Biostatistics最新文献_第10页

Similarity-based multimodal regression. 基于相似性的多模态回归。

IF 1.8 3区数学

Biostatistics Pub Date : 2023-12-06 DOI: 10.1093/biostatistics/kxad033

Andrew A Chen, Sarah M Weinstein, Azeez Adebimpe, Ruben C Gur, Raquel E Gur, Kathleen R Merikangas, Theodore D Satterthwaite, Russell T Shinohara, Haochang Shou

{"title":"Similarity-based multimodal regression.","authors":"Andrew A Chen, Sarah M Weinstein, Azeez Adebimpe, Ruben C Gur, Raquel E Gur, Kathleen R Merikangas, Theodore D Satterthwaite, Russell T Shinohara, Haochang Shou","doi":"10.1093/biostatistics/kxad033","DOIUrl":"10.1093/biostatistics/kxad033","url":null,"abstract":"To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138500309","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

Evaluating dynamic and predictive discrimination for recurrent event models: use of a time-dependent C-index. 评估动态和预测判别的反复事件模型:使用时间相关的c指数。

IF 1.8 3区数学

Biostatistics Pub Date : 2023-11-10 DOI: 10.1093/biostatistics/kxad031

Jian Wang, Xinyang Jiang, Jing Ning

{"title":"Evaluating dynamic and predictive discrimination for recurrent event models: use of a time-dependent C-index.","authors":"Jian Wang, Xinyang Jiang, Jing Ning","doi":"10.1093/biostatistics/kxad031","DOIUrl":"10.1093/biostatistics/kxad031","url":null,"abstract":"Interest in analyzing recurrent event data has increased over the past few decades. One essential aspect of a risk prediction model for recurrent event data is to accurately distinguish individuals with different risks of developing a recurrent event. Although the concordance index (C-index) effectively evaluates the overall discriminative ability of a regression model for recurrent event data, a local measure is also desirable to capture dynamic performance of the regression model over time. Therefore, in this study, we propose a time-dependent C-index measure for inferring the model's discriminative ability locally. We formulated the C-index as a function of time using a flexible parametric model and constructed a concordance-based likelihood for estimation and inference. We adapted a perturbation-resampling procedure for variance estimation. Extensive simulations were conducted to investigate the proposed time-dependent C-index's finite-sample performance and estimation procedure. We applied the time-dependent C-index to three regression models of a study of re-hospitalization in patients with colorectal cancer to evaluate the models' discriminative capability.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89720651","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

Signal detection statistics of adverse drug events in hierarchical structure for matched case-control data. 匹配病例对照数据的分级结构中药物不良事件的信号检测统计。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-10-26 DOI: 10.1093/biostatistics/kxad029

Seok-Jae Heo, Sohee Jeong, Dagyeom Jung, Inkyung Jung

{"title":"Signal detection statistics of adverse drug events in hierarchical structure for matched case-control data.","authors":"Seok-Jae Heo, Sohee Jeong, Dagyeom Jung, Inkyung Jung","doi":"10.1093/biostatistics/kxad029","DOIUrl":"https://doi.org/10.1093/biostatistics/kxad029","url":null,"abstract":"The tree-based scan statistic is a data mining method used to identify signals of adverse drug reactions in a database of spontaneous reporting systems. It is particularly beneficial when dealing with hierarchical data structures. One may use a retrospective case-control study design from spontaneous reporting systems (SRS) to investigate whether a specific adverse event of interest is associated with certain drugs. However, the existing Bernoulli model of the tree-based scan statistic may not be suitable as it fails to adequately account for dependencies within matched pairs. In this article, we propose signal detection statistics for matched case-control data based on McNemar's test, Wald test for conditional logistic regression, and the likelihood ratio test for a multinomial distribution. Through simulation studies, we demonstrate that our proposed methods outperform the existing approach in terms of the type I error rate, power, sensitivity, and false detection rate. To illustrate our proposed approach, we applied the three methods and the existing method to detect drug signals for dizziness-related adverse events related to antihypertensive drugs using the database of the Korea Adverse Event Reporting System.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54232410","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

Doubly robust evaluation of high-dimensional surrogate markers. 高维替代标记的双重稳健评估。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-10-18 DOI: 10.1093/biostatistics/kxac020

