Statistics in Medicine最新文献_第2页

BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis. BHAFT：贝叶斯遗传约束加速失败时间模型，用于在生存分析中检测基因与环境的相互作用。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-20 Epub Date: 2024-07-04 DOI: 10.1002/sim.10145

Na Sun, Jiadong Chu, Qida He, Yu Wang, Qiang Han, Nengjun Yi, Ruyang Zhang, Yueping Shen

{"title":"BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis.","authors":"Na Sun, Jiadong Chu, Qida He, Yu Wang, Qiang Han, Nengjun Yi, Ruyang Zhang, Yueping Shen","doi":"10.1002/sim.10145","DOIUrl":"10.1002/sim.10145","url":null,"abstract":"In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141498978","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 sequential, multiple assignment, randomized trial design with a tailoring function. 具有裁剪功能的顺序、多重分配、随机试验设计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-20 Epub Date: 2024-07-08 DOI: 10.1002/sim.10161

Holly Hartman, Matthew Schipper, Kelley Kidwell

引用次数: 0

Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks. 用于时变临床症状和神经成像网络的动态无向图模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-20 Epub Date: 2024-07-15 DOI: 10.1002/sim.10143

Erin I McDonnell, Shanghong Xie, Karen Marder, Fanyu Cui, Yuanjia Wang

{"title":"Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks.","authors":"Erin I McDonnell, Shanghong Xie, Karen Marder, Fanyu Cui, Yuanjia Wang","doi":"10.1002/sim.10143","DOIUrl":"10.1002/sim.10143","url":null,"abstract":"In this work, we propose methods to examine how the complex interrelationships between clinical symptoms and, separately, brain imaging biomarkers change over time leading up to the diagnosis of a disease in subjects with a known genetic near-certainty of disease. We propose a time-dependent undirected graphical model that ensures temporal and structural smoothness across time-specific networks to examine the trajectories of interactions between markers aligned at the time of disease onset. Specifically, we anchor subjects relative to the time of disease diagnosis (anchoring time) as in a revival process, and we estimate networks at each time point of interest relative to the anchoring time. To use all available data, we apply kernel weights to borrow information across observations that are close to the time of interest. Adaptive lasso weights are introduced to encourage temporal smoothness in edge strength, while a novel elastic fused- <math> <semantics> <mrow> <msub><mrow><mi>l</mi></mrow> <mrow><mn>0</mn></mrow> </msub> </mrow> <annotation>$$ {l}_0 $$</annotation></semantics> </math> penalty removes spurious edges and encourages temporal smoothness in network structure. Our approach can handle practical complications such as unbalanced visit times. We conduct simulation studies to compare our approach with existing methods. We then apply our method to data from PREDICT-HD, a large prospective observational study of pre-manifest Huntington's disease (HD) patients, to identify symptom and imaging network changes that precede clinical diagnosis of HD.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617152","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

Exploiting relationship directionality to enhance statistical modeling of peer-influence across social networks. 利用关系的方向性加强社交网络中同伴影响的统计建模。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-20 Epub Date: 2024-07-09 DOI: 10.1002/sim.10169

Xin Ran, Nancy E Morden, Ellen Meara, Erika L Moen, Daniel N Rockmore, A James O'Malley

{"title":"Exploiting relationship directionality to enhance statistical modeling of peer-influence across social networks.","authors":"Xin Ran, Nancy E Morden, Ellen Meara, Erika L Moen, Daniel N Rockmore, A James O'Malley","doi":"10.1002/sim.10169","DOIUrl":"10.1002/sim.10169","url":null,"abstract":"Risky-prescribing is the excessive or inappropriate prescription of drugs that singly or in combination pose significant risks of adverse health outcomes. In the United States, prescribing of opioids and other \"risky\" drugs is a national public health concern. We use a novel data framework-a directed network connecting physicians who encounter the same patients in a sequence of visits-to investigate if risky-prescribing diffuses across physicians through a process of peer-influence. Using a shared-patient network of 10 661 Ohio-based physicians constructed from Medicare claims data over 2014-2015, we extract information on the order in which patients encountered physicians to derive a directed patient-sharing network. This enables the novel decomposition of peer-effects of a medical practice such as risky-prescribing into directional (outbound and inbound) and bidirectional (mutual) relationship components. Using this framework, we develop models of peer-effects for contagion in risky-prescribing behavior as well as spillover effects. The latter is measured in terms of adverse health events suspected to be related to risky-prescribing in patients of peer-physicians. Estimated peer-effects were strongest when the patient-sharing relationship was mutual as opposed to directional. Using simulations we confirmed that our modeling and estimation strategies allows simultaneous estimation of each type of peer-effect (mutual and directional) with accuracy and precision. We also show that failing to account for these distinct mechanisms (a form of model mis-specification) produces misleading results, demonstrating the importance of retaining directional information in the construction of physician shared-patient networks. These findings suggest network-based interventions for reducing risky-prescribing.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11338714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141564412","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

