Statistics in Medicine最新文献_第2页

Federated Time-to-Event Data Analysis Using Piecewise Exponential Regression in DataSHIELD. 基于分段指数回归的DataSHIELD联邦时间到事件数据分析。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70789

Bodil Svennblad, Benedicte Delcoigne, Paul Burton, Demetris Avraam

{"title":"Federated Time-to-Event Data Analysis Using Piecewise Exponential Regression in DataSHIELD.","authors":"Bodil Svennblad, Benedicte Delcoigne, Paul Burton, Demetris Avraam","doi":"10.1002/sim.70789","DOIUrl":"10.1002/sim.70789","url":null,"abstract":"Time-to-event analyses on individual patient data are commonly performed with Cox or Poisson regression. The statistical power of epidemiological findings from such analyses is often sufficient when a large amount of data is analyzed, especially when studying rare outcomes or rare exposures. The increase of sample size can be achieved by co-analyzing data collected from different cohorts and hosted at different locations. For co-analyzing data located at different centers, the common options are either (i) to physically transfer and pool the data into a single dataset and analyze them jointly or (ii) to conduct a two-step meta-analysis where a separate analysis is done at each center and the center-specific results are then pooled and combined into a single estimate. DataSHIELD offers a third option where individual patient data from different locations are analyzed in a federated manner without transporting any data. In this paper, we describe how a federated Poisson regression can be conducted on time-to-event data using this infrastructure, providing relevant estimates.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70789"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13678026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220669","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}

引用次数: 0

Valid Data-Driven Subgroup Selection With Uniform Guarantees in Clinical Trials. 临床试验中具有统一保证的有效数据驱动的亚组选择。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70745

Manuel M Müller, Björn Bornkamp, Frank Bretz, Timothy I Cannings, Wei Liu, Henry W J Reeve, Richard J Samworth, Nikolaos Sfikas, Fang Wan, Konstantinos Sechidis

{"title":"Valid Data-Driven Subgroup Selection With Uniform Guarantees in Clinical Trials.","authors":"Manuel M Müller, Björn Bornkamp, Frank Bretz, Timothy I Cannings, Wei Liu, Henry W J Reeve, Richard J Samworth, Nikolaos Sfikas, Fang Wan, Konstantinos Sechidis","doi":"10.1002/sim.70745","DOIUrl":"10.1002/sim.70745","url":null,"abstract":"Subgroup selection in clinical trials is essential for identifying patient groups that react differently to a treatment, thereby enabling personalized medicine. In particular, subgroup selection can identify patient groups that respond particularly well to a treatment or that encounter adverse events more often. However, this is a post-selection inference problem, which may pose challenges for traditional techniques used for subgroup analysis, such as increased Type I error rates and potential biases from data-driven subgroup identification. In this paper, we discuss two recently developed methods for subgroup selection in regression problems: one based on generalized linear modeling and another on isotonic regression. We demonstrate how these methods can be used for data-driven subgroup identification in the analysis of clinical trials, focusing on two distinct tasks: identifying patient groups that are safe from manifesting adverse events and identifying patient groups with high treatment effect, while controlling for Type I error in both cases. A thorough simulation study is conducted to evaluate the strengths and weaknesses of each method, providing detailed insight into the sensitivity of the Type I error rate control to modeling assumptions.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70745"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13662529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184288","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}

引用次数: 0

Bootstrap Calibration of Likelihood-Ratio Inference for the Hypergeometric-Normal Random-Effects Model in Rare-Event Meta-Analysis. 罕见事件元分析中超几何-正态随机效应模型似然比推断的自举校准。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70776