Denis Agniel, Boris P Hejblum, Rodolphe Thiébaut, Layla Parast

{"title":"Doubly robust evaluation of high-dimensional surrogate markers.","authors":"Denis Agniel, Boris P Hejblum, Rodolphe Thiébaut, Layla Parast","doi":"10.1093/biostatistics/kxac020","DOIUrl":"10.1093/biostatistics/kxac020","url":null,"abstract":"When evaluating the effectiveness of a treatment, policy, or intervention, the desired measure of efficacy may be expensive to collect, not routinely available, or may take a long time to occur. In these cases, it is sometimes possible to identify a surrogate outcome that can more easily, quickly, or cheaply capture the effect of interest. Theory and methods for evaluating the strength of surrogate markers have been well studied in the context of a single surrogate marker measured in the course of a randomized clinical study. However, methods are lacking for quantifying the utility of surrogate markers when the dimension of the surrogate grows. We propose a robust and efficient method for evaluating a set of surrogate markers that may be high-dimensional. Our method does not require treatment to be randomized and may be used in observational studies. Our approach draws on a connection between quantifying the utility of a surrogate marker and the most fundamental tools of causal inference-namely, methods for robust estimation of the average treatment effect. This connection facilitates the use of modern methods for estimating treatment effects, using machine learning to estimate nuisance functions and relaxing the dependence on model specification. We demonstrate that our proposed approach performs well, demonstrate connections between our approach and certain mediation effects, and illustrate it by evaluating whether gene expression can be used as a surrogate for immune activation in an Ebola study.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10801117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9842686","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 design of clinical trials using joint models for recurrent and terminating events. 使用复发和终止事件的联合模型进行临床试验的贝叶斯设计。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-10-18 DOI: 10.1093/biostatistics/kxac025

Jiawei Xu, Matthew A Psioda, Joseph G Ibrahim

{"title":"Bayesian design of clinical trials using joint models for recurrent and terminating events.","authors":"Jiawei Xu, Matthew A Psioda, Joseph G Ibrahim","doi":"10.1093/biostatistics/kxac025","DOIUrl":"10.1093/biostatistics/kxac025","url":null,"abstract":"Joint models for recurrent event and terminating event data are increasingly used for the analysis of clinical trials. However, few methods have been proposed for designing clinical trials using these models. In this article, we develop a Bayesian clinical trial design methodology focused on evaluating the effect of an investigational product (IP) on both recurrent event and terminating event processes considered as multiple primary endpoints, using a multifrailty joint model. Dependence between the recurrent and terminating event processes is accounted for using a shared frailty. Inferences for the multiple primary outcomes are based on posterior model probabilities corresponding to mutually exclusive hypotheses regarding the benefit of IP with respect to the recurrent and terminating event processes. We propose an approach for sample size determination to ensure the trial design has a high power and a well-controlled type I error rate, with both operating characteristics defined from a Bayesian perspective. We also consider a generalization of the proposed parametric model that uses a nonparametric mixture of Dirichlet processes to model the frailty distributions and compare its performance to the proposed approach. We demonstrate the methodology by designing a colorectal cancer clinical trial with a goal of demonstrating that the IP causes a favorable effect on at least one of the two outcomes but no harm on either.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40605886","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

Designing three-level cluster randomized trials to assess treatment effect heterogeneity. 设计三级整群随机试验来评估治疗效果的异质性。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-10-18 DOI: 10.1093/biostatistics/kxac026

Fan Li, Xinyuan Chen, Zizhong Tian, Denise Esserman, Patrick J Heagerty, Rui Wang

{"title":"Designing three-level cluster randomized trials to assess treatment effect heterogeneity.","authors":"Fan Li, Xinyuan Chen, Zizhong Tian, Denise Esserman, Patrick J Heagerty, Rui Wang","doi":"10.1093/biostatistics/kxac026","DOIUrl":"10.1093/biostatistics/kxac026","url":null,"abstract":"Cluster randomized trials often exhibit a three-level structure with participants nested in subclusters such as health care providers, and subclusters nested in clusters such as clinics. While the average treatment effect has been the primary focus in planning three-level randomized trials, interest is growing in understanding whether the treatment effect varies among prespecified patient subpopulations, such as those defined by demographics or baseline clinical characteristics. In this article, we derive novel analytical design formulas based on the asymptotic covariance matrix for powering confirmatory analyses of treatment effect heterogeneity in three-level trials, that are broadly applicable to the evaluation of cluster-level, subcluster-level, and participant-level effect modifiers and to designs where randomization can be carried out at any level. We characterize a nested exchangeable correlation structure for both the effect modifier and the outcome conditional on the effect modifier, and generate new insights from a study design perspective for conducting analyses of treatment effect heterogeneity based on a linear mixed analysis of covariance model. A simulation study is conducted to validate our new methods and two real-world trial examples are used for illustrations.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40610488","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: 9

Constrained groupwise additive index models. 有约束的成组加性指数模型。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-10-18 DOI: 10.1093/biostatistics/kxac023

Pierre Masselot, Fateh Chebana, Céline Campagna, Éric Lavigne, Taha B M J Ouarda, Pierre Gosselin

