Statistics in Medicine最新文献_第2页

Double Sampling for Informatively Missing Data in Electronic Health Record-Based Comparative Effectiveness Research. 基于电子健康记录的比较有效性研究中信息缺失数据的双重抽样。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-12-05 DOI: 10.1002/sim.10298

Alexander W Levis, Rajarshi Mukherjee, Rui Wang, Heidi Fischer, Sebastien Haneuse

{"title":"Double Sampling for Informatively Missing Data in Electronic Health Record-Based Comparative Effectiveness Research.","authors":"Alexander W Levis, Rajarshi Mukherjee, Rui Wang, Heidi Fischer, Sebastien Haneuse","doi":"10.1002/sim.10298","DOIUrl":"10.1002/sim.10298","url":null,"abstract":"Missing data arise in most applied settings and are ubiquitous in electronic health records (EHR). When data are missing not at random (MNAR) with respect to measured covariates, sensitivity analyses are often considered. These solutions, however, are often unsatisfying in that they are not guaranteed to yield actionable conclusions. Motivated by an EHR-based study of long-term outcomes following bariatric surgery, we consider the use of double sampling as a means to mitigate MNAR outcome data when the statistical goals are estimation and inference regarding causal effects. We describe assumptions that are sufficient for the identification of the joint distribution of confounders, treatment, and outcome under this design. Additionally, we derive efficient and robust estimators of the average causal treatment effect under a nonparametric model and under a model assuming outcomes were, in fact, initially missing at random (MAR). We compare these in simulations to an approach that adaptively estimates based on evidence of violation of the MAR assumption. Finally, we also show that the proposed double sampling design can be extended to handle arbitrary coarsening mechanisms, and derive nonparametric efficient estimators of any smooth full data functional.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"6086-6098"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786604","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 Treatment Regimes on Dyadic Networks. 二元网络上的动态处理机制。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-11-28 DOI: 10.1002/sim.10278

Marizeh Mussavi Rizi, Joel A Dubin, Micheal P Wallace

{"title":"Dynamic Treatment Regimes on Dyadic Networks.","authors":"Marizeh Mussavi Rizi, Joel A Dubin, Micheal P Wallace","doi":"10.1002/sim.10278","DOIUrl":"10.1002/sim.10278","url":null,"abstract":"Identifying interventions that are optimally tailored to each individual is of significant interest in various fields, in particular precision medicine. Dynamic treatment regimes (DTRs) employ sequences of decision rules that utilize individual patient information to recommend treatments. However, the assumption that an individual's treatment does not impact the outcomes of others, known as the no interference assumption, is often challenged in practical settings. For example, in infectious disease studies, the vaccine status of individuals in close proximity can influence the likelihood of infection. Imposing this assumption when it, in fact, does not hold, may lead to biased results and impact the validity of the resulting DTR optimization. We extend the estimation method of dynamic weighted ordinary least squares (dWOLS), a doubly robust and easily implemented approach for estimating optimal DTRs, to incorporate the presence of interference within dyads (i.e., pairs of individuals). We formalize an appropriate outcome model and describe the estimation of an optimal decision rule in the dyadic-network context. Through comprehensive simulations and analysis of the Population Assessment of Tobacco and Health (PATH) data, we demonstrate the improved performance of the proposed joint optimization strategy compared to the current state-of-the-art conditional optimization methods in estimating the optimal treatment assignments when within-dyad interference exists.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5944-5967"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142751738","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 General Framework for the Multiple Nonparametric Behrens-Fisher Problem With Dependent Replicates. 依赖复制的多重非参数 Behrens-Fisher 问题的一般框架》（A General Framework for the Multiple Nonparametric Behrens-Fisher Problem With Dependent Replicates.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-11-11 DOI: 10.1002/sim.10262

