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Targeted therapy with a localized abluminal groove low-dose sirolimus-eluting bioabsorbable polymer coronary stent in chronic total occlusions: The TARGET CTO non-inferiority randomized trial

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AMERICAN HEART JOURNAL 2025年 285卷 93-104页

作者： Wang, Geng Zhang, Ruiyan Chen, Shao-Liang Wang, Jian'an Li, Yi Zheng, Ming Cao, Ruifen Ma, Yitong Sun, Zhiqi Li, Xueqi Su, Xi Lu, Wen Xu, Yawei Li, Xue Li, Yang Sun, Fucheng Han, Yaling Gen Hosp Northern Theater Command Cardiovasc Res Inst State Key Lab Frigid Zone Cardiovasc Dis Shenyang Peoples R China Gen Hosp Northern Theater Command Dept Cardiol Shenyang Peoples R China Shanghai Jiao Tong Univ Sch Med Ruijin Hosp Dept Cardiol Shanghai Peoples R China Nanjing Med Univ Nanjing Hosp 1 Dept Cardiol Nanjing Peoples R China Second Affiliated Hosp Zhejiang Univ Sch Med Dept Gastroenterol Hangzhou Peoples R China Shanghai MicroPort Med Grp Co Ltd Clin Sci & Med Affairs Dept Shanghai Peoples R China First Affiliated Hosp Xinjiang Med Univ Dept Cardiol Urumqi Peoples R China Daqing Oilfield Gen Hosp Dept Cardiol Daqing Heilongjiang Peoples R China Fourth Affiliated Hosp Harbin Med Univ Dept Ophthalmol Harbin Peoples R China Wuhan Asia Heart Hosp Dept Cardiol Wuhan Peoples R China Xuzhou Cent Hosp Dept Cardiol Xuzhou Peoples R China Shanghai Tenth Peoples Hosp Dept Cardiol Shanghai Peoples R China AF Mil Med Univ Tangdu Hosp Dept Cardiol Xian Peoples R China Beijing Hosp Dept Cardiol Beijing Peoples R China

Background Our objective was to compare the efficacy and safety of a drug-eluting stent featuring an abluminal bioabsorbable sirolimus-containing polymer coating (BP-SES) with an everolimus-eluting stent with a durable polymer (DPEES) in patients undergoing percutaneous coronary intervention (PCI) for chronic total occlusions (CTOs). Methods TARGET CTO is a multicenter, open-label, noninferiority trial that randomized patients to either BP-SES or DP-EES in a 1:1 fashion following successful CTO re-canalization. The primary endpoint that was powered for noninferiority assessment is in-stent late lumen loss (LLL) at 12 months. Results A total of 206 subjects underwent randomization, with 103 assigned to the BP-SES group and 103 to the DP-EES group. Baseline clinical and angiographic characteristics were comparable. The primary endpoint demonstrated noninferiority for the BP-SES group compared to the DP-EES group (0.21 +/- 0.43 mm vs 0.21 +/- 0.33 mm;P = . 934, 2-sided;difference 0.01mm [BP-SES minus DP-EES];95% CI:-0.13 to 0.12 mm;p noninferiority < .001,1-sided). No significant differences were observed in secondary angiographic or clinical endpoints. The rates of 12-month in-stent and in-segment binary restenosis in the BP-SES group and the DP-EES group were similar (6.8% vs 7.5%, P = . 86;and 8.1% vs 8.8%;P = . 89, respectively). Although there was a trend favoring the BP-SES group, the difference between the BP-SES group and DP-EES group at 12 months in target lesion failure (2.1% vs 8.0%, P = . 054) and target lesion revascularization (2.1% vs 7.1%, P = . 089) did not reach statistical significance. No definite or probable stent thromboses were reported in either group. Conclusions Compared to DP-EES, PCI of CTOs with BP-SES showed similar results in terms of late loss and binary restenosis at the 12-month follow-up. Clinical Trial ***, number NCT03040934 (Am HeartJ 2025;285:93-104.)

