Biostatistics最新文献_第10页

A transformation perspective on marginal and conditional models. 边际模型和条件模型的转换视角。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxac048

Luisa Barbanti, Torsten Hothorn

{"title":"A transformation perspective on marginal and conditional models.","authors":"Luisa Barbanti, Torsten Hothorn","doi":"10.1093/biostatistics/kxac048","DOIUrl":"10.1093/biostatistics/kxac048","url":null,"abstract":"Clustered observations are ubiquitous in controlled and observational studies and arise naturally in multicenter trials or longitudinal surveys. We present a novel model for the analysis of clustered observations where the marginal distributions are described by a linear transformation model and the correlations by a joint multivariate normal distribution. The joint model provides an analytic formula for the marginal distribution. Owing to the richness of transformation models, the techniques are applicable to any type of response variable, including bounded, skewed, binary, ordinal, or survival responses. We demonstrate how the common normal assumption for reaction times can be relaxed in the sleep deprivation benchmark data set and report marginal odds ratios for the notoriously difficult toe nail data. We furthermore discuss the analysis of two clinical trials aiming at the estimation of marginal treatment effects. In the first trial, pain was repeatedly assessed on a bounded visual analog scale and marginal proportional-odds models are presented. The second trial reported disease-free survival in rectal cancer patients, where the marginal hazard ratio from Weibull and Cox models is of special interest. An empirical evaluation compares the performance of the novel approach to general estimation equations for binary responses and to conditional mixed-effects models for continuous responses. An implementation is available in the tram add-on package to the R system and was benchmarked against established models in the literature.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"402-428"},"PeriodicalIF":1.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11212492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10297317","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

Characterizing quantile-varying covariate effects under the accelerated failure time model. 加速失效时间模型下的量变协变量效应特征。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxac052

Harrison T Reeder, Kyu Ha Lee, Sebastien Haneuse

{"title":"Characterizing quantile-varying covariate effects under the accelerated failure time model.","authors":"Harrison T Reeder, Kyu Ha Lee, Sebastien Haneuse","doi":"10.1093/biostatistics/kxac052","DOIUrl":"10.1093/biostatistics/kxac052","url":null,"abstract":"An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failure time (AFT) model structures each covariate effect as a constant multiplicative shift in the outcome distribution across all survival quantiles. Though parsimonious, this structure cannot detect or capture effects that differ across quantiles of the distribution, a limitation that is analogous to only permitting proportional hazards in the Cox model. To address this, we propose a general framework for quantile-varying multiplicative effects under the AFT model. Specifically, we embed flexible regression structures within the AFT model and derive a novel formula for interpretable effects on the quantile scale. A regression standardization scheme based on the g-formula is proposed to enable the estimation of both covariate-conditional and marginal effects for an exposure of interest. We implement a user-friendly Bayesian approach for the estimation and quantification of uncertainty while accounting for left truncation and complex censoring. We emphasize the intuitive interpretation of this model through numerical and graphical tools and illustrate its performance through simulation and application to a study of Alzheimer's disease and dementia.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"449-467"},"PeriodicalIF":1.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10513263","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

Multiple imputation of more than one environmental exposure with nondifferential measurement error. 具有非微分测量误差的一次以上环境暴露的多重插补。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad011

Yuanzhi Yu, Roderick J Little, Matthew Perzanowski, Qixuan Chen

{"title":"Multiple imputation of more than one environmental exposure with nondifferential measurement error.","authors":"Yuanzhi Yu, Roderick J Little, Matthew Perzanowski, Qixuan Chen","doi":"10.1093/biostatistics/kxad011","DOIUrl":"10.1093/biostatistics/kxad011","url":null,"abstract":"Measurement error is common in environmental epidemiologic studies, but methods for correcting measurement error in regression models with multiple environmental exposures as covariates have not been well investigated. We consider a multiple imputation approach, combining external or internal calibration samples that contain information on both true and error-prone exposures with the main study data of multiple exposures measured with error. We propose a constrained chained equations multiple imputation (CEMI) algorithm that places constraints on the imputation model parameters in the chained equations imputation based on the assumptions of strong nondifferential measurement error. We also extend the constrained CEMI method to accommodate nondetects in the error-prone exposures in the main study data. We estimate the variance of the regression coefficients using the bootstrap with two imputations of each bootstrapped sample. The constrained CEMI method is shown by simulations to outperform existing methods, namely the method that ignores measurement error, classical calibration, and regression prediction, yielding estimated regression coefficients with smaller bias and confidence intervals with coverage close to the nominal level. We apply the proposed method to the Neighborhood Asthma and Allergy Study to investigate the associations between the concentrations of multiple indoor allergens and the fractional exhaled nitric oxide level among asthmatic children in New York City. The constrained CEMI method can be implemented by imposing constraints on the imputation matrix using the mice and bootImpute packages in R.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"306-322"},"PeriodicalIF":1.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11017114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9522828","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

Cohort-based smoothing methods for age-specific contact rates. 基于队列的年龄接触率平滑方法。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad005

