Statistical Methods in Medical Research最新文献

Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs. 使用基于神经网络的建模和复杂的调查设计在美国人群中筛查糖尿病。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-07 DOI: 10.1177/09622802261442893

Marcos Matabuena, Juan C Vidal, Rahul Ghosal, Jukka-Pekka Onnela

{"title":"Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs.","authors":"Marcos Matabuena, Juan C Vidal, Rahul Ghosal, Jukka-Pekka Onnela","doi":"10.1177/09622802261442893","DOIUrl":"https://doi.org/10.1177/09622802261442893","url":null,"abstract":"Complex survey designs are widely used in medical cohort studies. Developing risk score models that adequately account for the sampling design is essential to minimize selection bias and obtain representative population estimates. This work addresses three complementary objectives. First, we propose a general predictive framework for regression and classification tasks that utilizes neural networks to incorporate survey weights into the model estimation process. Second, we introduce a procedure for quantifying prediction uncertainty based on conformal inference, adapted to the characteristics of complex survey data. Third, we demonstrate the application of the proposed methodology in a case study assessing the risk of diabetes mellitus in the US population, using the NHANES 2011-2014 cohort. The empirical results show that models of varying complexity, each using different sets of predictors, achieve different trade-offs between predictive performance and economic cost while maintaining generalizability at the population level. Although the case study focuses on diabetes, the proposed framework is directly applicable to the development of clinical prediction models for other diseases and complex survey datasets. All software and data used in this study are publicly available on GitHub.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261442893"},"PeriodicalIF":1.9,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A double-semiparametric approach for extending mixture cure models with interval-censored data. 带区间截尾数据的混合固化模型扩展的双半参数方法。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-29 DOI: 10.1177/09622802261442911

Xiaoyu Liu, Zsolt Szabo, Liming Xiang

{"title":"A double-semiparametric approach for extending mixture cure models with interval-censored data.","authors":"Xiaoyu Liu, Zsolt Szabo, Liming Xiang","doi":"10.1177/09622802261442911","DOIUrl":"https://doi.org/10.1177/09622802261442911","url":null,"abstract":"Mixture cure models (MCMs) have become a valuable tool for analyzing failure time data in settings where a subset of individuals is considered to be \"cured\" or no longer experience the failure event of interest. Under the MCM framework, the latency component typically postulates a semiparametric survival model for the failure times of uncured individuals, while the incidence component models the probability of being uncured using logistic regression. However, in many practical applications, assuming linear covariate effects or imposing a fully parametric form on the incidence may be unrealistic, given that cure status is inherently unobserved for right-censored individuals. We introduce a more general mixture cure model for interval-censored data by incorporating nonparametric covariate effects in the incidence component. Our method allows semiparametric frameworks for both components of the mixture cure model, offering flexibility in capturing complex relationships between susceptibility and risk factors while preserving interpretability. We develop a spline-based sieve maximum likelihood estimator for both the model parameters and the unknown functions, and establish its desirable asymptotic properties. The finite-sample performance and practical utility of the proposed method are demonstrated through a simulation study and an analysis of data from a cardiac allograft vasculopathy study, respectively.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261442911"},"PeriodicalIF":1.9,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When randomization is not random: Allocation bias in small sample, group sequential randomized clinical trials. 当随机化不是随机化时：在小样本、组序随机临床试验中的分配偏倚。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-29 DOI: 10.1177/09622802261442914