Hisashi Noma, Theodoros Evrenoglou, Kazushi Maruo

{"title":"Bootstrap Calibration of Likelihood-Ratio Inference for the Hypergeometric-Normal Random-Effects Model in Rare-Event Meta-Analysis.","authors":"Hisashi Noma, Theodoros Evrenoglou, Kazushi Maruo","doi":"10.1002/sim.70776","DOIUrl":"https://doi.org/10.1002/sim.70776","url":null,"abstract":"Conventional random-effects models in meta-analysis rely on normal approximations and fixed within-study variance assumptions that are often violated in rare-event settings. When zero cells occur, Haldane-type (+0.5) corrections can further bias the estimated effects. To overcome these limitations, likelihood-based approaches such as the hypergeometric-normal (HGN) model have been developed to enable exact conditional likelihood-based inference. However, ordinary Wald-type inference may still be inaccurate in small and sparse data settings. In this article, we investigate likelihood-ratio (LR)-based inference for the HGN random-effects model and propose two parametric bootstrap procedures for improving its finite-sample calibration: (1) bootstrap mean calibration of the LR statistic and (2) bootstrap inference based directly on the empirical distribution of the LR statistic. Extensive simulation studies under rare-event conditions demonstrate that the uncorrected LR-based confidence interval already provides stable coverage, reflecting the favorable properties of the exact conditional likelihood. Bootstrap mean calibration generally provided the most consistent additional calibration under the most challenging extremely rare-event settings, whereas direct bootstrap inference provided an alternative distribution-based approach. An application to a meta-analysis of rosiglitazone and myocardial infarction is also presented.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70776"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184165","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}

引用次数: 0

Locally Estimated Scatterplot Smoothing for Censored Survival Data. 截尾生存数据的局部估计散点图平滑。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70783

David van Klaveren, Peter C Austin, Patrick W Serruys, Ben van Calster, Frank E Harrell

{"title":"Locally Estimated Scatterplot Smoothing for Censored Survival Data.","authors":"David van Klaveren, Peter C Austin, Patrick W Serruys, Ben van Calster, Frank E Harrell","doi":"10.1002/sim.70783","DOIUrl":"10.1002/sim.70783","url":null,"abstract":"In the absence of censoring, locally estimated scatterplot smoothing (LOESS) is a powerful tool to estimate the relationship between a continuous covariate and the probability of an event and is often used to visually assess calibration of prognostic models. Since follow-up in prognostic studies is often incomplete, we aimed to study the extension of LOESS to right-censored data. In line with other work, we first obtained inverse-probability-of-censoring (IPC) weights through Kaplan-Meier estimation. When censoring varied by covariate levels, we used Cox regression with a spline function of the covariate to obtain IPC weights. We then applied LOESS to the weighted event indicators to estimate the relationship between the covariate and the probability of an event within a specific time horizon. We illustrated the application of LOESS for censored data (LOESScd) using patients with complex coronary artery disease. We used the LOESScd to estimate the association between continuous predictors and the probability of death within 10 years. LOESScd estimates closely followed grouped Kaplan-Meier estimates, especially in case of weak smoothing. Covariate-dependent IPC weights improved LOESScd when censoring varied by covariate levels. We then examined the finite-sample behavior of LOESScd in simulations motivated by four typical prognostic model validation scenarios. In 2000 samples of 1800 patients, bias was limited and coverage was close to nominal. In conclusion, LOESScd is a promising estimator of the relationship of a continuous covariate with the probability of an event in the presence of right-censoring. LOESScd is especially useful for estimating calibration curves when validating prognostic models.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70783"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149207935","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}

引用次数: 0

Multivariable Mendelian Randomization With Weak Instruments: A Comparison of Bayesian and Frequentist Methods. 弱仪器下的多变量孟德尔随机化：贝叶斯方法与频率方法的比较。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70788

Andrew J Grant, Ashish Patel, Stephen Burgess

{"title":"Multivariable Mendelian Randomization With Weak Instruments: A Comparison of Bayesian and Frequentist Methods.","authors":"Andrew J Grant, Ashish Patel, Stephen Burgess","doi":"10.1002/sim.70788","DOIUrl":"10.1002/sim.70788","url":null,"abstract":"Weak instruments are a well-known limitation for valid causal inference in Mendelian randomization studies. In the single exposure setting, weak instrument bias can be mitigated by selecting genetic instruments which are strongly associated with the exposure according to p-value and/or F-statistic thresholds. However, in the multi-exposure setting, genetic instruments may be strongly associated with an exposure but weakly associated with it conditional on all other exposures in the analysis. It is therefore more difficult to guarantee conditionally strong instruments in multivariable Mendelian randomization. Weak instrument bias can be mitigated using modeling approaches; however, there are fewer methods for doing this in the multivariable case compared with the single exposure case. In this paper, we consider a method for mitigating weak instrument bias in multivariable Mendelian randomization using a Bayesian framework: MVMR-Pony. We compare this method with existing frequentist methods. We show using simulation studies that the MVMR-Pony method outperforms the frequentist approaches with respect to bias, coverage, Type I error rates, and power, across settings where weak instrument bias arises due to correlated genetic effects, measurement error, and mediation.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70788"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13678027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220622","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}

