Statistics in Medicine最新文献_第7页

Causal Multistate Models to Evaluate Treatment Delay. 评价治疗延迟的因果多状态模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70061

Ilaria Prosepe, Saskia le Cessie, Hein Putter, Nan van Geloven

{"title":"Causal Multistate Models to Evaluate Treatment Delay.","authors":"Ilaria Prosepe, Saskia le Cessie, Hein Putter, Nan van Geloven","doi":"10.1002/sim.70061","DOIUrl":"10.1002/sim.70061","url":null,"abstract":"Multistate models allow for the study of scenarios where individuals experience different events over time. While effective for descriptive and predictive purposes, multistate models are not typically used for causal inference. We propose an estimator that combines a multistate model with g-computation to estimate the causal effect of treatment delay strategies. In particular, we estimate the impact of strategies such as awaiting natural recovery for 3 months, on the marginal probability of recovery. We use an illness-death model, where illness and death represent, respectively, treatment and recovery. We formulate the causal assumptions needed for identification and the modeling assumptions needed to estimate the quantities of interest. In a simulation study, we present scenarios where the proposed method can make more efficient use of data compared to an alternative approach using cloning-censoring-reweighting. We then showcase the proposed methodology on real data by estimating the effect of treatment delay on a cohort of 1896 couples with unexplained subfertility who seek intrauterine insemination.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70061"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11978571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812287","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

Bayesian Generalized Linear Models for Analyzing Compositional and Sub-Compositional Microbiome Data via EM Algorithm. 基于EM算法分析组成和亚组成微生物组数据的贝叶斯广义线性模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70084

Li Zhang, Zhenying Ding, Jinhong Cui, Xiaoxiao Zhou, Nengjun Yi

{"title":"Bayesian Generalized Linear Models for Analyzing Compositional and Sub-Compositional Microbiome Data via EM Algorithm.","authors":"Li Zhang, Zhenying Ding, Jinhong Cui, Xiaoxiao Zhou, Nengjun Yi","doi":"10.1002/sim.70084","DOIUrl":"https://doi.org/10.1002/sim.70084","url":null,"abstract":"The study of compositional microbiome data is critical for exploring the functional roles of microbial communities in human health and disease. Recent advances have shifted from traditional log-ratio transformations of compositional covariates to zero constraint on the sum of the corresponding coefficients. Various approaches, including penalized regression and Markov Chain Monte Carlo (MCMC) algorithms, have been extended to enforce this sum-to-zero constraint. However, these methods exhibit limitations: penalized regression yields only point estimates, limiting uncertainty assessment, while MCMC methods, although reliable, are computationally intensive, particularly in high-dimensional data settings. To address the challenges posed by existing methods, we proposed Bayesian generalized linear models for analyzing compositional and sub-compositional microbiome data. Our model employs a spike-and-slab double-exponential prior on the microbiome coefficients, inducing weak shrinkage on large coefficients and strong shrinkage on irrelevant ones, making it ideal for high-dimensional microbiome data. The sum-to-zero constraint is handled through soft-centers by applying prior distribution on the sum of compositional or subcompositional coefficients. To alleviate computational intensity, we have developed a fast and stable algorithm incorporating expectation-maximization (EM) steps into the routine iteratively weighted least squares (IWLS) algorithm for fitting GLMs. The performance of the proposed method was assessed by extensive simulation studies. The simulation results show that our approach outperforms existing methods with higher accuracy of coefficient estimates and lower prediction error. We also applied the proposed method to one microbiome study to find microorganisms linked to inflammatory bowel disease (IBD). The methods have been implemented in a freely available R package BhGLM https://github.com/nyiuab/BhGLM.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70084"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012662","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 Generalized Phase I/II Dose Optimization Trial Design With Multi-Categorical and Multi-Graded Outcomes. 具有多分类、多分级结果的I/II期剂量优化试验设计