Erin Sprünken, Robert Mertens, Frank Konietschke

{"title":"A General Framework for the Multiple Nonparametric Behrens-Fisher Problem With Dependent Replicates.","authors":"Erin Sprünken, Robert Mertens, Frank Konietschke","doi":"10.1002/sim.10262","DOIUrl":"10.1002/sim.10262","url":null,"abstract":"In many trials and experiments, subjects are not only observed once but multiple times, resulting in a cluster of possibly correlated observations (e.g., brain regions per patient). Observations often do not fulfill model assumptions of mixed models and require the use of nonparametric methods. In this article, we develop and present a purely nonparametric rank-based procedure that flexibly allows the unbiased and consistent estimation of the Wilcoxon-Mann-Whitney effect <math> <semantics><mrow><mi>P</mi> <mo>(</mo> <mi>X</mi> <mo><</mo> <mi>Y</mi> <mo>)</mo> <mo>+</mo> <mfrac><mrow><mn>1</mn></mrow> <mrow><mn>2</mn></mrow> </mfrac> <mi>P</mi> <mo>(</mo> <mi>X</mi> <mo>=</mo> <mi>Y</mi> <mo>)</mo></mrow> <annotation>$$ Pleft(X<Yright)+frac{1}{2}Pleft(X=Yright) $$</annotation></semantics> </math> in clustered data designs. Compared with existing methods, we allow flexible weights to be used in effect estimation. Additionally, we develop global and multiple contrast test procedures to test null hypotheses formulated regarding the generalized Mann-Whitney effects and for the computation of range-preserving simultaneous confidence intervals in a unified way. Extensive simulation studies show that these methods control the type-I error rate well and have reasonable power to detect alternatives in various situations.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5650-5666"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627997","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

Shape Mediation Analysis in Alzheimer's Disease Studies. 阿尔茨海默病研究中的形状中介分析。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-11-12 DOI: 10.1002/sim.10265

Xingcai Zhou, Miyeon Yeon, Jiangyan Wang, Shengxian Ding, Kaizhou Lei, Yanyong Zhao, Rongjie Liu, Chao Huang

{"title":"Shape Mediation Analysis in Alzheimer's Disease Studies.","authors":"Xingcai Zhou, Miyeon Yeon, Jiangyan Wang, Shengxian Ding, Kaizhou Lei, Yanyong Zhao, Rongjie Liu, Chao Huang","doi":"10.1002/sim.10265","DOIUrl":"10.1002/sim.10265","url":null,"abstract":"As a crucial tool in neuroscience, mediation analysis has been developed and widely adopted to elucidate the role of intermediary variables derived from neuroimaging data. Typically, structural equation models (SEMs) are employed to investigate the influences of exposures on outcomes, with model coefficients being interpreted as causal effects. While existing SEMs have proven to be effective tools for mediation analysis involving various neuroimaging-related mediators, limited research has explored scenarios where these mediators are derived from the shape space. In addition, the linear relationship assumption adopted in existing SEMs may lead to substantial efficiency losses and decreased predictive accuracy in real-world applications. To address these challenges, we introduce a novel framework for shape mediation analysis, designed to explore the causal relationships between genetic exposures and clinical outcomes, whether mediated or unmediated by shape-related factors while accounting for potential confounding variables. Within our framework, we apply the square-root velocity function to extract elastic shape representations, which reside within the linear Hilbert space of square-integrable functions. Subsequently, we introduce a two-layer shape regression model to characterize the relationships among neurocognitive outcomes, elastic shape mediators, genetic exposures, and clinical confounders. Both estimation and inference procedures are established for unknown parameters along with the corresponding causal estimands. The asymptotic properties of estimated quantities are investigated as well. Both simulated studies and real-data analyses demonstrate the superior performance of our proposed method in terms of estimation accuracy and robustness when compared to existing approaches for estimating causal estimands.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5698-5710"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142628013","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

Instrumental Variable Model Average With Applications in Nonlinear Causal Inference. 工具变量模型平均与非线性因果推理中的应用》（Instrumental Variable Model Average With Applications in Nonlinear Causal Inference）。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-11-18 DOI: 10.1002/sim.10269

