Journal of the Royal Statistical Society Series A-Statistics in Society最新文献

Model-robust standardization in stepped wedge cluster randomized trials. 阶梯形聚类随机试验模型稳健性标准化。

IF 1.9 3区数学

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2026-04-29 DOI: 10.1093/jrsssa/qnag056

Xi Fang, Xueqi Wang, Patrick J Heagerty, Bingkai Wang, Fan Li

{"title":"Model-robust standardization in stepped wedge cluster randomized trials.","authors":"Xi Fang, Xueqi Wang, Patrick J Heagerty, Bingkai Wang, Fan Li","doi":"10.1093/jrsssa/qnag056","DOIUrl":"10.1093/jrsssa/qnag056","url":null,"abstract":"Stepped-wedge cluster-randomized trials (SW-CRTs) are widely used in healthcare and implementation science, enabling all clusters to receive the intervention through a staggered rollout. Traditional model-based methods, including generalized estimating equations and mixed models, yield estimates that depend on implicit weighting schemes and parametric assumptions, and therefore may target ambiguous estimands under model misspecification. In this article, we propose a model-robust standardization framework for SW-CRTs that generalizes existing methods from parallel-arm CRTs to address informative sizes. We define causal estimands including horizontal-individual, horizontal-cluster, vertical-individual, and vertical-cluster average treatment effects under a super population framework and introduce a simple procedure that standardizes parametric and semiparametric working models for estimand-aligned analysis. For any specified working model, the resulting estimators remain consistent for their target estimands even if the working regression model is misspecified; moreover, their efficiency improves as the working model more closely approximates the true data-generating process. We evaluate the finite-sample properties of our proposed estimators through extensive simulations. The proposed methods are implemented in the MRStdLCRT R package on CRAN. Finally, we illustrate the application of our methods through reanalyses of two real-world SW-CRTs.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13401457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148594043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incorporating external risk information with the Cox model under population heterogeneity: applications to trans-ancestry polygenic hazard scores. 将外部风险信息与种群异质性下的Cox模型相结合：跨祖先多基因风险评分的应用。

IF 1.9 3区数学

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2026-04-02 DOI: 10.1093/jrsssa/qnag042

Di Wang, Wen Ye, Ji Zhu, Gongjun Xu, Weijing Tang, Matthew Zawistowski, Lars G Fritsche, Kevin He

{"title":"Incorporating external risk information with the Cox model under population heterogeneity: applications to trans-ancestry polygenic hazard scores.","authors":"Di Wang, Wen Ye, Ji Zhu, Gongjun Xu, Weijing Tang, Matthew Zawistowski, Lars G Fritsche, Kevin He","doi":"10.1093/jrsssa/qnag042","DOIUrl":"10.1093/jrsssa/qnag042","url":null,"abstract":"Polygenic hazard scores (PHS) designed for European ancestry (EUR) individuals provide ample information regarding risk discrimination. Incorporating such information can improve the performance of risk discrimination in the target small-sized non-EUR cohort. However, given that external EUR-based PHS and internal individual-level data come from different populations, ignoring population heterogeneity can introduce substantial bias. In this article, we develop a Kullback-Leibler-based Cox model (CoxKL) to integrate internal individual-level time-to-event data with external risk scores derived from published prediction models. Partial-likelihood-based KL information is utilized to measure the discrepancy between the external risk information and the internal data. We establish the asymptotic properties of the CoxKL estimator. Simulation studies show that the integration model by the proposed CoxKL method achieves improved estimation efficiency and prediction accuracy. We apply the proposed method to develop transancestry PHS models for prostate cancer and breast cancer and find that integrating EUR-based PHS with internal genotype data of African ancestry individuals yields considerable improvement on the cancer risk discrimination.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13263114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148254141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian mixture model approach to examining neighbourhood social determinants of health in endometrial cancer care in Massachusetts. 一个贝叶斯混合模型方法来检查在马萨诸塞州子宫内膜癌护理健康的社区社会决定因素。

IF 1.9 3区数学

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2026-03-30 DOI: 10.1093/jrsssa/qnag047

Carmen B Rodríguez, Stephanie M Wu, Stephanie Alimena, Alecia J McGregor, Briana J K Stephenson