Daniel Bodden, Ralf-Dieter Hilgers, Franz König

{"title":"When randomization is not random: Allocation bias in small sample, group sequential randomized clinical trials.","authors":"Daniel Bodden, Ralf-Dieter Hilgers, Franz König","doi":"10.1177/09622802261442914","DOIUrl":"https://doi.org/10.1177/09622802261442914","url":null,"abstract":"Even in rare diseases, where the sample size is limited and blinding is less frequently implemented, randomized controlled trials are considered the gold standard to prove efficacy. Randomization is used to mitigate bias and regulatory guidance recommend the investigation of the impact of bias on the test decision. We quantified how allocation bias affects the test decision in small-sample two-arm group sequential trials under a biasing policy based on the Blackwell-Hodges convergence strategy. Type I error and power were evaluated under Lan-DeMets spending (Pocock-, O'Brien-Fleming-, Wang-Tsiatis-type functions), with and without futility (non-binding, binding), varying interim timing, number of looks and stage-wise restarting of randomization. Allocation bias inflated type I error most for more restrictive randomization procedures, especially permuted blocks with small block sizes. Spending more alpha at interim reduced inflation. Non-binding futility reduced type I error, while binding increased type I error inflation for more aggressive stopping boundaries. Stage-wise restarting modestly reduced inflation for most procedures. Overall, group sequential choices had secondary effect and did not rescue a predictable randomization scheme. When allocation bias cannot be ruled out (e.g. open-label trials), we recommend less restrictive randomization procedures (e.g. big stick design) or, if using permuted blocks, large block sizes.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261442914"},"PeriodicalIF":1.9,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PRO-ADD: Patient-empowered dose-finding trials integrating safety, preliminary efficacy and patient-reported outcomes for optimal dose selection. PRO-ADD：患者授权的剂量寻找试验，综合安全性、初步疗效和患者报告的最佳剂量选择结果。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-29 DOI: 10.1177/09622802261435969

Emily Alger, Sumithra J Mandrekar, Jun Yin, Christina Yap

{"title":"PRO-ADD: Patient-empowered dose-finding trials integrating safety, preliminary efficacy and patient-reported outcomes for optimal dose selection.","authors":"Emily Alger, Sumithra J Mandrekar, Jun Yin, Christina Yap","doi":"10.1177/09622802261435969","DOIUrl":"https://doi.org/10.1177/09622802261435969","url":null,"abstract":"Advances in oncology drug development are driving the emergence of novel therapies, challenging traditional dose-efficacy assumptions in dose-finding oncology trials. Traditional trial designs aim to identify a maximum tolerated dose (MTD) by assessing patients' dose-limiting toxicities (DLTs) - adopting traditional dose-efficacy paradigms that efficacy increases with treatment dose. With these new therapies in mind, emphasis should shift toward methodological advancements in trial designs aimed at identifying optimal doses, rather than solely determining MTDs. Incorporating patient-reported outcomes (PROs) within dose-finding oncology trials is increasingly recommended to better understand treatments' tolerability profiles, especially given the extended tolerability assessment windows for novel immunotherapies and targeted therapies. This article introduces PRO-ADD (Patient-Reported Outcomes Aided Dose-optimisation Design), a modular trial design framework for dose-optimisation. We leverage this framework to optimise dosage with respect to three key outcomes - clinician-assessed DLTs, PROs and preliminary efficacy. PRO-ADD performs well at identifying the optimal dose (both efficacious and tolerable) - successfully identifying the most tolerable effective dose and avoiding escalation to larger, safe doses offering no additional efficacy benefit. As the field evolves, patient-centric dose-finding approaches incorporating PROs are crucial in advancing our understanding of treatment tolerability, and in turn, shaping the future landscape of dose-finding oncology trials.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261435969"},"PeriodicalIF":1.9,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smooth transformation models for survival analysis: A tutorial using R. 生存分析的平滑转换模型：使用R的教程。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-27 DOI: 10.1177/09622802251414595

Sandra Siegfried, Bálint Tamási, Torsten Hothorn

{"title":"Smooth transformation models for survival analysis: A tutorial using R.","authors":"Sandra Siegfried, Bálint Tamási, Torsten Hothorn","doi":"10.1177/09622802251414595","DOIUrl":"https://doi.org/10.1177/09622802251414595","url":null,"abstract":"Over the last five decades, we have seen strong methodological advances in survival analysis, using parametric methods and, more prominently, methods based on non-/semi-parametric estimation. As the methodological landscape continues to evolve, the task of navigating through the multitude of methods and identifying available software resources is becoming increasingly challenging-especially in more complex scenarios, such as when dealing with interval-censored or clustered survival data, non-proportional hazards, or dependent censoring. This tutorial explores the potential of using the framework of smooth transformation models for survival analysis in the R system for statistical computing. This framework provides a unified maximum-likelihood approach that covers a wide range of survival models, including well-established ones such as the Weibull model and a fully parametric version of the famous Cox proportional hazards model, and various extensions for more complex scenarios. We explore models for non-proportional/crossing hazards, dependent censoring, clustered observations and extensions towards personalized medicine within this framework. Using survival data from a two-arm randomized controlled trial on rectal cancer therapy, we demonstrate how survival analysis tasks can be seamlessly navigated in R within this framework using the implementation provided by the tram package, and few related packages.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251414595"},"PeriodicalIF":1.9,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantifying the effects of air pollution on respiratory ill health treated in primary care when the locations of the populations at risk are partially unknown. 在部分风险人群的地点未知的情况下，量化空气污染对初级保健治疗的呼吸系统疾病的影响。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-24 DOI: 10.1177/09622802261439259

