Biostatistics最新文献

{"title":"Causal functional mediation analysis with an application to functional magnetic resonance imaging data.","authors":"Yi Zhao, Xi Luo, Michael E Sobel, Martin A Lindquist, Brian S Caffo","doi":"10.1093/biostatistics/kxaf019","DOIUrl":"10.1093/biostatistics/kxaf019","url":null,"abstract":"<p><p>A primary goal of task-based functional magnetic resonance imaging (fMRI) studies is to quantify the effective connectivity between brain regions when stimuli are presented. Assessing the dynamics of effective connectivity has attracted increasing attention. Causal mediation analysis serves as a widely implemented tool aiming to delineate the mechanism between task stimuli and brain activations. However, the case, where the treatment, mediator, and outcome are continuous functions, has not been studied. Causal mediation analysis for functional data is considered. Semiparametric functional linear structural equation models are introduced and causal assumptions are discussed. The proposed models allow for the estimation of individual effect curves. The models are applied to a task-based fMRI study, providing a new perspective of studying dynamic brain connectivity. The R package cfma for implementation is available on CRAN.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531230","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}

{"title":"Model-based multifacet clustering with high-dimensional omics applications.","authors":"Wei Zong, Danyang Li, Marianne L Seney, Colleen A Mcclung, George C Tseng","doi":"10.1093/biostatistics/kxae020","DOIUrl":"10.1093/biostatistics/kxae020","url":null,"abstract":"<p><p>High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604511","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}

{"title":"Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting.","authors":"Zhichao Xu, Chunlin Li, Sunyi Chi, Tianzhong Yang, Peng Wei","doi":"10.1093/biostatistics/kxae037","DOIUrl":"10.1093/biostatistics/kxae037","url":null,"abstract":"<p><p>Mediation analysis is a useful tool in investigating how molecular phenotypes such as gene expression mediate the effect of exposure on health outcomes. However, commonly used mean-based total mediation effect measures may suffer from cancellation of component-wise mediation effects in opposite directions in the presence of high-dimensional omics mediators. To overcome this limitation, we recently proposed a variance-based R-squared total mediation effect measure that relies on the computationally intensive nonparametric bootstrap for confidence interval estimation. In the work described herein, we formulated a more efficient two-stage, cross-fitted estimation procedure for the R2 measure. To avoid potential bias, we performed iterative Sure Independence Screening (iSIS) in two subsamples to exclude the non-mediators, followed by ordinary least squares regressions for the variance estimation. We then constructed confidence intervals based on the newly derived closed-form asymptotic distribution of the R2 measure. Extensive simulation studies demonstrated that this proposed procedure is much more computationally efficient than the resampling-based method, with comparable coverage probability. Furthermore, when applied to the Framingham Heart Study, the proposed method replicated the established finding of gene expression mediating age-related variation in systolic blood pressure and identified the role of gene expression profiles in the relationship between sex and high-density lipoprotein cholesterol level. The proposed estimation procedure is implemented in R package CFR2M.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481495","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}

{"title":"The impact of coarsening an exposure on partial identifiability in instrumental variable settings.","authors":"Erin E Gabriel, Michael C Sachs, Arvid Sjölander","doi":"10.1093/biostatistics/kxae042","DOIUrl":"10.1093/biostatistics/kxae042","url":null,"abstract":"<p><p>In instrumental variable (IV) settings, such as imperfect randomized trials and observational studies with Mendelian randomization, one may encounter a continuous exposure, the causal effect of which is not of true interest. Instead, scientific interest may lie in a coarsened version of this exposure. Although there is a lengthy literature on the impact of coarsening of an exposure with several works focusing specifically on IV settings, all methods proposed in this literature require parametric assumptions. Instead, just as in the standard IV setting, one can consider partial identification via bounds making no parametric assumptions. This was first pointed out in Alexander Balke's PhD dissertation. We extend and clarify his work and derive novel bounds in several settings, including for a three-level IV, which will most likely be the case in Mendelian randomization. We demonstrate our findings in two real data examples, a randomized trial for peanut allergy in infants and a Mendelian randomization setting investigating the effect of homocysteine on cardiovascular disease.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632696","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}

{"title":"Fast standard error estimation for joint models of longitudinal and time-to-event data based on stochastic EM algorithms.","authors":"Tingting Yu, Lang Wu, Ronald J Bosch, Davey M Smith, Rui Wang","doi":"10.1093/biostatistics/kxae043","DOIUrl":"10.1093/biostatistics/kxae043","url":null,"abstract":"<p><p>Maximum likelihood inference can often become computationally intensive when performing joint modeling of longitudinal and time-to-event data, due to the intractable integrals in the joint likelihood function. The computational challenges escalate further when modeling HIV-1 viral load data, owing to the nonlinear trajectories and the presence of left-censored data resulting from the assay's lower limit of quantification. In this paper, for a joint model comprising a nonlinear mixed-effect model and a Cox Proportional Hazards model, we develop a computationally efficient Stochastic EM (StEM) algorithm for parameter estimation. Furthermore, we propose a novel technique for fast standard error estimation, which directly estimates standard errors from the results of StEM iterations and is broadly applicable to various joint modeling settings, such as those containing generalized linear mixed-effect models, parametric survival models, or joint models with more than two submodels. We evaluate the performance of the proposed methods through simulation studies and apply them to HIV-1 viral load data from six AIDS Clinical Trials Group studies to characterize viral rebound trajectories following the interruption of antiretroviral therapy (ART), accounting for the informative duration of off-ART periods.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142632694","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}