{"title":"A Bayesian mixture model approach to examining neighbourhood social determinants of health in endometrial cancer care in Massachusetts.","authors":"Carmen B Rodríguez, Stephanie M Wu, Stephanie Alimena, Alecia J McGregor, Briana J K Stephenson","doi":"10.1093/jrsssa/qnag047","DOIUrl":"10.1093/jrsssa/qnag047","url":null,"abstract":"Many studies examine social determinants of health (SDoH) in isolation, overlooking their interconnected nature. We used a multifactorial approach to construct a neighbourhood-level measure that explores how SDoH jointly impact care received for endometrial cancer (EC) patients in Massachusetts (MA). Using 2015-2019 American Community Survey data, we applied a Bayesian multivariate Bernoulli mixture model to identify MA neighbourhoods with similar SDoH characteristics. Five neighbourhood SDoH (NSDoH) profiles were derived and characterized: (1) advantaged non-Hispanic White; (2) disadvantaged racially/ethnically diverse, more renter-occupied housing with limited English proficiency; (3) working class, lower educational attainment; (4) racially/ethnically diverse and greater economic security and educational attainment; and (5) racially/ethnically diverse, more renter-occupied housing with limited English proficiency. We assigned these profiles to EC patients in the Massachusetts Cancer Registry and used them as the main exposure in a Bayesian logistic regression, adjusting for sociodemographic and clinical characteristics. NSDoH profiles were not associated with optimal care; however, patients in all other profiles had lower odds compared to Profile 1. Our findings demonstrate how a flexible model-based clustering approach captures the multidimensional nature of NSDoH in an interpretable way and may support targeted public health interventions based on neighbourhood-specific social factors to improve healthcare delivery.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13186426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147984153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multivariate mixed models accounting for don't know options in ordinal data. 多元混合模型考虑了有序数据中的未知选项。

IF 1.9 3区数学

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2025-12-26 DOI: 10.1093/jrsssa/qnaf197

Ralitza Gueorguieva, Maria Iannario

{"title":"Multivariate mixed models accounting for don't know options in ordinal data.","authors":"Ralitza Gueorguieva, Maria Iannario","doi":"10.1093/jrsssa/qnaf197","DOIUrl":"10.1093/jrsssa/qnaf197","url":null,"abstract":"Multivariate ordinal data characterised by between-subject heterogeneity or different response styles are prevalent in surveys and other observational studies. It is especially common in surveys designed to assess individual perceptions or knowledge to include a 'don't know' option on some or all survey questions. The latter makes the scales partially ordinal, which precludes the use of well-established models for ordinal data, whereas models for nominal data are inefficient and difficult to interpret. Ignoring the 'don't know' options may introduce bias as the subset of individuals who choose to provide ratings may not be representative of the population of interest. The suggested solution in this manuscript involves jointly modeling the selection of 'don't know' options and the ordinal ratings on multiple variables. The proposed multivariate mixed models are flexible, allow for heterogeneity in responses, response styles, and assessment of the effects of covariates on the ordinal ratings and on choosing the 'don't know' options. Likelihood-based inference and model comparisons are performed. Two case studies: one on financial risk perceptions and one on knowledge about the addictiveness of tobacco products are used for motivation and illustration. A simulation demonstrates that the proposed approach yields unbiased and efficient estimates. The results are straightforward to interpret, effectively capturing the complexity inherent in the data. This makes the proposed models particularly well-suited for analyzing partially ordinal ratings in social and behavioral surveys, providing a robust and reliable framework for such contexts.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12778354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

COADVISE: covariate adjustment with variable selection in randomized controlled trials. COADVISE：随机对照试验中带有变量选择的协变量调整。

IF 1.9 3区数学

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2025-11-04 eCollection Date: 2026-07-01 DOI: 10.1093/jrsssa/qnaf171