关键词： Coronary artery disease bioabsorbable polymer percutaneous coronary intervention chronic total occlusion CTO chronic total occlusion PCI percutaneous coronary intervention BMS bare metal stent DES drug-eluting stent DP-EES durable-polymer everolimus-eluting stents BP-SES bioabsorbable-polymer sirolimus-eluting stents LLL late lumen loss QCA quantitative coronary angiography PTCA percutaneous transluminal coronary angioplasty IWRS interactive web response system MLD minimum lumen diameter TVR target vessel revascularization ID-TLR ischemia-driven target lesion revascularization TLF target lesion failure MI myocardial infarction ITT intention-to-treat SD standard deviation GLM generalized linear model HR hazard ratio CI confidence interval CCS Canadian Cardiovascular Society

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Commercial pot selectivity for European lobster, Homarus gammarus, estimated through a novel v-notching mark-recapture approach

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FISHERIES RESEARCH 2025年 282卷

作者： Heney, Charlotte Gross, Julie M. Hoenig, John M. Hold, Natalie Bangor Univ Sch Ocean Sci Askew StMenai Bridge Anglesey LL59 5AB Wales William & Mary Virginia Inst Marine Sci POB 1346 Gloucester Point VA 23062 USA

Understanding gear selectivity can improve interpretation of population size data derived from commercial landings and support stock assessment models. The selectivity of gear over time often evolves, either due to industry modifications or as the result of implemented management regulations. Such gear modifications can be utilised to better target desired sizes of commercial species or to mitigate against bycatch issues. Most of the European lobster, Homarus gammarus, fishers use parlour pots and often utilise an escape gap to allow undersized animals and bycatch to exit. To quantify the gear selectivity of commercial parlour pots with and without escape gaps we utilise v-notching, as a novel approach to mark and recapture. Across both gear types, a total of 933 lobsters were notched (all sizes and both sexes) and 316 were recaught (34 %). A generalised linear model framework was applied to estimate selectivity which considered the interaction between size and sex of lobsters. Gear with escape gaps showed that males exhibit a dome-shaped selectivity curve, with the peak at 100-109 mm carapace length (CL) whereas females were less catchable for all size categories except for the smallest size class of 80-89 mm CL. Gear without escape gaps predominantly selected for small animals of both sexes (<90 mm) but, again, males exhibit a dome-shaped selectivity, with a peak at 80-89 mm (CL). This indicates that pots with escape gaps clearly select for legal-sized animals (>90 mm) for both sexes. This novel use of v-notching in markrelease-recapture surveys demonstrates the potential to quantify the efficacy of the gear used in the fleet and shows the scientific benefit of utilising current fishery management measures and fisher collaboration.

关键词： Gear selectivity Notching program generalized linear model Sex effect Crustacean fishery

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The Poisson-Weibull generalized linear model for analyzing motor vehicle crash data

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SAFETY SCIENCE 2013年 54卷 38-42页

作者： Cheng, Lingzi Geedipally, Srinivas Reddy Lord, Dominique Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77843 USA Texas Transportat Inst Arlington TX 76013 USA

Over the last 20-30 years, there has been a significant amount of tools and statistical methods that have been proposed for analyzing crash data. Yet, the Poisson-gamma (PG) is still the most commonly used and widely acceptable model. This paper documents the application of the Poisson-Weibull (PW) generalized linear model (GLM) for modeling motor vehicle crashes. The objectives of this study were to evaluate the application of the PW GLM for analyzing this kind of dataset and compare the results with the traditional PG model. To accomplish the objectives of the study, the modeling performance of the PW model was first examined using a simulated dataset and then several PW and PG GLMs were developed and compared using two observed crash datasets. The results of this study show that the PW GLM performs as well as the PG GLM in terms of goodness-of-fit statistics. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Poisson-Weibull Poisson-gamma generalized linear model Traffic crashes

