Statistics in Medicine最新文献

{"title":"An Efficient Estimation Method for Additive Subdistribution Hazards Model With Left-Truncated Competing Risks Data Under the Case-Cohort Study Design.","authors":"Xi Fang, Kwang Woo Ahn, Jianwen Cai, Soyoung Kim","doi":"10.1002/sim.70183","DOIUrl":"https://doi.org/10.1002/sim.70183","url":null,"abstract":"<p><p>The case-cohort study design provides a cost-effective approach for large cohort studies with competing risks outcomes. The additive subdistribution hazards model assesses direct covariate effects on cumulative incidence when investigating risk differences among different groups instead of relative risk. The presence of left truncation, which commonly occurs in biomedical studies, introduces additional complexities to the analysis. Existing inverse-probability-weighting methods for case-cohort studies on competing risks are inefficient in parameter estimation of coefficients for baseline covariates. In addition, their methods do not address left truncation. To improve the efficiency of parameter estimation of coefficients for baseline covariates and account for left-truncated competing risks data, we propose an augmented-inverse-probability-weighted estimating equation for left-truncated competing risks data with additive subdistribution models under the case-cohort study design. For multiple case-cohort studies, we further improve parameter estimation efficiency by incorporating extra information from the other causes. We study large sample properties of the proposed estimators. Simulation studies demonstrate the unbiasedness of our proposed estimator and the superior efficiency in regression parameter estimation. We apply the proposed methods to analyze data from the Atherosclerosis Risk in Communities study.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70183"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Maximum Likelihood Method for High-Dimensional Structural Equation Modeling.","authors":"Alexander Quinter, Xianming Tan, Donglin Zeng, Joseph G Ibrahim","doi":"10.1002/sim.70171","DOIUrl":"https://doi.org/10.1002/sim.70171","url":null,"abstract":"<p><p>Factor analysis provides an intuitive approach for dimension reduction when working with big data, allowing researchers to represent an extensive number of correlated variables via a subset of underlying latent factors. Traditional methods of factor analysis, such as Structural Equation Modeling (SEM) and factor regression, lack properties desirable for analyzing big data, such as the ability to handle high-dimensionality or the ability to enforce sparsity on the estimates of the factor loading matrices. These methods also assume that the number of latent constructs is known beforehand, a problem unique to factor analysis that often goes unaddressed or overlooked, with ad hoc methods being the most common ways to deal with such a fundamental question. Although recent developments in the literature have attempted to remedy these issues, particularly with regard to expanding SEM to high-dimensional and sparse applications, there is a noticeable lack of such methods that do so using likelihood theory. To rectify this shortcoming, we propose a new SEM-based method for estimation that utilizes maximum likelihood theory while simultaneously addressing some of the most common problems associated with big data. We substantiate our method through simulation studies, indicating that the proposed method can correctly identify the latent factors underlying the independent and dependent sets of variables, while also accurately estimating the entries of and enforcing sparsity upon the factor loading matrix estimates. We apply this method to the COVIDiSTRESS Global Survey dataset, a global survey collected to further our understanding of how the COVID-19 pandemic affected the human experience. Doing so demonstrates the performance of the model while simultaneously identifying the latent constructs intrinsic to the data.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70171"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural Effects in the Presence of an Intermediate Confounder: Evaluation of Pragmatic Estimation Strategies With an Emphasis on the Relationship Between Natural and Interventional Effects.","authors":"Jesse Gervais, Geneviève Lefebvre, Erica E M Moodie","doi":"10.1002/sim.70038","DOIUrl":"10.1002/sim.70038","url":null,"abstract":"<p><p>Mediation analysis using the so-called natural effects is an essential tool to uncover causal pathways between an exposure and an outcome. However, natural effects are not generally identified in the presence of an intermediate confounder ( <math> <semantics><mrow><mi>L</mi></mrow> <annotation>$$ L $$</annotation></semantics> </math> ), a situation that arguably arises frequently in practice. Three pragmatic approaches can be used to estimate natural effects when such a confounder <math> <semantics><mrow><mi>L</mi></mrow> <annotation>$$ L $$</annotation></semantics> </math> is present: Natural effects estimators that omit <math> <semantics><mrow><mi>L</mi></mrow> <annotation>$$ L $$</annotation></semantics> </math> , natural effects estimators that consider <math> <semantics><mrow><mi>L</mi></mrow> <annotation>$$ L $$</annotation></semantics> </math> as a pre-exposure confounder, or interventional effects estimators. Interventional effects are analogous to natural effects, but remain identified when <math> <semantics><mrow><mi>L</mi></mrow> <annotation>$$ L $$</annotation></semantics> </math> is present. The goal of this study was two-fold: (1) to assess the extent to which natural and interventional estimands differ under a variety of data-generating mechanisms with intermediate confounding and (2) using simulations, to investigate the corresponding performance of the three aforementioned strategies to estimate natural effects. In the continuous outcome case, using interventional effects estimators was found to be a better analytic strategy for estimating natural effects than using standard natural effects estimators when the interaction term between <math> <semantics><mrow><mi>L</mi></mrow> <annotation>$$ L $$</annotation></semantics> </math> and <math> <semantics><mrow><mi>M</mi></mrow> <annotation>$$ M $$</annotation></semantics> </math> in the outcome model was null or moderate in comparison to the other parameters. However, the performance of interventional effects declined as the <math> <semantics><mrow><mi>L</mi></mrow> <annotation>$$ L $$</annotation></semantics> </math> - <math> <semantics><mrow><mi>M</mi></mrow> <annotation>$$ M $$</annotation></semantics> </math> interaction was increased. In the binary outcome case, the three estimation strategies yielded more similar results than in the continuous outcome case. The difference between the three analytic strategies is illustrated using data from the World Value Survey.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70038"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Cross-Validated Targeted Maximum Likelihood Estimation.","authors":"Matthew J Smith, Rachael V Phillips, Camille Maringe, Miguel Angel Luque-Fernandez","doi":"10.1002/sim.70185","DOIUrl":"10.1002/sim.70185","url":null,"abstract":"<p><strong>Background: </strong>Advanced methods for causal inference, such as targeted maximum likelihood estimation (TMLE), require specific convergence rates and the Donsker class condition for valid statistical estimation and inference. In situations where there is no differentiability due to data sparsity or near-positivity violations, the Donsker class condition is violated. In such instances, the bias of the targeted estimand is inflated, and its variance is anti-conservative, leading to poor coverage. Cross-validation of the TMLE algorithm (CVTMLE) is a straightforward, yet effective way to ensure efficiency, especially in settings where the Donsker class condition is violated, such as random or near-positivity violations. We aim to investigate the performance of CVTMLE compared to TMLE in various settings.</p><p><strong>Methods: </strong>We utilized the data-generating mechanism described in Leger et al. (2022) to run a Monte Carlo experiment under different Donsker class violations. Then, we evaluated the respective statistical performances of TMLE and CVTMLE with different super learner libraries, with and without regression tree methods.</p><p><strong>Results: </strong>We found that CVTMLE vastly improves confidence interval coverage without adversely affecting bias, particularly in settings with small sample sizes and near-positivity violations. Furthermore, incorporating regression trees using standard TMLE with ensemble super learner-based initial estimates increases bias and reduces variance, leading to invalid statistical inference.</p><p><strong>Conclusions: </strong>We show through simulations that CVTMLE is much less sensitive to the choice of the super learner library and thereby provides better estimation and inference in cases where the super learner library uses more flexible candidates and is prone to overfitting.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70185"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A General Linear Mixed Effect Model to Infer Biomarker Correlations by Bridging Retrospectively Measured Data Across Multiple Studies.","authors":"Chengjie Xiong, Ruijin Lu, David Wolk, Leslie M Shaw, Carey E Gleason, Sterling C Johnson, Folasade Agboola, Suzanne E Schindler, John C Morris, Jingqin Luo","doi":"10.1002/sim.70200","DOIUrl":"https://doi.org/10.1002/sim.70200","url":null,"abstract":"<p><p>A major challenge in biomedical research is that large sample sizes are necessary for sufficient statistical power to detect subtle but potentially important associations between biomarkers and clinical outcomes. Large sample sizes can be achieved by combining biomarker data from multiple studies, but because fluid biomarker platforms and imaging protocols often vary across studies, data from different studies must be bridged or harmonized. We conceptualize that, for a biomarker measured by different studies, a true and latent biomarker exists and underlies the different versions of the observed biomarker through a measurement error model. We then examine the true biological correlation of the latent biomarker with a standard clinical outcome by leveraging biomarker values from a subset of \"bridging\" samples or scans across studies. Because the true biological correlation with the clinical outcome is related to the correlations of the observed versions of the biomarker with the same clinical outcome and the intraclass correlation coefficient (ICC) of the biomarker across studies, we propose a general linear mixed effects model to estimate the true biological correlation by integrating these correlations estimated across the studies and the bridging cohorts. Our proposed model accounts for study heterogeneity through a random effect and allows both study-specific and the test-retest