A Bayesian semi-parametric scalar-on-function regression with measurement error using instrumental variables. 利用工具变量的贝叶斯半参数标量-函数回归与测量误差。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-20 Epub Date: 2024-07-08 DOI: 10.1002/sim.10165

Roger S Zoh, Yuanyuan Luan, Lan Xue, David B Allison, Carmen D Tekwe

{"title":"A Bayesian semi-parametric scalar-on-function regression with measurement error using instrumental variables.","authors":"Roger S Zoh, Yuanyuan Luan, Lan Xue, David B Allison, Carmen D Tekwe","doi":"10.1002/sim.10165","DOIUrl":"10.1002/sim.10165","url":null,"abstract":"Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device-based physical activity measures are best treated as functions when assessing their associations with scalar-valued outcomes such as body mass index. Scalar-on-function regression (SoFR) is a suitable regression model in this setting. Most estimation approaches in SoFR assume that the measurement error in functional covariates is white noise. Violating this assumption can lead to underestimating model parameters. There are limited approaches to correcting measurement errors for frequentist methods and none for Bayesian methods in this area. We present a non-parametric Bayesian measurement error-corrected SoFR model that relaxes all the constraining assumptions often involved with these models. Our estimation relies on an instrumental variable allowing a time-varying biasing factor, a significant departure from the current generalized method of moment (GMM) approach. Our proposed method also permits model-based grouping of the functional covariate following measurement error correction. This grouping of the measurement error-corrected functional covariate allows additional ease of interpretation of how the different groups differ. Our method is easy to implement, and we demonstrate its finite sample properties in extensive simulations. Finally, we applied our method to data from the National Health and Examination Survey to assess the relationship between wearable device-based measures of physical activity and body mass index in adults in the United States.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141559797","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

Extending the DeLong algorithm for comparing areas under correlated receiver operating characteristic curves with missing data. 扩展 DeLong 算法，比较缺失数据下相关接收者操作特征曲线的面积。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-20 Epub Date: 2024-07-16 DOI: 10.1002/sim.10172

Lily Zou, Yun-Hee Choi, Leonardo Guizzetti, Di Shu, Joshua Zou, Guangyong Zou

引用次数: 0

Determining sample size in a personalized randomized controlled (PRACTical) trial. 确定个性化随机对照（PRACTical）试验的样本量。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-20 Epub Date: 2024-07-09 DOI: 10.1002/sim.10168

Rebecca M Turner, Kim May Lee, A Sarah Walker, Sally Ellis, Michael Sharland, Julia A Bielicki, Wolfgang Stöhr, Ian R White

{"title":"Determining sample size in a personalized randomized controlled (PRACTical) trial.","authors":"Rebecca M Turner, Kim May Lee, A Sarah Walker, Sally Ellis, Michael Sharland, Julia A Bielicki, Wolfgang Stöhr, Ian R White","doi":"10.1002/sim.10168","DOIUrl":"10.1002/sim.10168","url":null,"abstract":"In clinical settings with no commonly accepted standard-of-care, multiple treatment regimens are potentially useful, but some treatments may not be appropriate for some patients. A personalized randomized controlled trial (PRACTical) design has been proposed for this setting. For a network of treatments, each patient is randomized only among treatments which are appropriate for them. The aim is to produce treatment rankings that can inform clinical decisions about treatment choices for individual patients. Here we propose methods for determining sample size in a PRACTical design, since standard power-based methods are not applicable. We derive a sample size by evaluating information gained from trials of varying sizes. For a binary outcome, we quantify how many adverse outcomes would be prevented by choosing the top-ranked treatment for each patient based on trial results rather than choosing a random treatment from the appropriate personalized randomization list. In simulations, we evaluate three performance measures: mean reduction in adverse outcomes using sample information, proportion of simulated patients for whom the top-ranked treatment performed as well or almost as well as the best appropriate treatment, and proportion of simulated trials in which the top-ranked treatment performed better than a randomly chosen treatment. We apply the methods to a trial evaluating eight different combination antibiotic regimens for neonatal sepsis (NeoSep1), in which a PRACTical design addresses varying patterns of antibiotic choice based on disease characteristics and resistance. Our proposed approach produces results that are more relevant to complex decision making by clinicians and policy makers.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7616655/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141564411","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

A Precision Mixture Risk Model to Identify Adverse Drug Events in Subpopulations Using a Case-Crossover Design. 利用病例交叉设计识别亚人群药物不良事件的精确混合风险模型

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-19 DOI: 10.1002/sim.10216

Yi Shi, Michael T Eadon, Yao Chen, Anna Sun, Yuedi Yang, Chienwei Chiang, Macarius Donneyong, Jing Su, Pengyue Zhang