Yi Liu, Ke Zhu, Larry Han, Shu Yang

{"title":"COADVISE: covariate adjustment with variable selection in randomized controlled trials.","authors":"Yi Liu, Ke Zhu, Larry Han, Shu Yang","doi":"10.1093/jrsssa/qnaf171","DOIUrl":"10.1093/jrsssa/qnaf171","url":null,"abstract":"Adjusting for covariates in randomized controlled trials can enhance the credibility and efficiency of treatment effect estimation. However, handling numerous covariates and their complex (nonlinear) transformations poses a challenge. Motivated by the case study of the Best Apnea Interventions for Research (BestAIR) trial data from the National Sleep Research Resource (NSRR), where the number of covariates ( <math><mi>p</mi> <mo>=</mo> <mn>114</mn></math> ) is comparable to the sample size ( <math><mi>N</mi> <mo>=</mo> <mn>196</mn></math> ), we propose a principled covariate adjustment with variable selection (COADVISE) framework. COADVISE enables variable selection for covariates most relevant to the outcome while accommodating both linear and nonlinear adjustments. This framework ensures consistent estimates with improved efficiency over unadjusted estimators and provides robust variance estimation, even under outcome model misspecification. We demonstrate efficiency gains through theoretical analysis, extensive simulations, and a re-analysis of the BestAIR trial data to compare alternative variable selection strategies, offering cautionary recommendations. A user-friendly R package, Coadvise, is available to facilitate practical implementation.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"189 3","pages":"1909-1945"},"PeriodicalIF":1.9,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13366223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148450450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical latent class models for mortality surveillance using partially verified verbal autopsies. 使用部分验证的尸检进行死亡率监测的层次潜类模型。

IF 1.9 3区数学

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2025-10-21 eCollection Date: 2026-07-01 DOI: 10.1093/jrsssa/qnaf164

Yu Zhu, Zehang Richard Li

{"title":"Hierarchical latent class models for mortality surveillance using partially verified verbal autopsies.","authors":"Yu Zhu, Zehang Richard Li","doi":"10.1093/jrsssa/qnaf164","DOIUrl":"10.1093/jrsssa/qnaf164","url":null,"abstract":"Monitoring cause-of-death data is an important part of understanding disease burdens and effects of public health interventions. Verbal autopsy (VA) is a well-established method for gathering information about deaths outside of hospitals by conducting an interview to caregivers of a deceased person. It is usually the only tool for cause-of-death surveillance in low-resource settings. A critical limitation with current practices of VA analysis is that all algorithms require either domain knowledge about symptom-cause relationships or large labelled datasets for model training. Therefore, they cannot be easily adopted during public health emergencies when new diseases emerge with rapidly evolving epidemiological patterns. In this paper, we consider estimating the fraction of deaths due to an emerging disease. We develop a novel Bayesian framework using hierarchical latent class models to account for the informative cause-of-death verification process. Our model flexibly captures the joint distribution of symptoms and how they change over time in different sub-populations. We also propose structured priors to improve the precision of the cause-specific mortality estimates for small sub-populations. Our model is motivated by mortality surveillance of COVID-19 related deaths in low-resource settings. We apply our method to a dataset that includes suspected COVID-19 related deaths in Brazil in 2021.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"189 3","pages":"1838-1855"},"PeriodicalIF":1.9,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13366219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Survey Inference in Two-phase Designs Using Bayesian Machine Learning. 利用贝叶斯机器学习改进两阶段设计中的调查推断。

IF 1.6 3区数学

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2025-09-08 DOI: 10.1093/jrsssa/qnaf136

Xinru Wang, Anyu Zhu, Lauren Kennedy, Abigail Greenleaf, Qixuan Chen

{"title":"Improving Survey Inference in Two-phase Designs Using Bayesian Machine Learning.","authors":"Xinru Wang, Anyu Zhu, Lauren Kennedy, Abigail Greenleaf, Qixuan Chen","doi":"10.1093/jrsssa/qnaf136","DOIUrl":"10.1093/jrsssa/qnaf136","url":null,"abstract":"The two-phase sampling design is a cost-effective strategy widely used in public health research. Analyzing the Phase II sample often involves creating subsample-specific weights. However, these weights can be highly variable, leading to unstable weighted analyses. Alternatively, the rich data collected during the first phase can be leveraged to improve survey inference for the Phase II sample. In this paper, we propose a Bayesian tree-based multiple imputation (MI) approach for estimating population means using the Phase II sample, where the parent survey was conducted using a complex survey design. The design features of the parent survey, such as strata and clusters, are incorporated into the tree-based imputation models. Through simulations, we demonstrate that the tree-based MI method outperforms traditional weighted estimators, yielding smaller bias, lower root mean squared error, and narrower 95% confidence intervals, with coverage rates closer to the nominal level. Furthermore, we show that Rubin's variance estimation method provides valid statistical inference for population mean estimation in our setting. We illustrate the application of the proposed tree-based MI method using data from a cellphone survey on COVID-19 vaccination in Uganda, which represents a subcohort sample drawn from the 2020 Uganda Population-based HIV Impact Assessment Survey.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12905726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Statistical modelling of networked evolutionary public goods games. 网络进化公共产品博弈的统计建模。