biomarker data in a joint model to estimate and infer on the true biological correlation. We apply the model to a real world multi-center biomarker study in Alzheimer disease to correlate concentrations of cerebrospinal fluid biomarkers with a standard functional and cognitive outcome. Our simulations and real world applications indicate that the proposed meta-analytic model leads to a bias of no more than 0.03 in the estimated biological correlation of a biomarker with a clinical outcome, even with small to mediocre ICC. When the ICC is large, only 10% of bridging samples may be needed to obtain unbiased estimates to the correlation with close to the nominal level of coverage from the proposed 95% CI estimates. Our proposed methodologies hence provide a novel approach to harmonize retrospectively obtained biomarker data across studies, offer guidance on size of the bridging samples when ICCs are known, and may also be used in a single study to account for batch effects.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70200"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144675695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Analysis of N-Of-1 Trials That Incorporate Sequential Monitoring.","authors":"Shu Jiang, Steven E Arnold, Rebecca A Betensky","doi":"10.1002/sim.70177","DOIUrl":"10.1002/sim.70177","url":null,"abstract":"<p><p>For many diseases and disorders, such as Alzheimer's disease, patients demonstrate considerable heterogeneity in their responses to treatment interventions. One treatment may be most effective for some patients, while another may be most effective for others, and neither may be effective for another subset of patients. This potentially renders the conventional parallel group design highly inefficient. An attractive alternative is the N-of-1 design, also called the multi-crossover randomized controlled trial. In this design, each participant serves as their own control in a series of randomized blocks of treatment assignments. We propose novel designs for both the single-person and multi-person N-of-1 trials that employ sequential monitoring. In particular, we allow for early stopping for a single participant as soon as there is sufficient evidence of a preferred treatment for them, and early stopping for the group of participants as soon as there is sufficient evidence of a preferred treatment for the population of patients. We provide sample size calculations and decision rules for terminating the trial early and illustrate their properties in simulation studies. We apply our proposed methods to N-of-1 studies of brain tumor excisions and of methylphenidate in mild cognitive impairment.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70177"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144675699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Robust Transfer Learning for High-Dimensional GLM Using <ns0:math> <ns0:semantics><ns0:mrow><ns0:mi>γ</ns0:mi></ns0:mrow> <ns0:annotation>$$ gamma $$</ns0:annotation></ns0:semantics> </ns0:math> -Divergence With Applications to Cancer Genomics.","authors":"Fuzhi Xu, Shuangge Ma, Qingzhao Zhang, Yaqing Xu","doi":"10.1002/sim.70170","DOIUrl":"https://doi.org/10.1002/sim.70170","url":null,"abstract":"<p><p>In the analysis of complex diseases, high-dimensional profiling data is important for assessing risks and detecting biomarkers. With the increasing accessibility of cancer genomic data, the sample sizes remain limited in most studies. Hence, borrowing information from additional data sources is thus desirable to improve estimation and prediction. Transfer learning has been demonstrated to be flexible and effective in boosting modeling performance with a record in biomedical applications. In practice, outliers and even data contamination often occur. However, existing transfer learning methods often lack robustness to outliers and data contamination, issues commonly observed in real-world biomedical data. In this study, we propose a robust transfer learning approach based on the minimum <math> <semantics><mrow><mi>γ</mi></mrow> <annotation>$$ gamma $$</annotation></semantics> </math> -divergence under a generalized linear model (GLM) framework for high-dimensional data. Our method incorporates a data-driven source detection scheme that automatically identifies informative sources while mitigating the risk of negative transfer. We establish rigorous theoretical results, including consistency and high-dimensional estimation error bounds, ensuring robustness and reliable performance. A computationally efficient algorithm is developed based on proximal gradient descent to facilitate both the transfer and debiasing steps. Simulation demonstrates the superior and competitive performance of the proposed approach in selection and prediction/classification. We further validate its practical utility by analyzing data on breast cancer and glioblastoma, showcasing the method's effectiveness in real-world high-dimensional settings.