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70049

Yichen Yan, Ruitao Lin, Tianyu Guan, Haolun Shi, Xiaolei Lin

{"title":"A Generalized Phase I/II Dose Optimization Trial Design With Multi-Categorical and Multi-Graded Outcomes.","authors":"Yichen Yan, Ruitao Lin, Tianyu Guan, Haolun Shi, Xiaolei Lin","doi":"10.1002/sim.70049","DOIUrl":"https://doi.org/10.1002/sim.70049","url":null,"abstract":"Pursuing accurate observations and rational assumptions always drives advances in clinical trial design. In recent years, more trials have begun to collect multi-graded outcomes for more informative analyses. At the same time, assumptions other than the traditional monotonicity relationship have been considered in the dose-efficacy curve to be more realistic. Inspired by these two trends, we propose a phase I/II design that simultaneously considers multi-categorical toxicity and efficacy with multi-graded outcomes, measured as quasi-continuous probability based on prespecified weight matrices of clinical significance. Following keyboard design, our approach aims to screen out overly toxic doses by the toxicity probability intervals and adaptively makes dose escalation or de-escalation decisions by comparing the posterior distributions of dose desirability (utility) among the adjacent levels of the current dose. It helps to more accurately identify the OBD in a non-monotonically increasing dose-efficacy relationship. We also comprehensively present the safety, accuracy and reliability performance through numerical simulations in multiple scenarios and compare the results with several already available designs. The benchmarking results of multiple operating characteristics convincingly support that our design leads in overall performance while ensuring robustness.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70049"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000121","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

Measuring the Performance of Survival Models to Personalize Treatment Choices. 测量生存模型的性能以个性化治疗选择。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70050

Orestis Efthimiou, Jeroen Hoogland, Thomas P A Debray, Valerie Aponte Ribero, Wilma Knol, Huiberdina L Koek, Matthias Schwenkglenks, Séverine Henrard, Matthias Egger, Nicolas Rodondi, Ian R White

{"title":"Measuring the Performance of Survival Models to Personalize Treatment Choices.","authors":"Orestis Efthimiou, Jeroen Hoogland, Thomas P A Debray, Valerie Aponte Ribero, Wilma Knol, Huiberdina L Koek, Matthias Schwenkglenks, Séverine Henrard, Matthias Egger, Nicolas Rodondi, Ian R White","doi":"10.1002/sim.70050","DOIUrl":"https://doi.org/10.1002/sim.70050","url":null,"abstract":"Various statistical and machine learning algorithms can be used to predict treatment effects at the patient level using data from randomized clinical trials (RCTs). Such predictions can facilitate individualized treatment decisions. Recently, a range of methods and metrics were developed for assessing the accuracy of such predictions. Here, we extend these methods, focusing on the case of survival (time-to-event) outcomes. We start by providing alternative definitions of the participant-level treatment benefit; subsequently, we summarize existing and propose new measures for assessing the performance of models estimating participant-level treatment benefits. We explore metrics assessing discrimination and calibration for benefit and decision accuracy. These measures can be used to assess the performance of statistical as well as machine learning models and can be useful during model development (i.e., for model selection or for internal validation) or when testing a model in new settings (i.e., in an external validation). We illustrate methods using simulated data and real data from the OPERAM trial, an RCT in multimorbid older people, which randomized participants to either standard care or a pharmacotherapy optimization intervention. We provide R codes for implementing all models and measures.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70050"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11983264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000124","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

Combining Straight-Line and Map-Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease. 结合直线和基于地图的距离来研究接近健康食品与疾病之间的联系。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70054