IF 1.9 3区数学

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2025-09-05 eCollection Date: 2026-07-01 DOI: 10.1093/jrsssa/qnaf133

Hiroyasu Ando, Akihiro Nishi, Mark S Handcock

{"title":"Statistical modelling of networked evolutionary public goods games.","authors":"Hiroyasu Ando, Akihiro Nishi, Mark S Handcock","doi":"10.1093/jrsssa/qnaf133","DOIUrl":"10.1093/jrsssa/qnaf133","url":null,"abstract":"Repeated small dynamic networks are integral to studies in evolutionary game theory, where networked public goods games offer novel insights into human behaviours. Building on these findings, it is necessary to develop a statistical model that effectively captures dependencies across multiple small dynamic networks. While separable temporal exponential-family random graph models (STERGMs) have demonstrated success in modelling a large single dynamic network, their application to multiple small dynamic networks with less than 10 actors, remains unexplored. In this study, we extend the STERGM framework to accommodate multiple small dynamic networks, offering an approach to analysing such systems. Taking advantage of the small network sizes, our proposed approach improves accuracy in statistical inference through direct computation, unlike conventional approaches that rely on Markov Chain Monte Carlo methods. We demonstrate the validity of this framework through the analysis of a networked public goods experiment into individual decision-making about cooperation and defection. The resulting statistical inference uncovers insights into the dynamics of social dilemmas, showcasing the effectiveness, and robustness of this modelling and approach.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"189 3","pages":"1634-1649"},"PeriodicalIF":1.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13366220/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapping socio-economic status using mixed data: a hierarchical Bayesian approach. 利用混合数据绘制社会经济地位图：分层贝叶斯方法。

IF 1.5 3区数学

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2025-07-14 Epub Date: 2024-08-20 DOI: 10.1093/jrsssa/qnae080

Gabrielle Virgili-Gervais, Alexandra M Schmidt, Honor Bixby, Alicia Cavanaugh, George Owusu, Samuel Agyei-Mensah, Brian Robinson, Jill Baumgartner

{"title":"Mapping socio-economic status using mixed data: a hierarchical Bayesian approach.","authors":"Gabrielle Virgili-Gervais, Alexandra M Schmidt, Honor Bixby, Alicia Cavanaugh, George Owusu, Samuel Agyei-Mensah, Brian Robinson, Jill Baumgartner","doi":"10.1093/jrsssa/qnae080","DOIUrl":"10.1093/jrsssa/qnae080","url":null,"abstract":"We propose a Bayesian hierarchical model to estimate a socio-economic status (SES) index based on mixed dichotomous and continuous variables. In particular, we extend Quinn's ([2004]. Bayesian factor analysis for mixed ordinal and continuous responses. Political Analysis, 12(4), 338-353. https://doi.org/10.1093/pan/mph022) and Schliep and Hoeting's ([2013]. Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data. Journal of Agricultural, Biological, and Environmental Statistics, 18(4), 492-513. https://doi.org/10.1007/s13253-013-0136-z) factor analysis models for mixed dichotomous and continuous variables by allowing a spatial hierarchical structure of key parameters of the model. Unlike most SES assessment models proposed in the literature, the hierarchical nature of this model enables the use of census observations at the household level without needing to aggregate any information a priori. Therefore, it better accommodates the variability of the SES between census tracts and the number of households per area. The proposed model is used in the estimation of a socio-economic index using 10% of the 2010 Ghana census in the Greater Accra Metropolitan area. Out of the 20 observed variables, the number of people per room, access to water piping and flushable toilets differentiated high and low SES areas the best.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":" ","pages":"859-874"},"PeriodicalIF":1.5,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7617442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using case description information to reduce sensitivity to bias for the attributable fraction among the exposed. 使用病例描述信息来降低暴露者中可归因部分的偏倚敏感性。

IF 1.9 3区数学

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2025-07-01 eCollection Date: 2026-07-01 DOI: 10.1093/jrsssa/qnaf066

Kan Chen, Jing Cheng, M Elizabeth Halloran, Dylan S Small

引用次数: 0