Directed Acyclic Graph Assisted Method For Estimating Average Treatment Effect.

Observational data, such as electronic clinical records and claims data, can prove invaluable for evaluating the Average Treatment Effect (ATE) and supporting decision-making, provided they are employed correctly. The Inverse Probability of Treatment Weighting (IPTW) method, based on propensity scores, has demonstrated remarkable efficacy in estimating ATE, assuming that the assumptions of exchangeability, consistency, and positivity are met. Directed Acyclic Graphs (DAGs) offer a practical approach to assess the exchangeability assumption, which asserts that treatment assignment and potential outcomes are independent given a set of confounding variables that block all backdoor paths from treatment assignment to potential outcomes.

Prevalence and Treatment for Alcohol Use Disorders Based on Kentucky Medicaid 2012-2019 Datasets.

Alcohol use is the leading substance use in the United States. Persons with alcohol use disorder (AUD) face enormous health consequences and family problems. Analysis of Medicaid enrollee data is critical to understand different aspects of AUD and the treatment utilization for patients with AUD.

Applications of Bayesian shrinkage prior models in clinical research with categorical responses.

Background: Prediction and classification algorithms are commonly used in clinical research for identifying patients susceptible to clinical conditions such as diabetes, colon cancer, and Alzheimer's disease. Developing accurate prediction and classification methods benefits personalized medicine. Building an excellent predictive model involves selecting the features that are most significantly associated with the outcome.

Prevalence and Treatment Utilization of Patients Diagnosed with Depression and Anxiety Disorders Based on Kentucky Medicaid 2012-2019 Datasets.

Objectives: To examine the prevalence and treatment utilization of patients diagnosed with Depression and Anxiety Disorders (DAD) based on Kentucky Medicaid 2012-2019 datasets.

Methods: The study was based on Kentucky Medicaid claims data from 2012 through 2019 for patients 14 years and older. We constructed yearly patient-level databases using ICD_9 CM and ICD_10 CM codes to identify the patients with DAD, using the Current Procedure Terminology (CPT) codes to identify individual psychotherapy and group psychotherapy and using the National drug codes to categorize pharmacotherapy.

Rejoinder to discussions of "distributional independent component analysis for diverse neuroimaging modalities".

We thank the editors for organizing the discussions and the discussants for insightful comments. Our rejoinder provides results and comments to address the questions raised in the discussions. Specifically, we present results showing DICA largely demonstrates better or comparable stability as compared with standard ICA.

Distributional independent component analysis for diverse neuroimaging modalities.

Recent advances in neuroimaging technologies have provided opportunities to acquire brain images of different modalities for studying human brain organization from both functional and structural perspectives. Analysis of images derived from various modalities involves some common goals such as dimension reduction, denoising, and feature extraction. However, since these modalities have vastly different data characteristics, the current analysis is usually performed using distinct analytical tools that are only suitable for a specific imaging modality.