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70170"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast Variational Bayesian Inference for Correlated Survival Data: An Application to Invasive Mechanical Ventilation Duration Analysis.","authors":"Chengqian Xian, Camila P E de Souza, Wenqing He, Felipe F Rodrigues, Renfang Tian","doi":"10.1002/sim.70198","DOIUrl":"10.1002/sim.70198","url":null,"abstract":"<p><p>Correlated survival data are prevalent in various clinical settings and have been extensively discussed in the literature. A common example is clustered survival data, where survival times are associated due to shared characteristics within clusters. In our study, we analyze invasive mechanical ventilation data collected from multiple intensive care units (ICUs) across Ontario, Canada. Patients within the same ICU exhibit similarities in clinical profiles and mechanical ventilation settings, leading to a correlation in their ventilation durations. To address this association, we introduce a shared frailty log-logistic accelerated failure time model that accounts for intra-cluster correlation through a cluster-specific random intercept. We present a novel, fast variational Bayes (VB) algorithm for parameter inference and evaluate its performance using simulation studies varying the number of clusters and their sizes. We further compare the performance of our proposed VB algorithm with the h-likelihood method and a Markov Chain Monte Carlo (MCMC) algorithm. The proposed algorithm delivers satisfactory results and demonstrates computational efficiency over the MCMC algorithm. We apply our method to ICU ventilation data from Ontario to investigate the ICU-site random effect on ventilation duration.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70198"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144708812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subgroup Testing in the Change-Plane Cox Model.","authors":"Xiao Zhang, Panpan Ren, Xingjie Shi, Shuangge Ma, Xu Liu","doi":"10.1002/sim.70179","DOIUrl":"10.1002/sim.70179","url":null,"abstract":"<p><p>Survival outcomes are frequently observed in numerous biomedical and epidemiological studies. The impact of treatment on these outcomes may vary across subgroups characterized by other covariates, for example, immune checkpoint blockade therapy may have different effects on the survival of solid tumor patients based on their tumor mutational burden. In such scenarios, change-plane Cox models provide a suitable approach to identify subgroups that exhibit an improved treatment effect in the analysis of survival data. While some literature is available for testing the presence of a change plane in these models, the existing methods primarily rely on the score test, which has limited power in small sample situations. In this paper, we introduce a novel method based on the likelihood ratio test to enhance the power. The asymptotic distributions of the proposed test statistic under both the null and local alternative hypotheses are established. Furthermore, the finite sample performance of the proposed approach is comprehensively evaluated through extensive simulation studies. Finally, the proposed test is applied to analyze nonsmall cell lung cancer data, further demonstrating its practical utility.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70179"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporation of Patient and Public Involvement in Statistical Methodology Research: Summary of Workshop Proceedings.","authors":"Aiden Smith, Hannah Worboys, Samina Begum, Derrick Bennett, Jonathan Broomfield, Suzie Cro, Laura Evans-Hill, Justin Greenwood, Ania Henley, Mary Mancini, Kara-Louise Royle, Helen Saul, Jamie Sergeant, Derek Stewart, Freya Tyrer, James Wason, Christopher Yau, Laura J Gray","doi":"10.1002/sim.70159","DOIUrl":"10.1002/sim.70159","url":null,"abstract":"<p><p>Patient and Public Involvement (PPI) is well-established in applied health research but remains under utilised in statistical methodology research due to perceived irrelevance and communication challenges. This paper summarises a one-day workshop held in February 2024 in Leicester, organised by the University of Leicester and the NIHR Statistics Group, aimed at addressing barriers to meaningful PPI in statistical methodology. The workshop brought together statisticians and experienced public contributors to discuss strategies, share case studies, and offer practical guidance on conducting effective PPI. Key barriers identified included: (1) uncertainty about the relevance of PPI in methodology-focused research; (2) public contributors' anxiety over mathematical complexity; and (3) mismatched expectations due to different backgrounds in applied versus methodological research. Case studies showcased how PPI led to improved model structures, identification of data issues, and enhanced study materials. The importance of communication was a recurrent theme, with recommendations including use of plain English, regular updates, and visual storytelling tools. Feedback from attendees indicated increased confidence and motivation to engage in PPI. Public contributors emphasised the need for respectful, non-patronising interactions and flexible roles within projects. Recommendations include managing expectations, enhancing accessibility, co-developing materials, and fostering diversity among contributors. This paper highlights the need for tailored strategies to integrate PPI into statistical methodology, including the development of resources (e.g., glossaries, animations) and further case study collection. Future work will focus on expanding these resources, addressing challenges of equity and inclusion, and supporting PPI in complex methodological areas like simulation and model development.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 15-17","pages":"e70159"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12261386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}