Biostatistics最新文献_第3页

Within-trial data borrowing for sequential multiple assignment randomized trials. 序贯多任务随机试验的试验内数据借用。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf003

Ales Kotalik, David M Vock, Nancy E Sherwood, Brian P Hobbs, Joseph S Koopmeiners

{"title":"Within-trial data borrowing for sequential multiple assignment randomized trials.","authors":"Ales Kotalik, David M Vock, Nancy E Sherwood, Brian P Hobbs, Joseph S Koopmeiners","doi":"10.1093/biostatistics/kxaf003","DOIUrl":"10.1093/biostatistics/kxaf003","url":null,"abstract":"The Sequential Multiple Assignment Randomized Trial (SMART) is a complex trial design that involves randomizing a single participant multiple times in a sequential manner. This results in the branching nature of a SMART, which represents several distinct groups defined by different combinations of treatments, response statuses, etc. A SMART can then answer various scientific questions of interest, eg, the optimal dynamic treatment regime (DTR) for treating a chronic illness, what intervention to offer first, and what intervention to offer to nonresponders (or suboptimal responders). However, the analysis of a SMART can suffer from low precision, as the potentially widely branching structure can lead to reduced sample sizes in some groups of interest. In this paper, we propose a novel analysis method for a SMART in which dynamic borrowing is used to borrow strength across groups with similar expected outcomes, thus providing increased precision for the estimation of the expected outcomes of DTRs. We apply our method to a SMART evaluating various weight loss strategies using a binary endpoint of clinically significant weight loss and show by simulation that our method can improve the precision of the estimated expected outcome of a DTR, aid in the identification of the optimal DTR, and produce a clustering analysis of DTRs embedded in a SMART.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765923","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

A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data. 用于序数和连续纵向数据的正态-序数（probit）联合模型。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae014

Margaux Delporte, Geert Molenberghs, Steffen Fieuws, Geert Verbeke

{"title":"A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data.","authors":"Margaux Delporte, Geert Molenberghs, Steffen Fieuws, Geert Verbeke","doi":"10.1093/biostatistics/kxae014","DOIUrl":"10.1093/biostatistics/kxae014","url":null,"abstract":"In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariates have, however, several limitations; they can, for example, not be included when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models, where two or more longitudinal variables are treated as a response and modeled with a correlated random effect. Next, by conditioning on these response(s), we can study the effect of one or more longitudinal variables on another. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the model-based correlations between the responses on their original scale. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response. Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology is applied to a case study where, among others, a longitudinal ordinal response is predicted with a longitudinal continuous variable.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312354","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

Direct estimation and inference of higher-level correlations from lower-level measurements with applications in gene-pathway and proteomics studies. 从较低层次的测量结果直接估计和推断较高层次的相关性，并将其应用于基因通路和蛋白质组学研究。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae027

Yue Wang, Haoran Shi

{"title":"Direct estimation and inference of higher-level correlations from lower-level measurements with applications in gene-pathway and proteomics studies.","authors":"Yue Wang, Haoran Shi","doi":"10.1093/biostatistics/kxae027","DOIUrl":"10.1093/biostatistics/kxae027","url":null,"abstract":"This paper tackles the challenge of estimating correlations between higher-level biological variables (e.g. proteins and gene pathways) when only lower-level measurements are directly observed (e.g. peptides and individual genes). Existing methods typically aggregate lower-level data into higher-level variables and then estimate correlations based on the aggregated data. However, different data aggregation methods can yield varying correlation estimates as they target different higher-level quantities. Our solution is a latent factor model that directly estimates these higher-level correlations from lower-level data without the need for data aggregation. We further introduce a shrinkage estimator to ensure the positive definiteness and improve the accuracy of the estimated correlation matrix. Furthermore, we establish the asymptotic normality of our estimator, enabling efficient computation of P-values for the identification of significant correlations. The effectiveness of our approach is demonstrated through comprehensive simulations and the analysis of proteomics and gene expression datasets. We develop the R package highcor for implementing our method.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861746","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

Semiparametric mixture regression for asynchronous longitudinal data using multivariate functional principal component analysis. 基于多元泛函主成分分析的异步纵向数据半参数混合回归。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf008