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A p-step-ahead sequential adaptive algorithm for D-optimal nonlinear regression design

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STATISTICAL PAPERS 2024年第5期65卷 2811-2834页

作者： Freise, Fritjof Gaffke, Norbert Schwabe, Rainer Univ Vet Med Hannover Dept Biometry Epidemiol & Informat Proc D-30559 Hannover Germany Univ Magdeburg Fac Math D-39016 Magdeburg Germany

Under a nonlinear regression model with univariate response an algorithm for the generation of sequential adaptive designs is studied. At each stage, the current design is augmented by adding p design points where p is the dimension of the parameter of the model. The augmenting p points are such that, at the current parameter estimate, they constitute the locally D-optimal design within the set of all saturated designs. Two relevant subclasses of nonlinear regression models are focused on, which were considered in previous work of the authors on the adaptive Wynn algorithm: firstly, regression models satisfying the 'saturated identifiability condition' and, secondly, generalized linear models. Adaptive least squares estimators and adaptive maximum likelihood estimators in the algorithm are shown to be strongly consistent and asymptotically normal, under appropriate assumptions. For both model classes, if a condition of 'saturated D-optimality' is satisfied, the almost sure asymptotic D-optimality of the generated design sequence is implied by the strong consistency of the adaptive estimators employed by the algorithm. The condition states that there is a saturated design which is locally D-optimal at the true parameter point (in the class of all designs).

关键词： Asymptotic D-optimality Consistency of estimators Asymptotic normality generalized linear model

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Acute effects of air pollutants on influenza-like illness in Hangzhou, China

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SCIENTIFIC REPORTS 2025年第1期15卷 1-9页

作者： Lv, Ye Xu, Hong Sun, Zhou Liu, Muwen Xu, Shanshan Wang, Jing Li, Chaokang Ye, Hui Yang, Xuhui Hangzhou Ctr Dis Control & Prevent Hangzhou Hlth Supervis Inst Hangzhou Zhejiang Peoples R China Ecol & Environm Monitoring Ctr Hangzhou Hangzhou Zhejiang Peoples R China

At present, with increasing awareness of the relationship between respiratory disease and air pollution, it is critical to assess the environmental risk factors for influenza. This study aimed to estimate the associations between ambient air pollution and the number of influenza-like illness (ILI) cases in Hangzhou, China, from 2015 to 2021. Weekly meteorological data, including average ambient temperature and average relative humidity, from December 29, 2014 to January 2, 2022 were collected from the Hangzhou Meteorological Service Center, and air pollutants, including nitrogen dioxide (NO2), sulfur dioxide (SO2), ground-level ozone (O3), particulate matter (PM) with aerodynamic diameter <= 2.5 mu m (PM2.5), and PM with aerodynamic diameter <= 10 mu m (PM10), were collected from National Ambient Air Quality Automatic Monitoring Stations in Hangzhou. The number of weekly ILI cases was collected from 15 influenza surveillance sentinel hospitals in Hangzhou. A generalized linear model (GLM) with quasi-Poisson regression was adopted to estimate the association between air pollution and ILI. After adjusting for the effects of average temperature, relative humidity, and seasonal and long-term trends, PM2.5, PM10, NO2, and SO2 were found to be significantly associated with the number of ILI cases, with relative risk (RR) values of 1.018 (95% CI 1.001-1.036), 1.016 (1.005-1.028), 1.063 (1.067-1.364), and 1.207 (1.067-1.364), respectively. In the two-pollutant model, putting PM2.5, PM10, NO2, or SO2 into the model separately with O3 produced results similar to those of the single-pollutant model. PM2.5, PM10, and NO2 have statistical significance in cold seasons, with the RR values of 1.020 (95% CI 1.001-1.038), 1.012 (95% CI 1.000-1.024), and 1.060 (95% CI 1.031-1.090), respectively. In summary, our study found that most air pollutants (PM10, PM2.5, NO2, and SO2) have a significant association with the risk of ILI cases in Hangzhou. These findings can serve as a reference