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

{"title":"Instrumental Variable Model Average With Applications in Nonlinear Causal Inference.","authors":"Dong Chen, Yuquan Wang, Dapeng Shi, Yunlong Cao, Yue-Qing Hu","doi":"10.1002/sim.10269","DOIUrl":"10.1002/sim.10269","url":null,"abstract":"The instrumental variable method is widely used in causal inference research to improve the accuracy of estimating causal effects. However, the weak correlation between instruments and exposure, as well as the direct impact of instruments on the outcome, can lead to biased estimates. To mitigate the bias introduced by such instruments in nonlinear causal inference, we propose a two-stage nonlinear causal effect estimation based on model averaging. The model uses different subsets of instruments in the first stage to predict exposure after a nonlinear transformation with the help of sliced inverse regression. In the second stage, adaptive Lasso penalty is applied to instruments to obtain the estimation of causal effect. We prove that the proposed estimator exhibits favorable asymptotic properties and evaluate its performance through a series of numerical studies, demonstrating its effectiveness in identifying nonlinear causal effects and its capability to handle scenarios with weak and invalid instruments. We apply the proposed method to the Atherosclerosis Risk in Communities dataset to investigate the relationship between BMI and hypertension.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5814-5836"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669200","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

Joint Modelling of Longitudinal Measurements and Time-to-Event Outcomes With a Cure Fraction Using Functional Principal Component Analysis. 联合建模纵向测量和时间到事件的结果与治愈分数使用功能主成分分析。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-12-05 DOI: 10.1002/sim.10289

Siyuan Guo, Jiajia Zhang, Susan Halabi

{"title":"Joint Modelling of Longitudinal Measurements and Time-to-Event Outcomes With a Cure Fraction Using Functional Principal Component Analysis.","authors":"Siyuan Guo, Jiajia Zhang, Susan Halabi","doi":"10.1002/sim.10289","DOIUrl":"10.1002/sim.10289","url":null,"abstract":"In studying the association between clinical measurements and time-to-event outcomes within a cure model, utilizing repeated observations rather than solely baseline values may lead to more accurate estimation. However, there are two main challenges in this context. First, longitudinal measurements are usually observed at discrete time points and second, for diseases that respond well to treatment, a high censoring proportion may occur by the end of the trial. In this article, we propose a joint modelling approach to simultaneously study the longitudinal observations and time-to-event outcome with an assumed cure fraction. We employ the functional principal components analysis (FPCA) to model the longitudinal data, offering flexibility by not assuming a specific form for the longitudinal curve. We used a Cox's proportional hazards mixture cure model to study the survival outcome. To investigate the longitudinal binary observations, we adopt a quasi-likelihood method which builds pseudo normal distribution for the binary data and use the E-M algorithm to estimate the parameters. The tuning parameters are selected using the Akaike information criterion. Our proposed method is evaluated through extensive simulation studies and applied to a clinical trial data to study the relationship between the longitudinal prostate specific antigen (PSA) measurements and overall survival in men with metastatic prostate cancer.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"6059-6072"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142781056","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 Partially Randomized Patient Preference, Sequential, Multiple-Assignment, Randomized Trial Design Analyzed via Weighted and Replicated Frequentist and Bayesian Methods. 通过加权和重复频数法及贝叶斯法分析的部分随机患者偏好、顺序、多重分配、随机试验设计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-11-17 DOI: 10.1002/sim.10276

Marianthie Wank, Sarah Medley, Roy N Tamura, Thomas M Braun, Kelley M Kidwell

{"title":"A Partially Randomized Patient Preference, Sequential, Multiple-Assignment, Randomized Trial Design Analyzed via Weighted and Replicated Frequentist and Bayesian Methods.","authors":"Marianthie Wank, Sarah Medley, Roy N Tamura, Thomas M Braun, Kelley M Kidwell","doi":"10.1002/sim.10276","DOIUrl":"10.1002/sim.10276","url":null,"abstract":"Results from randomized control trials (RCTs) may not be representative when individuals refuse to be randomized or are excluded for having a preference for which treatment they receive. If trial designs do not allow for participant treatment preferences, trials can suffer in accrual, adherence, retention, and external validity of results. Thus, there is interest surrounding clinical trial designs that incorporate participant treatment preferences. We propose a Partially Randomized, Patient Preference, Sequential, Multiple Assignment, Randomized Trial (PRPP-SMART) which combines a Partially Randomized, Patient Preference (PRPP) design with a Sequential, Multiple Assignment, Randomized Trial (SMART) design. This novel PRPP-SMART design is a multi-stage clinical trial design where, at each stage, participants either receive their preferred treatment, or if they do not have a preferred treatment, they are randomized. This paper focuses on the clinical trial design for PRPP-SMARTs and the development of Bayesian and frequentist weighted and replicated regression models (WRRMs) to analyze data from such trials. We propose a two-stage PRPP-SMART with binary end of stage outcomes and estimate the embedded dynamic treatment regimes (DTRs). Our WRRMs use data from both randomized and non-randomized participants for efficient estimation of the DTR effects. We compare our method to a more traditional PRPP analysis which only considers participants randomized to treatment. Our Bayesian and frequentist methods produce more efficient DTR estimates with negligible bias despite the inclusion of non-randomized participants in the analysis. The proposed PRPP-SMART design and analytic method is a promising approach to incorporate participant treatment preferences into clinical trial design.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5777-5790"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649175","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