引用次数: 0

Reliable Bayesian Causal Inference for Externally Controlled Single-Arm Trials Under Transportability Violations. 在可运输性违反下外部控制单臂试验的可靠贝叶斯因果推断。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70748

Se Yoon Lee

{"title":"Reliable Bayesian Causal Inference for Externally Controlled Single-Arm Trials Under Transportability Violations.","authors":"Se Yoon Lee","doi":"10.1002/sim.70748","DOIUrl":"10.1002/sim.70748","url":null,"abstract":"Externally controlled single-arm trials are increasingly popular because they do not require randomized controls, thereby substantially reducing financial costs; however, valid causal inference in this setting relies critically on transportability between the trial and external populations, an assumption that is untestable. Frequentist semiparametrically efficient estimators, such as the doubly robust estimator, are typically analyzed under first-order asymptotics and are designed to mitigate nuisance-estimation bias under correct specification; however, this framework does not account for structural bias arising under local transportability violations, where bias and stochastic fluctuation occur at the same parametric rate. We propose a reliable Bayesian framework for estimating the average treatment effect on the treated (ATT) that combines synthetic outcome construction with residualized power-prior borrowing. Flexible machine learning is first used to construct covariate-aligned synthetic controls, after which Bayesian inference on residualized outcomes allows external borrowing to modulate uncertainty without distorting covariate-driven heterogeneity. The posterior admits a heavy-tailed distribution-unlike classical Wald procedures-and the resulting estimator lies outside the class of regular asymptotically linear estimators, thereby revealing behavior beyond first-order semiparametric asymptotics. We establish a reliability-efficiency trade-off under local transportability violations. Simulations and a case study on schizophrenia trial design demonstrate the theoretical findings and practical utility.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70748"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149040592","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}

引用次数: 0

A Model-Robust G-Computation Method for Analyzing Hybrid Control Studies Without Assuming Exchangeability. 不考虑互换性的混合控制研究模型鲁棒g计算方法。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70754

Zhiwei Zhang, Peisong Han, Wei Zhang

{"title":"A Model-Robust G-Computation Method for Analyzing Hybrid Control Studies Without Assuming Exchangeability.","authors":"Zhiwei Zhang, Peisong Han, Wei Zhang","doi":"10.1002/sim.70754","DOIUrl":"10.1002/sim.70754","url":null,"abstract":"There is growing interest in a hybrid control design for treatment evaluation, where a randomized controlled trial is augmented with external control data from a previous trial or a real-world data source. The hybrid control design has the potential to improve efficiency but also carries the risk of introducing bias. The potential bias in a hybrid control study can be mitigated by adjusting for baseline covariates that are related to the control outcome. Existing methods that serve this purpose commonly assume that the internal and external control outcomes are exchangeable upon conditioning on a set of measured covariates. Possible violations of the exchangeability assumption can be addressed using a g-computation method with variable selection under a correctly specified outcome regression model. In this article, we point out that a particular version of this g-computation method is protected against misspecification of the outcome regression model. This observation leads to a model-robust g-computation method that is remarkably simple and easy to implement, consistent and asymptotically normal under minimal assumptions, and able to improve efficiency by exploiting similarities between the internal and external control groups. The method is evaluated in a simulation study and illustrated using real data from HIV treatment trials.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70754"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149070173","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}

引用次数: 0

Group Sequential Sample Size for Comparing Two Survival Probabilities at a Specific Time Point. 在特定时间点比较两种生存概率的组顺序样本量。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70750