关键词： Air pollution Influenza-like illness generalized linear model Environmental epidemiology

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Integrative subgroup analysis for high-dimensional mixed-type multi-response data

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TEST 2025年第1期34卷 151-197页

作者： Song, Shuyang Wu, Jiaqi Zhang, Weiping Univ Sci & Technol China Sch Management Hefei Peoples R China

Identifying subgroup structures presents an intriguing challenge in data analysis. Various methods have been proposed to divide the population into subgroups based on individual heterogeneity. However, these methods often fail to accommodate mixed multi-responses and high-dimensional covariates. This article considers the problem of high-dimensional mixed multi-response data with heterogeneity and sparsity. We introduce an integrative subgroup analysis approach with general linear models, accounting for heterogeneity through unobserved latent factors across different responses and sparsity due to high-dimensional covariates. Our approach automatically divides observations into subgroups while identifying significant covariates using non-convex penalty functions. We develop an algorithm that combines the alternating direction method of multipliers with the coordinate descent algorithm for implementation. Additionally, we establish the oracle property of the estimator, illustrating consistent identification of latent subgroups and significant covariates. The efficacy of our method is further validated through numerical simulations and a case study on a randomized clinical trial for buprenorphine maintenance treatment in opiate dependence.

关键词： Subgroup identification Variable selection generalized linear model ADMM

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Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models

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BIOSYSTEMS 2025年 247卷 105360页

作者： Garcia, Juan A. Bouchnita, Anass Univ Texas El Paso Dept Math Sci El Paso TX 79968 USA

The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of characteristics related to their shape, structure and contained fluid, which influences the diffusion of proteins and their interactions. To gain insights into the spatial effects shaping intracellular regulation, we apply machine learning (ML) to explore a previously developed spatial model of the epidermal growth factor receptor (EGFR) signaling. The model describes the reactions between molecular species inside of cells following the transient activation of EGF receptors. To train our ML models, we conduct 10,000 numerical simulations in parallel where we calculate the cumulative activation of molecules and transcription factors under various conditions such as different diffusion speeds, inactivation rates, and cell structures. We take advantage of the low computational cost of ML algorithms to investigate the effects of cell and nucleus sizes, the diffusion speed of proteins, and the inactivation rate of the Ras molecules on the activation strength of transcription factors. Our results suggest that the predictions by both neural networks and random forests yielded minimal mean square error (MSEs), while linear generalized models displayed a significantly larger MSE. The exploration of the surrogate models has shown that smaller cell and nucleus radii as well, larger diffusion coefficients, and reduced inactivation rates increase the activation of transcription factors. These results are confirmed by numerical simulations. Our ML algorithms can be readily incorporated within multiscale models of tumor growth to embed the spatial effects regulating intracellular pathways, enabling the use of complex cell models within multiscale models while reducing the computational cost.

关键词： Neural networks Random forests generalized linear model Brownian dynamics Discrete model Intracellular signaling

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Heterogeneous treatment effect estimation for observational data using model-based forests

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STATISTICAL METHODS IN MEDICAL RESEARCH 2024年第3期33卷 392-413页

作者： Dandl, Susanne Bender, Andreas Hothorn, Torsten Ludwig Maximilians Univ Munchen Inst Stat Munich Germany Munich Ctr Machine Learning MCML Munich Germany Univ Zurich Inst Epidemiol Biostat & Pravent Zurich Switzerland Univ Zurich Inst Epidemiol Biostat & Pravent Hirschengraben 84 CH-8001 Zurich Switzerland

