Biostatistics最新文献_第8页

A Bayesian approach for investigating the pharmacogenetics of combination antiretroviral therapy in people with HIV. 研究艾滋病病毒感染者抗逆转录病毒联合疗法药物遗传学的贝叶斯方法。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-02-14 DOI: 10.1093/biostatistics/kxae001

Wei Jin, Yang Ni, Amanda B Spence, Leah H Rubin, Yanxun Xu

{"title":"A Bayesian approach for investigating the pharmacogenetics of combination antiretroviral therapy in people with HIV.","authors":"Wei Jin, Yang Ni, Amanda B Spence, Leah H Rubin, Yanxun Xu","doi":"10.1093/biostatistics/kxae001","DOIUrl":"10.1093/biostatistics/kxae001","url":null,"abstract":"Combination antiretroviral therapy (ART) with at least three different drugs has become the standard of care for people with HIV (PWH) due to its exceptional effectiveness in viral suppression. However, many ART drugs have been reported to associate with neuropsychiatric adverse effects including depression, especially when certain genetic polymorphisms exist. Pharmacogenetics is an important consideration for administering combination ART as it may influence drug efficacy and increase risk for neuropsychiatric conditions. Large-scale longitudinal HIV databases provide researchers opportunities to investigate the pharmacogenetics of combination ART in a data-driven manner. However, with more than 30 FDA-approved ART drugs, the interplay between the large number of possible ART drug combinations and genetic polymorphisms imposes statistical modeling challenges. We develop a Bayesian approach to examine the longitudinal effects of combination ART and their interactions with genetic polymorphisms on depressive symptoms in PWH. The proposed method utilizes a Gaussian process with a composite kernel function to capture the longitudinal combination ART effects by directly incorporating individuals' treatment histories, and a Bayesian classification and regression tree to account for individual heterogeneity. Through both simulation studies and an application to a dataset from the Women's Interagency HIV Study, we demonstrate the clinical utility of the proposed approach in investigating the pharmacogenetics of combination ART and assisting physicians to make effective individualized treatment decisions that can improve health outcomes for PWH.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139747854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator. 使用剩余生命值估算器，利用电子病历数据估算最佳治疗方案。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-02-09 DOI: 10.1093/biostatistics/kxae002

Grace Rhodes, Marie Davidian, Wenbin Lu

{"title":"Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator.","authors":"Grace Rhodes, Marie Davidian, Wenbin Lu","doi":"10.1093/biostatistics/kxae002","DOIUrl":"10.1093/biostatistics/kxae002","url":null,"abstract":"Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating patient information, like that commonly found in electronic medical record (EMR) data. When applied to a patient population, an optimal treatment regime leads to the most favorable outcome on average. Identifying optimal treatment regimes that maximize residual life is especially desirable for patients with life-threatening diseases such as sepsis, a complex medical condition that involves severe infections with organ dysfunction. We introduce the residual life value estimator (ReLiVE), an estimator for the expected value of cumulative restricted residual life under a fixed treatment regime. Building on ReLiVE, we present a method for estimating an optimal treatment regime that maximizes expected cumulative restricted residual life. Our proposed method, ReLiVE-Q, conducts estimation via the backward induction algorithm Q-learning. We illustrate the utility of ReLiVE-Q in simulation studies, and we apply ReLiVE-Q to estimate an optimal treatment regime for septic patients in the intensive care unit using EMR data from the Multiparameter Intelligent Monitoring Intensive Care database. Ultimately, we demonstrate that ReLiVE-Q leverages accumulating patient information to estimate personalized treatment regimes that optimize a clinically meaningful function of residual life.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139708547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian semiparametric model for sequential treatment decisions with informative timing. 具有信息时间的序列治疗决策的贝叶斯半参数模型。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-01-16 DOI: 10.1093/biostatistics/kxad035

Arman Oganisian, Kelly D Getz, Todd A Alonzo, Richard Aplenc, Jason A Roy

{"title":"Bayesian semiparametric model for sequential treatment decisions with informative timing.","authors":"Arman Oganisian, Kelly D Getz, Todd A Alonzo, Richard Aplenc, Jason A Roy","doi":"10.1093/biostatistics/kxad035","DOIUrl":"10.1093/biostatistics/kxad035","url":null,"abstract":"We develop a Bayesian semiparametric model for the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in a phase III clinical trial in which patients move through a sequence of four treatment courses. At each course, they undergo treatment that may or may not include anthracyclines (ACT). While ACT is known to be effective at treating AML, it is also cardiotoxic and can lead to early death for some patients. Our task is to estimate the potential survival probability under hypothetical dynamic ACT treatment strategies, but there are several impediments. First, since ACT is not randomized, its effect on survival is confounded over time. Second, subjects initiate the next course depending on when they recover from the previous course, making timing potentially informative of subsequent treatment and survival. Third, patients may die or drop out before ever completing the full treatment sequence. We develop a generative Bayesian semiparametric model based on Gamma Process priors to address these complexities. At each treatment course, the model captures subjects' transition to subsequent treatment or death in continuous time. G-computation is used to compute a posterior over potential survival probability that is adjusted for time-varying confounding. Using our approach, we estimate the efficacy of hypothetical treatment rules that dynamically modify ACT based on evolving cardiac function.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139479547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes 医院盈利状况与心脏病发作结果的全国性观察研究中的可扩展内核平衡权重