Qiangqiang Zhu, Duncan Lee, Oliver Stoner

{"title":"Quantifying the effects of air pollution on respiratory ill health treated in primary care when the locations of the populations at risk are partially unknown.","authors":"Qiangqiang Zhu, Duncan Lee, Oliver Stoner","doi":"10.1177/09622802261439259","DOIUrl":"https://doi.org/10.1177/09622802261439259","url":null,"abstract":"Most air pollution and health studies focus on severe outcomes such as hospitalisations and deaths, overlooking the impact that air pollution may have on non-hospitalised respiratory ill health treated in primary care. This paper presents a new study investigating the effects of NO2, PM10 and PM<math><msub><mrow></mrow><mrow><mn>2.5</mn></mrow></msub></math> on the prescription rates of respiratory medications in Scotland between 2016 and 2020 at a monthly resolution. To enhance the spatial accuracy of the exposure estimates, air pollution predictions at a 1 km<math><msup><mrow></mrow><mn>2</mn></msup></math> resolution are realigned to General Practioner (GP) surgery patient populations by accounting for where patients are likely to live rather than just where the GP surgery is. A Bayesian spatio-temporal conditional autoregressive model is utilised to account for spatial and temporal dependencies in the data, and this paper proposes two novel spatial neighbourhood matrices to better represent the spatial closeness among the patient populations registered at each GP surgery. These matrices improve model performance in capturing spatial correlation compared to standard distance-based approaches, such as using <math><mi>K</mi></math>-nearest neighbours approach. The results of the study suggest that particulate matter pollution has a significant impact on prescription rates for inhaled corticosteroids that are taken to prevent the symptoms of respiratory ill health, while NO2 demonstrates no such association.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261439259"},"PeriodicalIF":1.9,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A practical review of response-adaptive randomization: Under-explored challenges and potential directions. 反应适应性随机化的实践回顾：未被探索的挑战和潜在的方向。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-16 DOI: 10.1177/09622802261427330

Hao Mei, Xiaolin Xu, Hang Yang, Fan Wang, Yang Li

{"title":"A practical review of response-adaptive randomization: Under-explored challenges and potential directions.","authors":"Hao Mei, Xiaolin Xu, Hang Yang, Fan Wang, Yang Li","doi":"10.1177/09622802261427330","DOIUrl":"https://doi.org/10.1177/09622802261427330","url":null,"abstract":"Response-adaptive randomization (RAR) dynamically adjusts allocation probabilities of sequentially enrolled patients based on accumulating response information. It has gained increasing attention in clinical trials for its ability to enhance statistical efficiency by accelerating estimation of treatment effects, improve ethical allocation by favoring superior interventions, and maintain robust parameter estimation (e.g. via variance or uncertainty reduction) while accounting for adaptive modifications. While both regulatory agencies and the broader clinical research community are making efforts to promote the application of RAR, its real-world implementation remains limited. In this article, we review the application of RAR in clinical practice from 2015 to 2024, identifying key challenges such as managing diverse patient outcome types, incorporating repeated measurements, and addressing missing data. We categorize various RAR methods and propose practical solutions to these challenges, providing insights and practical references for clinical trial practitioners. Additionally, we discuss the limitations of existing RAR methods and outline potential future research directions. Our review aims to bridge the gap between theory and practice, promoting the broader adoption of RAR in clinical trials while advancing the development of RAR theory and methodology.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261427330"},"PeriodicalIF":1.9,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian sample size determination using robust commensurate priors with interpretable discrepancy weights. 使用具有可解释差异权重的稳健相称先验的贝叶斯样本量确定。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-16 DOI: 10.1177/09622802261432816