{"title":"A Precision Mixture Risk Model to Identify Adverse Drug Events in Subpopulations Using a Case-Crossover Design.","authors":"Yi Shi, Michael T Eadon, Yao Chen, Anna Sun, Yuedi Yang, Chienwei Chiang, Macarius Donneyong, Jing Su, Pengyue Zhang","doi":"10.1002/sim.10216","DOIUrl":"https://doi.org/10.1002/sim.10216","url":null,"abstract":"Despite the success of pharmacovigilance studies in detecting signals of adverse drug events (ADEs) from real-world data, the risks of ADEs in subpopulations warrant increased scrutiny to prevent them in vulnerable individuals. Recently, the case-crossover design has been implemented to leverage large-scale administrative claims data for ADE detection, while controlling both observed confounding effects and short-term fixed unobserved confounding effects. Additionally, as the case-crossover design only includes cases, subpopulations can be conveniently derived. In this manuscript, we propose a precision mixture risk model (PMRM) to identify ADE signals from subpopulations under the case-crossover design. The proposed model is able to identify signals from all ADE-subpopulation-drug combinations, while controlling for false discovery rate (FDR) and confounding effects. We applied the PMRM to an administrative claims data. We identified ADE signals in subpopulations defined by demographic variables, comorbidities, and detailed diagnosis codes. Interestingly, certain drugs were associated with a higher risk of ADE only in subpopulations, while these drugs had a neutral association with ADE in the general population. Additionally, the PMRM could control FDR at a desired level and had a higher probability to detect true ADE signals than the widely used McNemar's test. In conclusion, the PMRM is able to identify subpopulation-specific ADE signals from a tremendous number of ADE-subpopulation-drug combinations, while controlling for both FDR and confounding effects.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142295957","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

Transportability of model‐based estimands in evidence synthesis 基于模型的估计值在证据综合中的可移植性

IF 2 4区医学

Statistics in Medicine Pub Date : 2024-09-18 DOI: 10.1002/sim.10111

Antonio Remiro‐Azócar

{"title":"Transportability of model‐based estimands in evidence synthesis","authors":"Antonio Remiro‐Azócar","doi":"10.1002/sim.10111","DOIUrl":"https://doi.org/10.1002/sim.10111","url":null,"abstract":"In evidence synthesis, effect modifiers are typically described as variables that induce treatment effect heterogeneity at the individual level, through treatment‐covariate interactions in an outcome model parametrized at such level. As such, effect modification is defined with respect to a conditional measure, but marginal effect estimates are required for population‐level decisions in health technology assessment. For noncollapsible measures, purely prognostic variables that are not determinants of treatment response at the individual level may modify marginal effects, even where there is individual‐level treatment effect homogeneity. With heterogeneity, marginal effects for measures that are not directly collapsible cannot be expressed in terms of marginal covariate moments, and generally depend on the joint distribution of conditional effect measure modifiers and purely prognostic variables. There are implications for recommended practices in evidence synthesis. Unadjusted anchored indirect comparisons can be biased in the absence of individual‐level treatment effect heterogeneity, or when marginal covariate moments are balanced across studies. Covariate adjustment may be necessary to account for cross‐study imbalances in joint covariate distributions involving purely prognostic variables. In the absence of individual patient data for the target, covariate adjustment approaches are inherently limited in their ability to remove bias for measures that are not directly collapsible. Directly collapsible measures would facilitate the transportability of marginal effects between studies by: (1) reducing dependence on model‐based covariate adjustment where there is individual‐level treatment effect homogeneity or marginal covariate moments are balanced; and (2) facilitating the selection of baseline covariates for adjustment where there is individual‐level treatment effect heterogeneity.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265865","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

Nonparametric Biomarker Based Treatment Selection With Reproducibility Data 基于非参数生物标志物的治疗选择与可重复性数据

IF 2 4区医学

Statistics in Medicine Pub Date : 2024-09-18 DOI: 10.1002/sim.10218

Sara Byers, Xiao Song

{"title":"Nonparametric Biomarker Based Treatment Selection With Reproducibility Data","authors":"Sara Byers, Xiao Song","doi":"10.1002/sim.10218","DOIUrl":"https://doi.org/10.1002/sim.10218","url":null,"abstract":"We consider evaluating biomarkers for treatment selection under assay modification. Survival outcome, treatment, and Affymetrix gene expression data were attained from cancer patients. Consider migrating a gene expression biomarker to the Illumina platform. A recent novel approach allows a quick evaluation of the migrated biomarker with only a reproducibility study needed to compare the two platforms, achieved by treating the original biomarker as an error‐contaminated observation of the migrated biomarker. However, its assumptions of a classical measurement error model and a linear predictor for the outcome may not hold. Ignoring such model deviations may lead to sub‐optimal treatment selection or failure to identify effective biomarkers. To overcome such limitations, we adopt a nonparametric logistic regression to model the relationship between the event rate and the biomarker, and the deduced marker‐based treatment selection is optimal. We further assume a nonparametric relationship between the migrated and original biomarkers and show that the error‐contaminated biomarker leads to sub‐optimal treatment selection compared to the error‐free biomarker. We obtain the estimation via B‐spline approximation. The approach is assessed by simulation studies and demonstrated through application to lung cancer data.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265863","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