IF 2.1 3区数学

Biostatistics Pub Date : 2023-12-21 DOI: 10.1093/biostatistics/kxad032

Kwangho Kim, Bijan A Niknam, José R Zubizarreta

{"title":"Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes","authors":"Kwangho Kim, Bijan A Niknam, José R Zubizarreta","doi":"10.1093/biostatistics/kxad032","DOIUrl":"https://doi.org/10.1093/biostatistics/kxad032","url":null,"abstract":"Summary Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the weights have minimal dispersion and thus produce a more stable estimator. A recent, increasingly common approach directly optimizes the weights toward these two objectives. However, this approach has not yet been feasible in large-scale datasets when investigators wish to flexibly balance general basis functions in an extended feature space. To address this practical problem, we describe a scalable and flexible approach to weighting that integrates a basis expansion in a reproducing kernel Hilbert space with state-of-the-art convex optimization techniques. Specifically, we use the rank-restricted Nyström method to efficiently compute a kernel basis for balancing in nearly linear time and space, and then use the specialized first-order alternating direction method of multipliers to rapidly find the optimal weights. In an extensive simulation study, we provide new insights into the performance of weighting estimators in large datasets, showing that the proposed approach substantially outperforms others in terms of accuracy and speed. Finally, we use this weighting approach to conduct a national study of the relationship between hospital profit status and heart attack outcomes in a comprehensive dataset of 1.27 million patients. We find that for-profit hospitals use interventional cardiology to treat heart attacks at similar rates as other hospitals but have higher mortality and readmission rates.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138823499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An imputation approach for a time-to-event analysis subject to missing outcomes due to noncoverage in disease registries. 针对因疾病登记未覆盖而导致结果缺失的时间到事件分析的估算方法。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-12-15 DOI: 10.1093/biostatistics/kxac049

Joanna H Shih, Paul S Albert, Jason Fine, Danping Liu

{"title":"An imputation approach for a time-to-event analysis subject to missing outcomes due to noncoverage in disease registries.","authors":"Joanna H Shih, Paul S Albert, Jason Fine, Danping Liu","doi":"10.1093/biostatistics/kxac049","DOIUrl":"10.1093/biostatistics/kxac049","url":null,"abstract":"Disease incidence data in a national-based cohort study would ideally be obtained through a national disease registry. Unfortunately, no such registry currently exists in the United States. Instead, the results from individual state registries need to be combined to ascertain certain disease diagnoses in the United States. The National Cancer Institute has initiated a program to assemble all state registries to provide a complete assessment of all cancers in the United States. Unfortunately, not all registries have agreed to participate. In this article, we develop an imputation-based approach that uses self-reported cancer diagnosis from longitudinally collected questionnaires to impute cancer incidence not covered by the combined registry. We propose a two-step procedure, where in the first step a mover-stayer model is used to impute a participant's registry coverage status when it is only reported at the time of the questionnaires given at 10-year intervals and the time of the last-alive vital status and death. In the second step, we propose a semiparametric working model, fit using an imputed coverage area sample identified from the mover-stayer model, to impute registry-based survival outcomes for participants in areas not covered by the registry. The simulation studies show the approach performs well as compared with alternative ad hoc approaches for dealing with this problem. We illustrate the methodology with an analysis that links the United States Radiologic Technologists study cohort with the combined registry that includes 32 of the 50 states.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10939403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10751930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint modeling of longitudinal and competing-risk data using cumulative incidence functions for the failure submodels accounting for potential failure cause misclassification through double sampling. 利用故障子模型的累积发生率函数对纵向数据和竞争风险数据进行联合建模，并考虑到通过双重抽样可能出现的故障原因分类错误。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-12-15 DOI: 10.1093/biostatistics/kxac043

Christos Thomadakis, Loukia Meligkotsidou, Constantin T Yiannoutsos, Giota Touloumi