The estimation of heterogeneous treatment effects has attracted considerable interest in many disciplines, most prominently in medicine and economics. Contemporary research has so far primarily focused on continuous and binary responses where heterogeneous treatment effects are traditionally estimated by a linear model, which allows the estimation of constant or heterogeneous effects even under certain model misspecifications. More complex models for survival, count, or ordinal outcomes require stricter assumptions to reliably estimate the treatment effect. Most importantly, the noncollapsibility issue necessitates the joint estimation of treatment and prognostic effects. model-based forests allow simultaneous estimation of covariate-dependent treatment and prognostic effects, but only for randomized trials. In this paper, we propose modifications to model-based forests to address the confounding issue in observational data. In particular, we evaluate an orthogonalization strategy originally proposed by Robinson (1988, Econometrica) in the context of model-based forests targeting heterogeneous treatment effect estimation in generalized linear models and transformation models. We found that this strategy reduces confounding effects in a simulated study with various outcome distributions. We demonstrate the practical aspects of heterogeneous treatment effect estimation for survival and ordinal outcomes by an assessment of the potentially heterogeneous effect of Riluzole on the progress of Amyotrophic Lateral Sclerosis.

关键词： Heterogeneous treatment effects personalized medicine random forest observational data censored survival data generalized linear model transformation model

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New Paradigm of Identifiable General-response Cognitive Diagnostic models: Beyond Categorical Data

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PSYCHOMETRIKA 2024年第4期89卷 1304-1336页

作者： Lee, Seunghyun Gu, Yuqi Columbia Univ New York NY USA

Cognitive diagnostic models (CDMs) are a popular family of discrete latent variable models that model students' mastery or deficiency of multiple fine-grained skills. CDMs have been most widely used to model categorical item response data such as binary or polytomous responses. With advances in technology and the emergence of varying test formats in modern educational assessments, new response types, including continuous responses such as response times, and count-valued responses from tests with repetitive tasks or eye-tracking sensors, have also become available. Variants of CDMs have been proposed recently for modeling such responses. However, whether these extended CDMs are identifiable and estimable is entirely unknown. We propose a very general cognitive diagnostic modeling framework for arbitrary types of multivariate responses with minimal assumptions, and establish identifiability in this general setting. Surprisingly, we prove that our general-response CDMs are identifiable under Q\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\textbf{Q}}$$\end{document}-matrix-based conditions similar to those for traditional categorical-response CDMs. Our conclusions set up a new paradigm of identifiable general-response CDMs. We propose an EM algorithm to efficiently estimate a broad class of exponential family-based general-response CDMs. We conduct simulation studies under various response types. The simulation results not only corroborate our identifiability theory, but also demonstrate the superior empirical performance of our estimation algorithms. We illustrate our methodology by applying it to a TIMSS 2019 response time dataset.

关键词： cognitive diagnostic model diagnostic classification model EM algorithm exponential family general responses generalized linear model identifiability Q-matrix

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generalized Varying Coefficient Mediation models

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COMMUNICATIONS IN MATHEMATICS AND STATISTICS 2024年 1-23页

作者： Liu, Jingyuan Liao, Yujie Li, Runze Xiamen Univ Fujian Key Lab Stat Wang Yanan Inst Studies Econ MOE Key Lab EconometrDept Stat & Data SciSch Eco Xiamen 361005 Fujian Peoples R China Penn State Univ Dept Stat University Pk PA 16802 USA

Motivated by an analysis of causal mechanism from economic stress to entrepreneurial withdrawals through depressed affect, we develop a two-layer generalized varying coefficient mediation model. This model captures the bridging effects of mediators that may vary with another variable, by treating them as smooth functions of this variable. It also allows various response types by introducing the generalized varying coefficient model in the first layer. The varying direct and indirect effects are estimated through spline expansion. The theoretical properties of the estimated direct and indirect coefficient functions including estimation biases, asymptotic distributions and so forth, are explored. Simulation studies validate the finite-sample performance of the proposed estimation method. A real data analysis based on the proposed model discovers some interesting behavioral economic phenomenon, that self-efficacy influences the deleterious impact of economic stress, both directly and indirectly through depressed affect, on business owners' withdrawal intentions.

关键词： Mediation analysis Varying coefficient model Direct and indirect effect generalized linear model

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