Susan Halabi, Lu Liu, Chenxi Yu, Yuan Wu

{"title":"Group Sequential Sample Size for Comparing Two Survival Probabilities at a Specific Time Point.","authors":"Susan Halabi, Lu Liu, Chenxi Yu, Yuan Wu","doi":"10.1002/sim.70750","DOIUrl":"10.1002/sim.70750","url":null,"abstract":"We propose a novel method that simultaneously determines the sample size for testing two survival probabilities at a pre-specified time while guaranteeing Type I error control in both fixed and group-sequential trial designs. Simulations across varying hypothesized differences, failure distributions, censoring proportions, and nominal powers demonstrate consistent performance, while interim analyses highlight maintained nominal Type I error control and increased power at each look relative to the standard binomial approach, regardless of the underlying failure time distribution or spending function. Importantly, our method is especially useful for evaluating survival outcomes at a fixed time in randomized trials where one treatment arm includes neoadjuvant therapy prior to surgery while the other involves surgery alone. Furthermore, it is advantageous when the proportional hazards assumption is not satisfied, as often occurs in immunotherapy trials with delayed or time-varying treatment effects or crossing survival curves. The method is also applicable to randomized Phase II trials, where smaller sample sizes and the use of intermediate or surrogate time-to-event endpoints demand efficient data use and robust error control. We illustrate the approach with motivating examples in renal and prostate cancer. An accompanying R Shiny application enables investigators to compute sample sizes interactively, facilitating practical trial planning in diverse settings.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70750"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13670043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149138378","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}

引用次数: 0

Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach. 混合固化模型的动态预测：一种基于模型的地标方法。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70774

Marta Cipriani, Marco Alfò, Mirko Signorelli

{"title":"Dynamic Prediction in Mixture Cure Models: A Model-Based Landmarking Approach.","authors":"Marta Cipriani, Marco Alfò, Mirko Signorelli","doi":"10.1002/sim.70774","DOIUrl":"10.1002/sim.70774","url":null,"abstract":"Mixture cure models are widely used in survival analysis when a portion of patients is considered cured and is no longer at risk for the event of interest. In clinical settings, dynamic survival prediction is particularly important to refine prognosis by incorporating patient information updated over time. Landmarking methods have emerged as a flexible approach for this purpose, as they allow to summarize longitudinal covariates up to a given landmark time and use these summaries in subsequent prediction. For mixture cure models, the only landmarking strategy available in the literature relies on summarizing the dynamics of observed longitudinal markers through the last observation carried forward (LOCF) method. However, LOCF discards most of the longitudinal information, does not correct for measurement error, and may rely on outdated values if observation times are far apart. To overcome these limitations, we propose a sequential approach that integrates model-based landmarking within a mixture cure model. Initially, longitudinal covariates are modeled using (generalized) linear mixed models, from which individual-specific summaries are predicted. The predicted summaries are then incorporated as covariates into a Cox proportional hazards cure model. We investigated the performance of the proposed approach under different cure fractions, sample sizes, and longitudinal data structures through an extensive simulation study. The results show that the model-based strategy provides more refined predictions compared to LOCF, even when the model is misspecified in favor of the LOCF approach. Finally, we illustrate our method using a real-world dataset on renal transplant patients.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70774"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13678029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220617","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}

引用次数: 0

Calibrating Machine Learning Approaches for Probability Estimation Without Calibration Data. 无校准数据的概率估计的机器学习校准方法。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70751

Eleonora Di Carluccio, Georgios Koliopanos, Francisco M Ojeda, Christian Weimar, Andreas Ziegler

{"title":"Calibrating Machine Learning Approaches for Probability Estimation Without Calibration Data.","authors":"Eleonora Di Carluccio, Georgios Koliopanos, Francisco M Ojeda, Christian Weimar, Andreas Ziegler","doi":"10.1002/sim.70751","DOIUrl":"10.1002/sim.70751","url":null,"abstract":"Statistical prediction models for binary outcomes are becoming increasingly popular. One significant challenge is calibrating these models to suit the characteristics of a target population that is structurally different from the original population. Calibration is especially challenging when there is no training data available from the target population. To address this problem, we propose a novel calibration method, SimCal, which uses synthetic data generated from the model development data in conjunction with marginal statistics from the calibration cohort. We show that expert judgment modeling (EJM) may be used for calibration if cross-sectional data from the target population are available comprising expert judgments about the potential outcome and the covariates. We describe three alternative calibration approaches when calibration data are lacking: similarity-binning averaging (SBA), adaptive calibration of predictions (ACP), and Elkan calibration. In a simulation study, we compare SBA, ACP, Elkan calibration, and SimCal. R code for applying these methods is provided from the re-analysis of data on coronary artery disease. We illustrate all 5 calibration approaches with a real data set for predicting functional outcome after stroke and all approaches but EJM in the re-analysis of the Cleveland Clinic data. None of the approaches performed convincingly well in all situations. SimCal performed well when model parameters were correctly specified. EJM failed on the stroke data. Further research is urgently required for calibration in the absence of calibration data.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70751"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13678028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220647","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}

引用次数: 0