{"title":"Joint modeling of longitudinal and competing-risk data using cumulative incidence functions for the failure submodels accounting for potential failure cause misclassification through double sampling.","authors":"Christos Thomadakis, Loukia Meligkotsidou, Constantin T Yiannoutsos, Giota Touloumi","doi":"10.1093/biostatistics/kxac043","DOIUrl":"10.1093/biostatistics/kxac043","url":null,"abstract":"Most of the literature on joint modeling of longitudinal and competing-risk data is based on cause-specific hazards, although modeling of the cumulative incidence function (CIF) is an easier and more direct approach to evaluate the prognosis of an event. We propose a flexible class of shared parameter models to jointly model a normally distributed marker over time and multiple causes of failure using CIFs for the survival submodels, with CIFs depending on the \"true\" marker value over time (i.e., removing the measurement error). The generalized odds rate transformation is applied, thus a proportional subdistribution hazards model is a special case. The requirement that the all-cause CIF should be bounded by 1 is formally considered. The proposed models are extended to account for potential failure cause misclassification, where the true failure causes are available in a small random sample of individuals. We also provide a multistate representation of the whole population by defining mutually exclusive states based on the marker values and the competing risks. Based solely on the assumed joint model, we derive fully Bayesian posterior samples for state occupation and transition probabilities. The proposed approach is evaluated in a simulation study and, as an illustration, it is fitted to real data from people with HIV.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10724131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40664538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inference after latent variable estimation for single-cell RNA sequencing data. 单细胞 RNA 测序数据的潜变量估计后推断。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-12-15 DOI: 10.1093/biostatistics/kxac047

Anna Neufeld, Lucy L Gao, Joshua Popp, Alexis Battle, Daniela Witten

引用次数: 12

A scalable and unbiased discordance metric with H. 带 H 的可扩展且无偏的不和谐度量。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-12-15 DOI: 10.1093/biostatistics/kxac035

Nathan Dyjack, Daniel N Baker, Vladimir Braverman, Ben Langmead, Stephanie C Hicks

{"title":"A scalable and unbiased discordance metric with H.","authors":"Nathan Dyjack, Daniel N Baker, Vladimir Braverman, Ben Langmead, Stephanie C Hicks","doi":"10.1093/biostatistics/kxac035","DOIUrl":"10.1093/biostatistics/kxac035","url":null,"abstract":"A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or separation of the clusters, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the \"scale-agnostic\" $G_{+}$ discordance metric; however, this internal metric is slow to calculate for large data. Furthermore, in the setting of unsupervised clustering with $k$ groups, we show that $G_{+}$ varies as a function of the proportion of observations assigned to each of the groups (or clusters), referred to as the group balance, which is an undesirable property. To address this problem, we propose a modification of $G_{+}$, referred to as $H_{+}$, and demonstrate that $H_{+}$ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate $H_{+}$, which are available in the $mathtt{fasthplus}$ R package.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10724244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40350600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multilayer Exponential Family Factor models for integrative analysis and learning disease progression. 用于综合分析和学习疾病进展的多层指数族因子模型

IF 2.1 3区数学

Biostatistics Pub Date : 2023-12-15 DOI: 10.1093/biostatistics/kxac042

Qinxia Wang, Yuanjia Wang

{"title":"Multilayer Exponential Family Factor models for integrative analysis and learning disease progression.","authors":"Qinxia Wang, Yuanjia Wang","doi":"10.1093/biostatistics/kxac042","DOIUrl":"10.1093/biostatistics/kxac042","url":null,"abstract":"Current diagnosis of neurological disorders often relies on late-stage clinical symptoms, which poses barriers to developing effective interventions at the premanifest stage. Recent research suggests that biomarkers and subtle changes in clinical markers may occur in a time-ordered fashion and can be used as indicators of early disease. In this article, we tackle the challenges to leverage multidomain markers to learn early disease progression of neurological disorders. We propose to integrate heterogeneous types of measures from multiple domains (e.g., discrete clinical symptoms, ordinal cognitive markers, continuous neuroimaging, and blood biomarkers) using a hierarchical Multilayer Exponential Family Factor (MEFF) model, where the observations follow exponential family distributions with lower-dimensional latent factors. The latent factors are decomposed into shared factors across multiple domains and domain-specific factors, where the shared factors provide robust information to perform extensive phenotyping and partition patients into clinically meaningful and biologically homogeneous subgroups. Domain-specific factors capture remaining unique variations for each domain. The MEFF model also captures nonlinear trajectory of disease progression and orders critical events of neurodegeneration measured by each marker. To overcome computational challenges, we fit our model by approximate inference techniques for large-scale data. We apply the developed method to Parkinson's Progression Markers Initiative data to integrate biological, clinical, and cognitive markers arising from heterogeneous distributions. The model learns lower-dimensional representations of Parkinson's disease (PD) and the temporal ordering of the neurodegeneration of PD.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10939400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10653302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatiotemporal varying coefficient model for respiratory disease mapping in Taiwan. 用于绘制台湾呼吸道疾病地图的时空变化系数模型。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-12-15 DOI: 10.1093/biostatistics/kxac046

Feifei Wang, Congyuan Duan, Yang Li, Hui Huang, Ben-Chang Shia

引用次数: 0