Biostatistics最新文献_第10页

DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies. DeLIVR：在全转录组关联研究中测试非线性因果效应的深度学习 IV 回归方法。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxac051

Ruoyu He, Mingyang Liu, Zhaotong Lin, Zhong Zhuang, Xiaotong Shen, Wei Pan

{"title":"DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.","authors":"Ruoyu He, Mingyang Liu, Zhaotong Lin, Zhong Zhuang, Xiaotong Shen, Wei Pan","doi":"10.1093/biostatistics/kxac051","DOIUrl":"10.1093/biostatistics/kxac051","url":null,"abstract":"Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"468-485"},"PeriodicalIF":2.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11017120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10861888","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

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":" ","pages":"559-576"},"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":" ","pages":"429-448"},"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":"98 1","pages":""},"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

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":"28 1","pages":""},"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":" ","pages":"117-133"},"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

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 1.8 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: 0

Differences in set-based tests for sparse alternatives when testing sets of outcomes compared to sets of explanatory factors in genetic association studies. 在遗传关联研究中，基于集合的稀疏替代品测试在测试结果集合与解释因素集合时的差异。

IF 1.8 3区数学

Biostatistics Pub Date : 2023-12-15 DOI: 10.1093/biostatistics/kxac036

Ryan Sun, Andy Shi, Xihong Lin

{"title":"Differences in set-based tests for sparse alternatives when testing sets of outcomes compared to sets of explanatory factors in genetic association studies.","authors":"Ryan Sun, Andy Shi, Xihong Lin","doi":"10.1093/biostatistics/kxac036","DOIUrl":"10.1093/biostatistics/kxac036","url":null,"abstract":"Set-based association tests are widely popular in genetic association settings for their ability to aggregate weak signals and reduce multiple testing burdens. In particular, a class of set-based tests including the Higher Criticism, Berk-Jones, and other statistics have recently been popularized for reaching a so-called detection boundary when signals are rare and weak. Such tests have been applied in two subtly different settings: (a) associating a genetic variant set with a single phenotype and (b) associating a single genetic variant with a phenotype set. A significant issue in practice is the choice of test, especially when deciding between innovated and generalized type methods for detection boundary tests. Conflicting guidance is present in the literature. This work describes how correlation structures generate marked differences in relative operating characteristics for settings (a) and (b). The implications for study design are significant. We also develop novel power bounds that facilitate the aforementioned calculations and allow for analysis of individual testing settings. In more concrete terms, our investigation is motivated by translational expression quantitative trait loci (eQTL) studies in lung cancer. These studies involve both testing for groups of variants associated with a single gene expression (multiple explanatory factors) and testing whether a single variant is associated with a group of gene expressions (multiple outcomes). Results are supported by a collection of simulation studies and illustrated through lung cancer eQTL examples.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"171-187"},"PeriodicalIF":1.8,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10724113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9632716","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":" ","pages":"203-219"},"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