Yannick Vandendijck, Oswaldo Gressani, Christel Faes, Carlo G Camarda, Niel Hens

{"title":"Cohort-based smoothing methods for age-specific contact rates.","authors":"Yannick Vandendijck, Oswaldo Gressani, Christel Faes, Carlo G Camarda, Niel Hens","doi":"10.1093/biostatistics/kxad005","DOIUrl":"10.1093/biostatistics/kxad005","url":null,"abstract":"The use of social contact rates is widespread in infectious disease modeling since it has been shown that they are key driving forces of important epidemiological parameters. Quantification of contact patterns is crucial to parameterize dynamic transmission models and to provide insights on the (basic) reproduction number. Information on social interactions can be obtained from population-based contact surveys, such as the European Commission project POLYMOD. Estimation of age-specific contact rates from these studies is often done using a piecewise constant approach or bivariate smoothing techniques. For the latter, typically, smoothness is introduced in the dimensions of the respondent's and contact's age (i.e., the rows and columns of the social contact matrix). We propose a smoothing constrained approach-taking into account the reciprocal nature of contacts-introducing smoothness over the diagonal (including all subdiagonals) of the social contact matrix. This modeling approach is justified assuming that when people age their contact behavior changes smoothly. We call this smoothing from a cohort perspective. Two approaches that allow for smoothing over social contact matrix diagonals are proposed, namely (i) reordering of the diagonal components of the contact matrix and (ii) reordering of the penalty matrix ensuring smoothness over the contact matrix diagonals. Parameter estimation is done in the likelihood framework by using constrained penalized iterative reweighted least squares. A simulation study underlines the benefits of cohort-based smoothing. Finally, the proposed methods are illustrated on the Belgian POLYMOD data of 2006. Code to reproduce the results of the article can be downloaded on this GitHub repository https://github.com/oswaldogressani/Cohort_smoothing.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"521-540"},"PeriodicalIF":2.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9141117","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

Systematically missing data in causally interpretable meta-analysis. 可解释因果关系的荟萃分析中的系统缺失数据。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad006

Jon A Steingrimsson, David H Barker, Ruofan Bie, Issa J Dahabreh

{"title":"Systematically missing data in causally interpretable meta-analysis.","authors":"Jon A Steingrimsson, David H Barker, Ruofan Bie, Issa J Dahabreh","doi":"10.1093/biostatistics/kxad006","DOIUrl":"10.1093/biostatistics/kxad006","url":null,"abstract":"Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experimentation may not be possible but from which covariate information can be obtained. In such analyses, a key practical challenge is the presence of systematically missing data when some trials have collected data on one or more baseline covariates, but other trials have not, such that the covariate information is missing for all participants in the latter. In this article, we provide identification results for potential (counterfactual) outcome means and average treatment effects in the target population when covariate data are systematically missing from some of the trials in the meta-analysis. We propose three estimators for the average treatment effect in the target population, examine their asymptotic properties, and show that they have good finite-sample performance in simulation studies. We use the estimators to analyze data from two large lung cancer screening trials and target population data from the National Health and Nutrition Examination Survey (NHANES). To accommodate the complex survey design of the NHANES, we modify the methods to incorporate survey sampling weights and allow for clustering.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"289-305"},"PeriodicalIF":2.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11017122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9567977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian approach to estimating COVID-19 incidence and infection fatality rates. 用贝叶斯方法估算 COVID-19 发病率和感染死亡率。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-04-15 DOI: 10.1093/biostatistics/kxad003

Justin J Slater, Aiyush Bansal, Harlan Campbell, Jeffrey S Rosenthal, Paul Gustafson, Patrick E Brown

{"title":"A Bayesian approach to estimating COVID-19 incidence and infection fatality rates.","authors":"Justin J Slater, Aiyush Bansal, Harlan Campbell, Jeffrey S Rosenthal, Paul Gustafson, Patrick E Brown","doi":"10.1093/biostatistics/kxad003","DOIUrl":"10.1093/biostatistics/kxad003","url":null,"abstract":"Naive estimates of incidence and infection fatality rates (IFR) of coronavirus disease 2019 suffer from a variety of biases, many of which relate to preferential testing. This has motivated epidemiologists from around the globe to conduct serosurveys that measure the immunity of individuals by testing for the presence of SARS-CoV-2 antibodies in the blood. These quantitative measures (titer values) are then used as a proxy for previous or current infection. However, statistical methods that use this data to its full potential have yet to be developed. Previous researchers have discretized these continuous values, discarding potentially useful information. In this article, we demonstrate how multivariate mixture models can be used in combination with post-stratification to estimate cumulative incidence and IFR in an approximate Bayesian framework without discretization. In doing so, we account for uncertainty from both the estimated number of infections and incomplete deaths data to provide estimates of IFR. This method is demonstrated using data from the Action to Beat Coronavirus erosurvey in Canada.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"354-384"},"PeriodicalIF":2.1,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11017123/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10850020","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

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