{"title":"Constrained groupwise additive index models.","authors":"Pierre Masselot, Fateh Chebana, Céline Campagna, Éric Lavigne, Taha B M J Ouarda, Pierre Gosselin","doi":"10.1093/biostatistics/kxac023","DOIUrl":"10.1093/biostatistics/kxac023","url":null,"abstract":"In environmental epidemiology, there is wide interest in creating and using comprehensive indices that can summarize information from different environmental exposures while retaining strong predictive power on a target health outcome. In this context, the present article proposes a model called the constrained groupwise additive index model (CGAIM) to create easy-to-interpret indices predictive of a response variable, from a potentially large list of variables. The CGAIM considers groups of predictors that naturally belong together to yield meaningful indices. It also allows the addition of linear constraints on both the index weights and the form of their relationship with the response variable to represent prior assumptions or operational requirements. We propose an efficient algorithm to estimate the CGAIM, along with index selection and inference procedures. A simulation study shows that the proposed algorithm has good estimation performances, with low bias and variance and is applicable in complex situations with many correlated predictors. It also demonstrates important sensitivity and specificity in index selection, but non-negligible coverage error on constructed confidence intervals. The CGAIM is then illustrated in the construction of heat indices in a health warning system context. We believe the CGAIM could become useful in a wide variety of situations, such as warning systems establishment, and multipollutant or exposome studies.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40474660","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

Automated splitting into batches for observational biomedical studies with sequential processing. 通过顺序处理将观察生物医学研究自动拆分为批次。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-10-18 DOI: 10.1093/biostatistics/kxac014

Bram Burger, Marc Vaudel, Harald Barsnes

引用次数: 0

Reassessing pharmacogenomic cell sensitivity with multilevel statistical models. 用多水平统计模型重新评估药物基因组细胞的敏感性。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-10-18 DOI: 10.1093/biostatistics/kxac010

Matt Ploenzke, Rafael Irizarry

{"title":"Reassessing pharmacogenomic cell sensitivity with multilevel statistical models.","authors":"Matt Ploenzke, Rafael Irizarry","doi":"10.1093/biostatistics/kxac010","DOIUrl":"10.1093/biostatistics/kxac010","url":null,"abstract":"Pharmacogenomic experiments allow for the systematic testing of drugs, at varying dosage concentrations, to study how genomic markers correlate with cell sensitivity to treatment. The first step in the analysis is to quantify the response of cell lines to variable dosage concentrations of the drugs being tested. The signal to noise in these measurements can be low due to biological and experimental variability. However, the increasing availability of pharmacogenomic studies provides replicated data sets that can be leveraged to gain power. To do this, we formulate a hierarchical mixture model to estimate the drug-specific mixture distributions for estimating cell sensitivity and for assessing drug effect type as either broad or targeted effect. We use this formulation to propose a unified approach that can yield posterior probability of a cell being susceptible to a drug conditional on being a targeted effect or relative effect sizes conditioned on the cell being broad. We demonstrate the usefulness of our approach via case studies. First, we assess pairwise agreements for cell lines/drugs within the intersection of two data sets and confirm the moderate pairwise agreement between many publicly available pharmacogenomic data sets. We then present an analysis that identifies sensitivity to the drug crizotinib for cells harboring EML4-ALK or NPM1-ALK gene fusions, as well as significantly down-regulated cell-matrix pathways associated with crizotinib sensitivity.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583722/pdf/kxac010.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241149","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

Alleviating spatial confounding in frailty models. 缓解虚弱模型中的空间混淆。

IF 2.1 3区数学

Biostatistics Pub Date : 2023-10-18 DOI: 10.1093/biostatistics/kxac028

Douglas R M Azevedo, Marcos O Prates, Dipankar Bandyopadhyay

{"title":"Alleviating spatial confounding in frailty models.","authors":"Douglas R M Azevedo, Marcos O Prates, Dipankar Bandyopadhyay","doi":"10.1093/biostatistics/kxac028","DOIUrl":"10.1093/biostatistics/kxac028","url":null,"abstract":"The confounding between fixed effects and (spatial) random effects in a regression setup is termed spatial confounding. This topic continues to gain attention and has been studied extensively in recent years, given that failure to account for this may lead to a suboptimal inference. To mitigate this, a variety of projection-based approaches under the class of restricted spatial models are available in the context of generalized linear mixed models. However, these projection approaches cannot be directly extended to the spatial survival context via frailty models due to dimension incompatibility between the fixed and spatial random effects. In this work, we introduce a two-step approach to handle this, which involves (i) projecting the design matrix to the dimension of the spatial effect (via dimension reduction) and (ii) assuring that the random effect is orthogonal to this new design matrix (confounding alleviation). Under a fully Bayesian paradigm, we conduct fast estimation and inference using integrated nested Laplace approximation. Both simulation studies and application to a motivating data evaluating respiratory cancer survival in the US state of California reveal the advantages of our proposal in terms of model performance and confounding alleviation, compared to alternatives.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11004977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40615982","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