Biostatistics最新文献_第7页

Differential transcript usage analysis incorporating quantification uncertainty via compositional measurement error regression modeling. 通过成分测量误差回归建模纳入量化不确定性的差异转录本使用分析。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad008

Amber M Young, Scott Van Buren, Naim U Rashid

{"title":"Differential transcript usage analysis incorporating quantification uncertainty via compositional measurement error regression modeling.","authors":"Amber M Young, Scott Van Buren, Naim U Rashid","doi":"10.1093/biostatistics/kxad008","DOIUrl":"10.1093/biostatistics/kxad008","url":null,"abstract":"Differential transcript usage (DTU) occurs when the relative expression of multiple transcripts arising from the same gene changes between different conditions. Existing approaches to detect DTU often rely on computational procedures that can have speed and scalability issues as the number of samples increases. Here we propose a new method, CompDTU, that uses compositional regression to model the relative abundance proportions of each transcript that are of interest in DTU analyses. This procedure leverages fast matrix-based computations that make it ideally suited for DTU analysis with larger sample sizes. This method also allows for the testing of and adjustment for multiple categorical or continuous covariates. Additionally, many existing approaches for DTU ignore quantification uncertainty in the expression estimates for each transcript in RNA-seq data. We extend our CompDTU method to incorporate quantification uncertainty leveraging common output from RNA-seq expression quantification tool in a novel method CompDTUme. Through several power analyses, we show that CompDTU has excellent sensitivity and reduces false positive results relative to existing methods. Additionally, CompDTUme results in further improvements in performance over CompDTU with sufficient sample size for genes with high levels of quantification uncertainty, while also maintaining favorable speed and scalability. We motivate our methods using data from the Cancer Genome Atlas Breast Invasive Carcinoma data set, specifically using RNA-seq data from primary tumors for 740 patients with breast cancer. We show greatly reduced computation time from our new methods as well as the ability to detect several novel genes with significant DTU across different breast cancer subtypes.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11017126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9683536","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

Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations. 使用集成嵌套拉普拉斯近似值快速灵活地推断多变量纵向和生存数据的联合模型

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad019

Denis Rustand, Janet van Niekerk, Elias Teixeira Krainski, Håvard Rue, Cécile Proust-Lima

{"title":"Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.","authors":"Denis Rustand, Janet van Niekerk, Elias Teixeira Krainski, Håvard Rue, Cécile Proust-Lima","doi":"10.1093/biostatistics/kxad019","DOIUrl":"10.1093/biostatistics/kxad019","url":null,"abstract":"Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11017128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10301894","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

Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders 实用因果中介分析：扩展非参数估算器，以适应多个中介因素和多个中间混杂因素

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-05 DOI: 10.1093/biostatistics/kxae012

Kara E Rudolph, Nicholas T Williams, Ivan Diaz

{"title":"Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders","authors":"Kara E Rudolph, Nicholas T Williams, Ivan Diaz","doi":"10.1093/biostatistics/kxae012","DOIUrl":"https://doi.org/10.1093/biostatistics/kxae012","url":null,"abstract":"Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including (i) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be (ii) multivariate, and (iii) the existence of multivariate mediators. All three challenges are present in the mediation analysis we consider here, where our goal is to estimate the indirect effects of receiving a Section 8 housing voucher as a young child on the risk of developing a psychiatric mood disorder in adolescence that operate through mediators related to neighborhood poverty, the school environment, and instability of the neighborhood and school environments, considered together and separately. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator–outcome relationship, but currently, no readily implementable nonparametric estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders. The absence of such an IDE/IIE estimator that can easily accommodate both multivariate mediators and post-exposure confounders represents a significant limitation for real-world analyses, because when considering each mediator subgroup separately, the remaining mediator subgroups (or a subset of them) become post-exposure intermediate confounders. We address this gap by extending a recently developed nonparametric estimator for the IDE/IIE to allow for easy incorporation of multivariate mediators and multivariate post-exposure confounders simultaneously. We apply the proposed estimation approach to our analysis, including walking through a strategy to account for other, possibly co-occurring intermediate variables when considering each mediator subgroup separately.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140579180","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