Lou E Whitehead, James Ms Wason, Oliver Sailer, Haiyan Zheng

{"title":"Bayesian sample size determination using robust commensurate priors with interpretable discrepancy weights.","authors":"Lou E Whitehead, James Ms Wason, Oliver Sailer, Haiyan Zheng","doi":"10.1177/09622802261432816","DOIUrl":"https://doi.org/10.1177/09622802261432816","url":null,"abstract":"Randomized controlled clinical trials provide the gold standard for evidence generation in relation to the efficacy of a new treatment in clinical research. Relevant information from previous studies may be desirable to incorporate in the design and analysis of a new trial, with the Bayesian paradigm providing a coherent framework to formally incorporate prior knowledge. Many established methods involve the use of a discounting factor, sometimes related to a measure of 'similarity' between historical and the new trials. However, it is often the case that the sample size is highly nonlinear in those discounting factors. This hinders communication with subject-matter experts to elicit sensible values for borrowing strength at the trial design stage. Focussing on a method that can incorporate historical data from multiple sources, we highlight a particular issue of nonmonotonicity and explain why this causes issues with interpretability of discounting factors (hereafter referred to as 'weights'). We propose a solution from which an analytical sample size formula is derived. We then propose a linearization technique such that the sample size changes uniformly over the weights. This leads to interpretable weights (as a percentage of information to borrow/discount) which could facilitate easier elicitation of expert opinion on their values.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261432816"},"PeriodicalIF":1.9,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The applicability to systematic reviews of common effect, random effects and fixed effects approaches to meta-analysis. 在meta分析中对共同效应、随机效应和固定效应方法的系统评价的适用性。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-16 DOI: 10.1177/09622802261439260

Richard J Stevens

{"title":"The applicability to systematic reviews of common effect, random effects and fixed effects approaches to meta-analysis.","authors":"Richard J Stevens","doi":"10.1177/09622802261439260","DOIUrl":"https://doi.org/10.1177/09622802261439260","url":null,"abstract":"Systematic reviewers planning quantitative meta-analysis usually choose between fixed effect meta-analysis, and random effects meta-analysis. An alternative method is called fixed effects (note the s in the name). This method has the unique property that the target estimand is defined by the variances of the studies found by the systematic review. This article considers each in relation to the quantitative analysis of data to be obtained by systematic review. For clarity, I refer to the traditional fixed effect method as the common effect method and the newer approach as fixed effects (plural). Case studies illustrate the post hoc nature of the fixed effects (plural) method, in which the population under study is determined by the data rather than by the protocol. Mathematical analysis shows that unlike common effect and random effects methods, the fixed effects (plural) method requires an additional, and unrealistic, assumption about the data obtained in systematic reviews. A simulation study demonstrates that confidence intervals from fixed effects (plural) meta-analysis do not account for the post hoc nature of the method. Fixed effects (plural) meta-analysis is neither a slot-in replacement for the common effect method nor for the random effects method of meta-analysis. Of the three methods considered here, the common effect method and the random effects method are potentially valid for the quantitative analysis of systematic reviews.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261439260"},"PeriodicalIF":1.9,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A flexible semiparametric approach for robust causal inference with invalid instruments and unmeasured confounder. 一种灵活的半参数方法，用于具有无效仪器和未测量混杂因素的稳健因果推理。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-12 DOI: 10.1177/09622802261439252

Yunlong Cao, Yuquan Wang, Dapeng Shi, Dong Chen, Yue-Qing Hu

{"title":"A flexible semiparametric approach for robust causal inference with invalid instruments and unmeasured confounder.","authors":"Yunlong Cao, Yuquan Wang, Dapeng Shi, Dong Chen, Yue-Qing Hu","doi":"10.1177/09622802261439252","DOIUrl":"https://doi.org/10.1177/09622802261439252","url":null,"abstract":"Inferring causal effects with unmeasured confounder is a main challenge in causal inference. Many researchers impose parametric assumptions on the distribution of unmeasured confounder. However, due to the unobservable nature of the unmeasured confounder, it is more reasonable to leave its distribution unrestricted. Another key challenge in causal inference is the involvement of invalid instrumental variables, which may lead to biased inference and potentially deceptive results. To this end, we employ a flexible semiparametric model that allows for possibly invalid instruments without specifying the distribution of unmeasured confounder in this work. A penalized semiparametric estimator for causal effects is constructed and its oracle and asymptotic properties are well established for statistical inference. We evaluate the performance of the estimator through simulation studies, revealing that our proposed estimator exhibits asymptotic unbiasedness and robustness in estimating causal effects, along with consistent selection of invalid instruments. We also demonstrate its application using Atherosclerosis Risk in Communities Study data set, which further validates its robustness in the presence of invalid instruments.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261439252"},"PeriodicalIF":1.9,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147676772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0