Yong Chen - US grants

Project Summary Comparative effectiveness research (CER) relies fundamentally on accurate and timely assessment of the benefits and risks of different treatment options. Empirical evidence suggests that a median of 35% of efficacy and 50% of safety outcomes per parallel group trials were incompletely reported, and statistically significant outcomes had a higher likelihood of being fully reported compared to non-significant outcomes, both for efficacy and safety. Such a bias is referred to as outcome reporting bias (ORB), i.e., ?the selective reporting of some outcomes but not others, depending on the nature and direction of the results (i.e., missing certain outcomes).? Selective reporting can invalidate results from meta-analyses. As acknowledged in the Cochrane handbook ?Statistical methods to detect within-study selective reporting (i.e., outcome-reporting bias) are, as yet, not well developed? (chapter 8.14.2, version 5.0.2), there is a critical need to develop methods specifically accounting for ORB. In response to PA-16-160, the overall goal of this proposal is to develop, test and evaluate new statistical methods and user-friendly software to account for ORB in multivariate and network meta-analyses. In this proposal, we will focus on: (1) To propose and evaluate new methods for quantifying the evidence of ORB, to adjusting for ORB, and to develop a procedure of sensitivity analysis under ORB in multivariate meta-analysis. (2) To generalize the methods in Aim 1 to network meta-analyses (where more than 2 treatments are compared simultaneously), and to propose methods to evaluate the evidence consistency. And (3) To develop publicly available, user-friendly and well-documented software and apply the proposed methods to research data sets. We will use carefully designed simulation studies to investigate the performance of the proposed methods, apply the proposed methods to multiple existing databases, and develop statistical software for wider research communities. We propose to perform empirical assessment of the strengths and weaknesses of these methods through carefully designed simulation studies and, more importantly, applications to (network) meta-analyses of clinical trials with multivariate outcomes. Completion of these three aims in this proposal will directly benefit the CER program by providing state-of-the art methods implemented in user-friendly R package that will be made freely available to the public. This has the potential to catalyze the development of many new methods, amplifying the impact of our project.

Project Summary This proposal seeks support for developing advanced clinical informatics and computational approaches for drug-repositioning for Alzheimer's disease (AD) and related dementias (ADRD). The proposed project directly addresses the areas of emphasis in PAR-20-156 to ?develop computational methods such as artificial intelligence/machine learning to investigate new uses of FDA-approved drugs or candidate drugs from failed Phase II/Phase III clinical trials through analysis of multimodal data.? The overarching goals of this proposal are to develop novel clinical informatics and computational approaches for drug repositioning of AD/ADRD. Specifically, we will develop statistical methods and ontology technology to extract drug-repositioning signals from multidimensional data (e.g., pharmacy-linked genetic data and biobank data, historical trials, and EHR data). The proposed framework is novel because it integrates advanced statistical inference procedures with semantic technology for data-driven and reproducible drug repositioning for AD/ADRD. We have three aims: We have three specific aims: Aim 1: Develop signal detection methods using multi-modal data (pharmacy-linked genetic data, genetic and electronic health record (EHR) data, and BioBank data). Aim 2: Evaluate the efficacy and safety of candidate drugs via historical trials and EHR data. Aim 3: Develop novel semantic and natural language processing (NLP) methods for Knowledge Graph (KG) construction. The success of this project will lead to novel computational methods, KG, and software for facilitating drug repositioning for AD/ADRD based on multimodal data. If successful, the proposed method could identify novel drug repositioning signals and generate novel hypotheses for prevention and treatment intervention of treat AD/ADRD. Our project holds the promise of identifying novel drug repositioning signals. This project is novel for integrating evidence synthesis methods with signal detection methods using advanced multimodal modeling, and it is potentially transformative for advancing prevention and treatment for AD/ADRD.

Project Summary In response to the (PAR-18-896), the overarching goal of this proposal is to fully develop a joint effort between statisticians, medical informaticians, clinicians with a focus on developing a rigorous bias correction framework through modern knowledge engineering and data-driven statistical modeling, for improving the unbiasedness and reproducibility of health system data driven research. In this proposal, we will focus on: (1) Develop a novel prior-knowledge-guided integrated likelihood approach to enable bias correction by incorporating prior phenotyping accuracy. (2) Develop methods and algorithms to account for EHR phenotyping errors in both outcomes and predictors. And (3) Validation, Application and Software development. We will use the proposed bias correction methods to several EHR datasets to replicate existing findings and investigate new hypothesis in multiple datasets at University of Texas and University of Pennsylvania. We will also develop software for the proposed methods to facilitate ongoing EHR-based clinical studies.