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 : 2024-04-05 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":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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 : 2024-03-17 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":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Functional support vector machine. 功能支持向量机

IF 1.8 3区数学

Biostatistics Pub Date : 2024-03-13 DOI: 10.1093/biostatistics/kxae007

Shanghong Xie, R Todd Ogden

{"title":"Functional support vector machine.","authors":"Shanghong Xie, R Todd Ogden","doi":"10.1093/biostatistics/kxae007","DOIUrl":"10.1093/biostatistics/kxae007","url":null,"abstract":"Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g. continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the functional predictors is complex. On the other hand, support vector machines (SVMs) are among the most robust prediction models but do not take account of the high correlations between repeated measurements and cannot be used for irregular data. In this work, we propose a novel method to integrate functional principal component analysis with SVM techniques for classification and regression to account for the continuous nature of functional data and the nonlinear relationship between the scalar response variable and the functional predictors. We demonstrate the performance of our method through extensive simulation experiments and two real data applications: the classification of alcoholics using electroencephalography signals and the prediction of glucobrassicin concentration using near-infrared reflectance spectroscopy. Our methods especially have more advantages when the measurement errors in functional predictors are relatively large.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140112299","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

Mendelian randomization analysis using multiple biomarkers of an underlying common exposure. 利用潜在共同暴露的多种生物标志物进行孟德尔随机分析。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-03-08 DOI: 10.1093/biostatistics/kxae006

Jin Jin, Guanghao Qi, Zhi Yu, Nilanjan Chatterjee

{"title":"Mendelian randomization analysis using multiple biomarkers of an underlying common exposure.","authors":"Jin Jin, Guanghao Qi, Zhi Yu, Nilanjan Chatterjee","doi":"10.1093/biostatistics/kxae006","DOIUrl":"10.1093/biostatistics/kxae006","url":null,"abstract":"Mendelian randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers or other types of traits that are co-regulated by the exposure. We propose a method for MR analysis on latent exposures (MRLE), which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of GWAS summary association statistics for the observable traits, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies show that MRLE has well-controlled type I error rates and enhanced power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-α, and MCP-1) provide evidence for potential causal effects of inflammation on increasing the risk of coronary artery disease, colorectal cancer, and rheumatoid arthritis, while standard MR analysis for individual biomarkers fails to detect consistent evidence for such effects.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140066298","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

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 : 2024-02-29 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":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Projection-based two-sample inference for sparsely observed multivariate functional data. 基于投影的稀疏观测多变量函数数据的双样本推断。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-02-27 DOI: 10.1093/biostatistics/kxae004

Salil Koner, Sheng Luo

{"title":"Projection-based two-sample inference for sparsely observed multivariate functional data.","authors":"Salil Koner, Sheng Luo","doi":"10.1093/biostatistics/kxae004","DOIUrl":"10.1093/biostatistics/kxae004","url":null,"abstract":"Modern longitudinal studies collect multiple outcomes as the primary endpoints to understand the complex dynamics of the diseases. Oftentimes, especially in clinical trials, the joint variation among the multidimensional responses plays a significant role in assessing the differential characteristics between two or more groups, rather than drawing inferences based on a single outcome. We develop a projection-based two-sample significance test to identify the population-level difference between the multivariate profiles observed under a sparse longitudinal design. The methodology is built upon widely adopted multivariate functional principal component analysis to reduce the dimension of the infinite-dimensional multi-modal functions while preserving the dynamic correlation between the components. The test applies to a wide class of (non-stationary) covariance structures of the response, and it detects a significant group difference based on a single p-value, thereby overcoming the issue of adjusting for multiple p-values that arise due to comparing the means in each of components separately. Finite-sample numerical studies demonstrate that the test maintains the type-I error, and is powerful to detect significant group differences, compared to the state-of-the-art testing procedures. The test is carried out on two significant longitudinal studies for Alzheimer's disease and Parkinson's disease (PD) patients, namely, TOMMORROW study of individuals at high risk of mild cognitive impairment to detect differences in the cognitive test scores between the pioglitazone and the placebo groups, and Azillect study to assess the efficacy of rasagiline as a potential treatment to slow down the progression of PD.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139984624","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

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 : 2024-02-23 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":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0