tensile properties, which exhibited a yield strength of 242.1 ± 19.6 MPa, an ultimate tensile strength of 407.1 ± 31.1 MPa, respectively

Fast preparation of uniform large grain size perovskite thin film in air condition via spray deposition method for high efficient planar solar cells

Spray deposition has been demonstrated to be a promising method to prepare perovskite thin film with many advantages, such as easy and processable under fully ambient condition, which is suitable for large-scale production. In this work, we reveal two typical spray deposition process of rapid and slow solvent evaporation. It is emphasized that the rapid solvent evaporation process is essential to avoid dendritic crystal and obtain dense perovskite thin film without pin-holes, which can be realized with a suitable substrate temperature. With optimized spray conditions including flow rate of precursor solution and carrier gas pressure, a dense and uniform perovskite layer with full surface coverage was immediately formed in similar to 5 s without any post-annealing process. The as-fabricated planar heterojunction solar cell achieved a power conversion efficiency (PCE) of 13.54% with 300 +/- 30 nm in thickness of perovskite layer. To the best of our knowledge, this result is the highest value for the CH3NH3PbI3 perovskite solar cells fabricated in air condition with high humidity up to 50%