Sarah C Lotspeich, Ashley E Mullan, Lucy D'Agostino McGowan, Staci A Hepler

{"title":"Combining Straight-Line and Map-Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease.","authors":"Sarah C Lotspeich, Ashley E Mullan, Lucy D'Agostino McGowan, Staci A Hepler","doi":"10.1002/sim.70054","DOIUrl":"https://doi.org/10.1002/sim.70054","url":null,"abstract":"Healthy foods are essential for a healthy life, but accessing healthy food can be more challenging for some people than others. This disparity in food access may lead to disparities in well-being, potentially with disproportionate rates of diseases in communities that face more challenges in accessing healthy food (i.e., low-access communities). Identifying low-access, high-risk communities for targeted interventions is a public health priority, but current methods to quantify food access rely on distance measures that are either computationally simple (like the length of the shortest straight-line route) or accurate (like the length of the shortest map-based driving route), but not both. We propose a multiple imputation approach to combine these distance measures, allowing researchers to harness the computational ease of one with the accuracy of the other. The approach incorporates straight-line distances for all neighborhoods and map-based distances for just a subset, offering comparable estimates to the \"gold standard\" model using map-based distances for all neighborhoods and improved efficiency over the \"complete case\" model using map-based distances for just the subset. Through the adoption of a measurement error framework, information from the straight-line distances can be leveraged to compute informative placeholders (i.e., impute) for any neighborhoods without map-based distances. Using simulations and data for the Piedmont Triad region of North Carolina, we quantify and compare the associations between two health outcomes (diabetes and obesity) and neighborhood-level access to healthy foods. The imputation procedure also makes it possible to predict the full landscape of food access in an area without requiring map-based measurements for all neighborhoods.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70054"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11995689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047640","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

Single-Index Measurement Error Jump Regression Model in Alzheimer's Disease Studies. 阿尔茨海默病研究中的单指标测量误差跳跃回归模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-30 DOI: 10.1002/sim.70081

Yan-Yong Zhao, Kaizhou Lei, Yuan Liu, Yuanyao Tan, Noriszura Ismail, Razik Ridzuan Mohd Tajuddin, Rongjie Liu, Chao Huang

{"title":"Single-Index Measurement Error Jump Regression Model in Alzheimer's Disease Studies.","authors":"Yan-Yong Zhao, Kaizhou Lei, Yuan Liu, Yuanyao Tan, Noriszura Ismail, Razik Ridzuan Mohd Tajuddin, Rongjie Liu, Chao Huang","doi":"10.1002/sim.70081","DOIUrl":"https://doi.org/10.1002/sim.70081","url":null,"abstract":"Alzheimer's disease (AD) is the major cause of dementia in the elderly, and investigations on the impact of risk factors on neurocognitive performance are crucial in preventative treatment. While existing statistical regression models, such as single-index models, have proven effective tools for uncovering the relationship between the neurocognitive scores and covariates of interest such as demographic information, clinical variables, and neuroimaging features, limited research has explored scenarios where jump discontinuities exist in the regression patterns and the covariates are unobservable but measured with errors, which are common in real applications. To address these challenges, we propose a single-index measurement error jump regression model (SMEJRM) that can handle both jump discontinuities and measurement errors in image covariates introduced by different image processing software. This development is motivated by data from 168 patients in the Alzheimer's Disease Neuroimaging Initiative. We establish both the estimation procedure and the corresponding asymptotic results. Simulation studies are conducted to evaluate the finite sample performance of our SMEJRM and the estimation procedure. The real application reveals that jump discontinuities do exist in the relationship between neurocognitive scores and some covariates of interest in this study.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70081"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144038259","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 New Semiparametric Power-Law Regression Model With Long-Term Survival, Change-Point Detection and Regularization. 具有长期生存、变化点检测和正则化的半参数幂律回归模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70043

Nixon Jerez-Lillo, Alejandra Tapia, Victor Hugo Lachos, Pedro Luiz Ramos

引用次数: 0

Causal Inference in Presence of Intra-Patient Correlation due to Repeated Measurements of Exposure and Outcome in Longitudinal Settings. 在纵向设置中，由于暴露和结果的重复测量，存在患者内部相关性的因果推理。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70037

Antoine Gavoille, Fabien Rollot, Romain Casey, Sandra Vukusic, Muriel Rabilloud, Fabien Subtil