Ruihan Lu, Yehua Li, Weixin Yao

{"title":"Semiparametric mixture regression for asynchronous longitudinal data using multivariate functional principal component analysis.","authors":"Ruihan Lu, Yehua Li, Weixin Yao","doi":"10.1093/biostatistics/kxaf008","DOIUrl":"10.1093/biostatistics/kxaf008","url":null,"abstract":"The transitional phase of menopause induces significant hormonal fluctuations, exerting a profound influence on the long-term well-being of women. In an extensive longitudinal investigation of women's health during mid-life and beyond, known as the Study of Women's Health Across the Nation (SWAN), hormonal biomarkers are repeatedly assessed, following an asynchronous schedule compared to other error-prone covariates, such as physical and cardiovascular measurements. We conduct a subgroup analysis of the SWAN data employing a semiparametric mixture regression model, which allows us to explore how the relationship between hormonal responses and other time-varying or time-invariant covariates varies across subgroups. To address the challenges posed by asynchronous scheduling and measurement errors, we model the time-varying covariate trajectories as functional data with reduced-rank Karhunen-Loéve expansions, where splines are employed to capture the mean and eigenfunctions. Treating the latent subgroup membership and the functional principal component (FPC) scores as missing data, we propose an Expectation-Maximization algorithm to effectively fit the joint model, combining the mixture regression for the hormonal response and the FPC model for the asynchronous, time-varying covariates. In addition, we explore data-driven methods to determine the optimal number of subgroups within the population. Through our comprehensive analysis of the SWAN data, we unveil a crucial subgroup structure within the aging female population, shedding light on important distinctions and patterns among women undergoing menopause.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694532","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

Bayesian estimation of covariate assisted principal regression for brain functional connectivity. 针对大脑功能连接性的协变量辅助主回归贝叶斯估计。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae023

Hyung G Park

引用次数: 0

Correction to: Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes. 修正：在一项全国性的医院盈利状况和心脏病发作结果的观察性研究中，可扩展的核平衡权值。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae050

引用次数: 0

Recurrent events modeling based on a reflected Brownian motion with application to hypoglycemia. 基于反射布朗运动的反复事件模型及其在低血糖中的应用。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae053

Yingfa Xie, Haoda Fu, Yuan Huang, Vladimir Pozdnyakov, Jun Yan

{"title":"Recurrent events modeling based on a reflected Brownian motion with application to hypoglycemia.","authors":"Yingfa Xie, Haoda Fu, Yuan Huang, Vladimir Pozdnyakov, Jun Yan","doi":"10.1093/biostatistics/kxae053","DOIUrl":"10.1093/biostatistics/kxae053","url":null,"abstract":"Patients with type 2 diabetes need to closely monitor blood sugar levels as their routine diabetes self-management. Although many treatment agents aim to tightly control blood sugar, hypoglycemia often stands as an adverse event. In practice, patients can observe hypoglycemic events more easily than hyperglycemic events due to the perception of neurogenic symptoms. We propose to model each patient's observed hypoglycemic event as a lower boundary crossing event for a reflected Brownian motion with an upper reflection barrier. The lower boundary is set by clinical standards. To capture patient heterogeneity and within-patient dependence, covariates and a patient level frailty are incorporated into the volatility and the upper reflection barrier. This framework provides quantification for the underlying glucose level variability, patients heterogeneity, and risk factors' impact on glucose. We make inferences based on a Bayesian framework using Markov chain Monte Carlo. Two model comparison criteria, the deviance information criterion and the logarithm of the pseudo-marginal likelihood, are used for model selection. The methodology is validated in simulation studies. In analyzing a dataset from the diabetic patients in the DURABLE trial, our model provides adequate fit, generates data similar to the observed data, and offers insights that could be missed by other models.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048852","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

Testing for a difference in means of a single feature after clustering. 聚类后对单个特征的均值差异进行测试。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxae046

Yiqun T Chen, Lucy L Gao

引用次数: 0

Causal functional mediation analysis with an application to functional magnetic resonance imaging data. 因果功能中介分析及其在功能磁共振成像数据中的应用。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-12-31 DOI: 10.1093/biostatistics/kxaf019

Yi Zhao, Xi Luo, Michael E Sobel, Martin A Lindquist, Brian S Caffo

引用次数: 0

Model-based multifacet clustering with high-dimensional omics applications. 基于模型的多面聚类与高维 omics 应用。