Q-Learning in Dynamic Treatment Regimes With Misclassified Binary Outcome. 二元结果分类错误的动态治疗机制中的 Q-Learning

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-11-20 DOI: 10.1002/sim.10223

Dan Liu, Wenqing He

引用次数: 0

Statistical Inference for Box-Cox based Receiver Operating Characteristic Curves. 基于 Box-Cox 的受体工作特征曲线的统计推断。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-11-17 DOI: 10.1002/sim.10252

Leonidas E Bantis, Benjamin Brewer, Christos T Nakas, Benjamin Reiser

{"title":"Statistical Inference for Box-Cox based Receiver Operating Characteristic Curves.","authors":"Leonidas E Bantis, Benjamin Brewer, Christos T Nakas, Benjamin Reiser","doi":"10.1002/sim.10252","DOIUrl":"10.1002/sim.10252","url":null,"abstract":"Receiver operating characteristic (ROC) curve analysis is widely used in evaluating the effectiveness of a diagnostic test/biomarker or classifier score. A parametric approach for statistical inference on ROC curves based on a Box-Cox transformation to normality has frequently been discussed in the literature. Many investigators have highlighted the difficulty of taking into account the variability of the estimated transformation parameter when carrying out such an analysis. This variability is often ignored and inferences are made by considering the estimated transformation parameter as fixed and known. In this paper, we will review the literature discussing the use of the Box-Cox transformation for ROC curves and the methodology for accounting for the estimation of the Box-Cox transformation parameter in the context of ROC analysis, and detail its application to a number of problems. We present a general framework for inference on any functional of interest, including common measures such as the AUC, the Youden index, and the sensitivity at a given specificity (and vice versa). We further developed a new R package (named 'rocbc') that carries out all discussed approaches and is available in CRAN.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"6099-6122"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649177","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 Bayesian Approach to Modeling Variance of Intensive Longitudinal Biomarker Data as a Predictor of Health Outcomes. 用贝叶斯方法对作为健康结果预测因子的密集纵向生物标志物数据的方差进行建模。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-11-14 DOI: 10.1002/sim.10281

Mingyan Yu, Zhenke Wu, Margaret Hicken, Michael R Elliott

{"title":"A Bayesian Approach to Modeling Variance of Intensive Longitudinal Biomarker Data as a Predictor of Health Outcomes.","authors":"Mingyan Yu, Zhenke Wu, Margaret Hicken, Michael R Elliott","doi":"10.1002/sim.10281","DOIUrl":"10.1002/sim.10281","url":null,"abstract":"Intensive longitudinal biomarker data are increasingly common in scientific studies that seek temporally granular understanding of the role of behavioral and physiological factors in relation to outcomes of interest. Intensive longitudinal biomarker data, such as those obtained from wearable devices, are often obtained at a high frequency typically resulting in several hundred to thousand observations per individual measured over minutes, hours, or days. Often in longitudinal studies, the primary focus is on relating the means of biomarker trajectories to an outcome, and the variances are treated as nuisance parameters, although they may also be informative for the outcomes. In this paper, we propose a Bayesian hierarchical model to jointly model a cross-sectional outcome and the intensive longitudinal biomarkers. To model the variability of biomarkers and deal with the high intensity of data, we develop subject-level cubic B-splines and allow the sharing of information across individuals for both the residual variability and the random effects variability. Then different levels of variability are extracted and incorporated into an outcome submodel for inferential and predictive purposes. We demonstrate the utility of the proposed model via an application involving bio-monitoring of hertz-level heart rate information from a study on social stress.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5748-5764"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142627920","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