{"title":"Causal Inference in Presence of Intra-Patient Correlation due to Repeated Measurements of Exposure and Outcome in Longitudinal Settings.","authors":"Antoine Gavoille, Fabien Rollot, Romain Casey, Sandra Vukusic, Muriel Rabilloud, Fabien Subtil","doi":"10.1002/sim.70037","DOIUrl":"10.1002/sim.70037","url":null,"abstract":"Introduction: In causal inference with time-dependent confounding between an exposure and an outcome, the repeated nature of measures is likely to lead to intra-patient correlation and introduces bias in the estimation of the causal effect, even in the absence of unmeasured confounders.Method: We evaluated the impact of intra-patient correlation on causal effect estimation with g-computation, inverse probability weighting (IPW) and longitudinal targeted maximum likelihood estimator (LTMLE), and compared two ways of accounting for it, using a fixed-effects or a mixed-effects approach. We conducted a simulation analysis under different scenarios for numerous time points and a real-life analysis to investigate the causal effect of pregnancy on neurological disability in multiple sclerosis.Results: In simulation analyses, the presence of intra-patient correlation led to bias in the causal effect estimation with g-computation, IPW, and LTMLE when this was not accounted for; the bias was smaller for LTMLE. Taking into account intra-patient correlation with fixed-effects and mixed-effects approaches reduced the bias in g-computation, with lower standard errors for the mixed-effects approach. Regarding IPW, the fixed-effects approach suffered from weight stability issues when the number of time points increased, and the mixed-effects approach provided inconsistent estimates of the intra-patient correlation in the exposure model. Application to real-life data yielded results consistent with the simulation study, highlighting the importance of accounting for intra-patient correlation.Conclusion: When analyzing longitudinal data in the presence of time-dependent confounding using g-methods, intra-patient correlation due to repeated measurements of exposure and outcome should be accounted for in the causal reasoning.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e70037"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11881680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143557930","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

Can the Unit Size Predict Outcomes? Testing for Informativeness in Three-Level Designs. 单位大小能预测结果吗？三水平设计的信息性检验。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70041

Samuel Anyaso-Samuel, Somnath Datta, Eva Roos, Jaakko Nevalainen

{"title":"Can the Unit Size Predict Outcomes? Testing for Informativeness in Three-Level Designs.","authors":"Samuel Anyaso-Samuel, Somnath Datta, Eva Roos, Jaakko Nevalainen","doi":"10.1002/sim.70041","DOIUrl":"10.1002/sim.70041","url":null,"abstract":"Multilevel data are frequently encountered in biomedical research, and several statistical methods have been developed to analyze such data. Informativeness of the number of units on certain levels often manifests itself in multilevel data analysis and failure to account for this phenomenon will lead to biased inference. Moreover, utilizing an incorrect marginalization approach will also lead to invalid conclusions. To identify the appropriate marginal distribution to be tested in multilevel designs, we propose a sequential testing procedure to test for informativeness of unit sizes in multilevel structures with three levels. At a given level of the design, a bootstrap method is developed to estimate the null distribution of no informativeness of unit size. Simulation studies confirm the efficacy of our sequential procedure in maintaining an overall Type I error rate. Additionally, we extend our testing procedure to a multilevel regression setting, enhancing its practical applicability. We demonstrate the utility of our proposed methods through the analysis of data from a study on periodontal disease and a study on stress levels of preschoolers.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e70041"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586866","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

Bayesian Additive Regression Trees for Group Testing Data. 组测试数据的贝叶斯加性回归树。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-03-15 DOI: 10.1002/sim.70052

Madeleine E St Ville, Christopher S McMahan, Joe D Bible, Joshua M Tebbs, Christopher R Bilder

{"title":"Bayesian Additive Regression Trees for Group Testing Data.","authors":"Madeleine E St Ville, Christopher S McMahan, Joe D Bible, Joshua M Tebbs, Christopher R Bilder","doi":"10.1002/sim.70052","DOIUrl":"10.1002/sim.70052","url":null,"abstract":"When screening for low-prevalence diseases, pooling specimens (e.g., blood, urine, swabs, etc.) through group testing has the potential to substantially reduce costs when compared to testing specimens individually. A common goal in group testing applications is to estimate the relationship between an individual's true disease status and their individual-level covariate information. However, estimating such a relationship is a non-trivial problem because true individual disease statuses are unknown due to the group testing protocol and the possibility of imperfect testing. While several regression methods have been developed in recent years to accommodate the complexity of group testing data, the functional form of covariate effects is typically assumed to be known. To avoid model misspecification and to provide a more flexible approach, we propose a Bayesian additive regression trees framework to model the individual-level probability of disease with potentially misclassified group testing data. Our methods can be used to analyze data arising from any group testing protocol with the goal of estimating unknown functions of covariates and assay classification accuracy probabilities.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 6","pages":"e70052"},"PeriodicalIF":1.8,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11907685/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626180","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