@article{yanchenko_sengupta_2024, title={A generalized hypothesis test for community structure in networks}, volume={3}, ISSN={["2050-1250"]}, DOI={10.1017/nws.2024.1}, abstractNote={Abstract}, journal={NETWORK SCIENCE}, author={Yanchenko, Eric and Sengupta, Srijan}, year={2024}, month={Mar} }
 @article{yanchenko_bondell_reich_2024, title={The R2D2 Prior for Generalized Linear Mixed Models}, volume={5}, ISSN={["1537-2731"]}, DOI={10.1080/00031305.2024.2352010}, abstractNote={In Bayesian analysis, the selection of a prior distribution is typically done by considering each parameter in the model. While this can be convenient, in many scenarios it may be desirable to place a prior on a summary measure of the model instead. In this work, we propose a prior on the model fit, as measured by a Bayesian coefficient of determination (R2), which then induces a prior on the individual parameters. We achieve this by placing a beta prior on R2 and then deriving the induced prior on the global variance parameter for generalized linear mixed models. We derive closed-form expressions in many scenarios and present several approximation strategies when an analytic form is not possible and/or to allow for easier computation. In these situations, we suggest approximating the prior by using a generalized beta prime distribution and provide a simple default prior construction scheme. This approach is quite flexible and can be easily implemented in standard Bayesian software. Lastly, we demonstrate the performance of the method on simulated and real-world data, where the method particularly shines in high-dimensional settings, as well as modeling random effects.}, journal={AMERICAN STATISTICIAN}, author={Yanchenko, Eric and Bondell, Howard D. and Reich, Brian J.}, year={2024}, month={May} }
 @article{yanchenko_sengupta_2023, title={Core-periphery structure in networks: A statistical exposition}, volume={17}, ISSN={1935-7516}, url={http://dx.doi.org/10.1214/23-SS141}, DOI={10.1214/23-SS141}, abstractNote={Many real-world networks are theorized to have core-periphery structure consisting of a densely-connected core and a loosely-connected periphery. While this phenomenon has been extensively studied in a range of scientific disciplines, it has not received sufficient attention in the statistics community. In this expository article, our goal is to raise awareness about this topic and encourage statisticians to address the many open inference problems in this area. To this end, we first summarize the current research landscape by reviewing the metrics and models that have been used for quantitative studies on core-periphery structure. Next, we formulate and explore various inferential problems in this context, such as estimation, hypothesis testing, and Bayesian inference, and discuss related computational techniques. We also outline the multidisciplinary scientific impact of core-periphery structure in a number of real-world networks. Throughout the article, we provide our own interpretation of the literature from a statistical perspective, with the goal of prioritizing open problems where contribution from the statistics community will be most effective and important.}, number={none}, journal={Statistics Surveys}, publisher={Institute of Mathematical Statistics}, author={Yanchenko, Eric and Sengupta, Srijan}, year={2023}, month={Jan}, pages={42–74} }
 @article{swaminathan_snyder_hong_stevens_long_yanchenko_qiu_liu_zhang_fischer_et al._2023, title={External Control Arms in Idiopathic Pulmonary Fibrosis Using Clinical Trial and Real-World Data Sources}, volume={208}, ISSN={["1535-4970"]}, DOI={10.1164/rccm.202210-1947OC}, abstractNote={RATIONALE
Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease for which novel therapies are needed. External controls (ECs) could enhance IPF trial efficiency, though the direct comparability of ECs to concurrent controls is unknown.


OBJECTIVES
To develop IPF ECs by fit for purpose data standards to historical randomized clinical trial (RCT), multicenter registry (Pulmonary Fibrosis Foundation Patient Registry [PFF-PR]), and electronic health record (EHR) data and to evaluate endpoint comparability among ECs and the BMS-986020 phase 2 RCT.


METHODS
After data curation, the rate of change in forced vital capacity (FVC) from baseline to 26 weeks among participants receiving BMS-986020 600 mg twice daily was compared with the BMS-placebo arm and ECs using mixed effects models with inverse probability weights.


MEASUREMENTS AND MAIN RESULTS
At 26 weeks, the rate of change in FVC was -32.71 mL (BMS-986020) versus -130.09 mL (BMS-placebo; difference, 97.4 mL, 95% CI, 24.6, 170.2), replicating the original BMS-986020 RCT. RCT-ECs showed treatment effect point estimates within the 95% CI of the original BMS-986020 RCT. Both PFF-PR-ECs and EHR-ECs experienced a slower rate of FVC decline compared with the BMS-placebo arm, resulting in treatment effect point estimates outside of the 95% CI of the original BMS-986020 RCT.


CONCLUSIONS
IPF ECs generated from historical RCT placebo arms result in comparable primary treatment effects to that of the original clinical trial, while ECs from real-world data sources, including registry or EHR data, do not. RCT-ECs may serve as a potentially useful supplement to future IPF RCTs.}, number={5}, journal={AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE}, author={Swaminathan, Aparna C. and Snyder, Laurie D. and Hong, Hwanhee and Stevens, Susanna R. and Long, Alexander S. and Yanchenko, Eric and Qiu, Ying and Liu, Rong and Zhang, Hongtao and Fischer, Aryeh and et al.}, year={2023}, month={Sep}, pages={579–588} }
 @article{yanchenko_murata_holme_2023, title={Link prediction for ex ante influence maximization on temporal networks}, volume={8}, ISSN={["2364-8228"]}, DOI={10.1007/s41109-023-00594-z}, abstractNote={Abstract}, number={1}, journal={APPLIED NETWORK SCIENCE}, author={Yanchenko, Eric and Murata, Tsuyoshi and Holme, Petter}, year={2023}, month={Sep} }
 @article{yanchenko_bondell_reich_2023, title={Spatial regression modeling via the R2D2 framework}, volume={10}, ISSN={["1099-095X"]}, DOI={10.1002/env.2829}, abstractNote={Abstract}, journal={ENVIRONMETRICS}, author={Yanchenko, Eric and Bondell, Howard D. and Reich, Brian J.}, year={2023}, month={Oct} }
 @article{yanchenko_2022, title={A divide-and-conquer algorithm for core-periphery identification in large networks}, volume={11}, ISSN={["2049-1573"]}, DOI={10.1002/sta4.475}, abstractNote={Core‐periphery structure is an important network feature where the network is broken into two components: a densely connected core and a loosely connected periphery. In this work, we propose a divide‐and‐conquer algorithm to identify the core‐periphery structure in large networks. By finding this structure on much smaller sub‐samples of the network and then combining the results across sub‐samples, this method yields fast and accurate core‐periphery labels. Additionally, the method provides a measure of the statistical significance of the structure. We apply our approach to synthetic data to find the algorithm's detection limit and on a real‐world network with more than 35,000 nodes.}, number={1}, journal={STAT}, author={Yanchenko, Eric}, year={2022}, month={Dec} }