Biostatistics最新文献_第6页

Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy. 树状信息贝叶斯多源领域适应：利用口头尸检进行跨人群死因概率分配。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae005

Zhenke Wu, Zehang R Li, Irena Chen, Mengbing Li

{"title":"Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.","authors":"Zhenke Wu, Zehang R Li, Irena Chen, Mengbing Li","doi":"10.1093/biostatistics/kxae005","DOIUrl":"10.1093/biostatistics/kxae005","url":null,"abstract":"Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or \"domains\") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"1233-1253"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139944717","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

Neuroimaging meta regression for coordinate based meta analysis data with a spatial model. 利用空间模型对基于坐标的元分析数据进行神经成像元回归。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae024

Yifan Yu, Rosario Pintos Lobo, Michael Cody Riedel, Katherine Bottenhorn, Angela R Laird, Thomas E Nichols

{"title":"Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.","authors":"Yifan Yu, Rosario Pintos Lobo, Michael Cody Riedel, Katherine Bottenhorn, Angela R Laird, Thomas E Nichols","doi":"10.1093/biostatistics/kxae024","DOIUrl":"10.1093/biostatistics/kxae024","url":null,"abstract":"Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. Keywords: generalized linear models; meta-analysis; spatial statistics; statistical modeling.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"1210-1232"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604512","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

Dynamic models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level. 分层数据增强的动态模型：估算国家以下一级艾滋病毒流行情况的应用。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae003

Bao Le, Xiaoyue Niu, Tim Brown, Jeffrey W Imai-Eaton

{"title":"Dynamic models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level.","authors":"Bao Le, Xiaoyue Niu, Tim Brown, Jeffrey W Imai-Eaton","doi":"10.1093/biostatistics/kxae003","DOIUrl":"10.1093/biostatistics/kxae003","url":null,"abstract":"Dynamic models have been successfully used in producing estimates of HIV epidemics at the national level due to their epidemiological nature and their ability to estimate prevalence, incidence, and mortality rates simultaneously. Recently, HIV interventions and policies have required more information at sub-national levels to support local planning, decision-making and resource allocation. Unfortunately, many areas lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time-consuming. In this article, we propose a simple and innovative way to incorporate hierarchical information into the dynamical systems by using auxiliary data. The proposed method efficiently uses information from multiple areas within each country without increasing the computational burden. As a result, the new model improves predictive ability and uncertainty assessment.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"1049-1061"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139998375","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 mixed model inference for genetic association under related samples with brain network phenotype. 贝叶斯混合模型推断脑网络表型相关样本下的遗传关联。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae008

Xinyuan Tian, Yiting Wang, Selena Wang, Yi Zhao, Yize Zhao

{"title":"Bayesian mixed model inference for genetic association under related samples with brain network phenotype.","authors":"Xinyuan Tian, Yiting Wang, Selena Wang, Yi Zhao, Yize Zhao","doi":"10.1093/biostatistics/kxae008","DOIUrl":"10.1093/biostatistics/kxae008","url":null,"abstract":"Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"1195-1209"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140144658","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

Identifying predictors of resilience to stressors in single-arm studies of pre-post change. 在前后变化的单臂研究中确定对压力的恢复能力的预测因素。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxad018

Ravi Varadhan, Jiafeng Zhu, Karen Bandeen-Roche

{"title":"Identifying predictors of resilience to stressors in single-arm studies of pre-post change.","authors":"Ravi Varadhan, Jiafeng Zhu, Karen Bandeen-Roche","doi":"10.1093/biostatistics/kxad018","DOIUrl":"10.1093/biostatistics/kxad018","url":null,"abstract":"Many older adults experience a major stressor at some point in their lives. The ability to recover well after a major stressor is known as resilience. An important goal of geriatric research is to identify factors that influence resilience to stressors. Studies of resilience in older adults are typically conducted with a single-arm where everyone experiences the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to mathematical coupling and regression to the mean (RTM). We develop a method to correct the bias. We extend the method to include covariates. Our approach considers a counterfactual control group and involves sensitivity analyses to evaluate different settings of control group parameters. Only minimal distributional assumptions are required. Simulation studies demonstrate the validity of the method. We illustrate the method using a large, registry of older adults (N =7239) who underwent total knee replacement (TKR). We demonstrate how external data can be utilized to constrain the sensitivity analysis. Naive analyses implicated several treatment effect modifiers including baseline function, age, body-mass index (BMI), gender, number of comorbidities, income, and race. Corrected analysis revealed that baseline (pre-stressor) function was not strongly linked to recovery after TKR and among the covariates, only age and number of comorbidities were consistently and negatively associated with post-stressor recovery in all functional domains. Correction of mathematical coupling and RTM is necessary for drawing valid inferences regarding the effect of covariates and baseline status on pre-post change. Our method provides a simple estimator to this end.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"1094-1111"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10297247","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

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 : 2024-10-01 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":" ","pages":"1140-1155"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471962/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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 : 2024-10-01 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":" ","pages":"1122-1139"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471965/pdf/","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":"OA","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 : 2024-10-01 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":" ","pages":"933-946"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471959/pdf/","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":"OA","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 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 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":"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":" ","pages":"1112-1121"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639176/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of complier and noncomplier average causal effects in the presence of latent missing-at-random (LMAR) outcomes: a unifying view and choices of assumptions. 在存在潜在随机遗漏（LMAR）结果的情况下，识别合意者和非合意者的平均因果效应：统一观点和假设选择。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-10-01 DOI: 10.1093/biostatistics/kxae011

Trang Quynh Nguyen, Michelle C Carlson, Elizabeth A Stuart

{"title":"Identification of complier and noncomplier average causal effects in the presence of latent missing-at-random (LMAR) outcomes: a unifying view and choices of assumptions.","authors":"Trang Quynh Nguyen, Michelle C Carlson, Elizabeth A Stuart","doi":"10.1093/biostatistics/kxae011","DOIUrl":"10.1093/biostatistics/kxae011","url":null,"abstract":"The study of treatment effects is often complicated by noncompliance and missing data. In the one-sided noncompliance setting where of interest are the complier and noncomplier average causal effects, we address outcome missingness of the latent missing at random type (LMAR, also known as latent ignorability). That is, conditional on covariates and treatment assigned, the missingness may depend on compliance type. Within the instrumental variable (IV) approach to noncompliance, methods have been proposed for handling LMAR outcome that additionally invoke an exclusion restriction-type assumption on missingness, but no solution has been proposed for when a non-IV approach is used. This article focuses on effect identification in the presence of LMAR outcomes, with a view to flexibly accommodate different principal identification approaches. We show that under treatment assignment ignorability and LMAR only, effect nonidentifiability boils down to a set of two connected mixture equations involving unidentified stratum-specific response probabilities and outcome means. This clarifies that (except for a special case) effect identification generally requires two additional assumptions: a specific missingness mechanism assumption and a principal identification assumption. This provides a template for identifying effects based on separate choices of these assumptions. We consider a range of specific missingness assumptions, including those that have appeared in the literature and some new ones. Incidentally, we find an issue in the existing assumptions, and propose a modification of the assumptions to avoid the issue. Results under different assumptions are illustrated using data from the Baltimore Experience Corps Trial.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"978-996"},"PeriodicalIF":1.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11471963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140861668","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