{"title":"Adaptive Gaussian Markov random fields for child mortality estimation.","authors":"Serge Aleshin-Guendel, Jon Wakefield","doi":"10.1093/biostatistics/kxae030","DOIUrl":"10.1093/biostatistics/kxae030","url":null,"abstract":"<p><p>The under-5 mortality rate (U5MR), a critical health indicator, is typically estimated from household surveys in lower and middle income countries. Spatio-temporal disaggregation of household survey data can lead to highly variable estimates of U5MR, necessitating the usage of smoothing models which borrow information across space and time. The assumptions of common smoothing models may be unrealistic when certain time periods or regions are expected to have shocks in mortality relative to their neighbors, which can lead to oversmoothing of U5MR estimates. In this paper, we develop a spatial and temporal smoothing approach based on Gaussian Markov random field models which incorporate knowledge of these expected shocks in mortality. We demonstrate the potential for these models to improve upon alternatives not incorporating knowledge of expected shocks in a simulation study. We apply these models to estimate U5MR in Rwanda at the national level from 1985 to 2019, a time period which includes the Rwandan civil war and genocide.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894969","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}

{"title":"Incorporating historic information to further improve power when conducting Bayesian information borrowing in basket trials.","authors":"Libby Daniells, Pavel Mozgunov, Helen Barnett, Alun Bedding, Thomas Jaki","doi":"10.1093/biostatistics/kxaf016","DOIUrl":"10.1093/biostatistics/kxaf016","url":null,"abstract":"<p><p>In basket trials a single therapeutic treatment is tested on several patient populations simultaneously, each of which forming a basket, where patients across all baskets on the trial share a common genetic aberration. These trials allow testing of treatments on small groups of patients, however, limited basket sample sizes can result in inadequate precision and power of estimates. It is well known that Bayesian information borrowing models such as the exchangeability-nonexchangeability (EXNEX) model can be implemented to tackle such a problem, drawing on information from one basket when making inference in another. An alternative approach to improve power of estimates, is to incorporate any historical or external information available. This paper considers models that amalgamate both forms of information borrowing, allowing borrowing between baskets in the ongoing trial whilst also drawing on response data from historical sources, with the aim to further improve treatment effect estimates. We propose several Bayesian information borrowing approaches that incorporate historical information into the model. These methods are data-driven, updating the degree of borrowing based on the level of homogeneity between information sources. A thorough simulation study is presented to draw comparisons between the proposed approaches, whilst also comparing to the standard EXNEX model in which no historical information is utilized. The models are also applied to a real-life trial example to demonstrate their performance in practice. We show that the incorporation of historic data under the novel approaches can lead to a substantial improvement in precision and power of treatment effect estimates when such data is homogeneous to the responses in the ongoing trial. Under some approaches, this came alongside an inflation in type I error rate in cases of heterogeneity. However, the use of a power prior in the EXNEX model is shown to increase power and precision, whilst maintaining similar error rates to the standard EXNEX model.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144327836","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}

{"title":"Predicting distributions of physical activity profiles in the National Health and Nutrition Examination Survey database using a partially linear Fréchet single index model.","authors":"Marcos Matabuena, Aritra Ghosal, Wendy Meiring, Alexander Petersen","doi":"10.1093/biostatistics/kxaf013","DOIUrl":"10.1093/biostatistics/kxaf013","url":null,"abstract":"<p><p>Object-oriented data analysis is a fascinating and evolving field in modern statistical science, with the potential to make significant contributions to biomedical applications. This statistical framework facilitates the development of new methods to analyze complex data objects that capture more information than traditional clinical biomarkers. This paper applies the object-oriented framework to analyze physical activity levels, measured by accelerometers, as response objects in a regression model. Unlike traditional summary metrics, we utilize a recently proposed representation of physical activity data as a distributional object, providing a more nuanced and complete profile of individual energy expenditure across all ranges of monitoring intensity. A novel hybrid Fréchet regression model is proposed and applied to US population accelerometer data from National Health and Nutrition Examination Survey (NHANES) 2011 to 2014. The semi-parametric nature of the model allows for the inclusion of nonlinear effects for critical variables, such as age, which are biologically known to have subtle impacts on physical activity. Simultaneously, the inclusion of linear effects preserves interpretability for other variables, particularly categorical covariates such as ethnicity and sex. The results obtained are valuable from a public health perspective and could lead to new strategies for optimizing physical activity interventions in specific American subpopulations.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129647","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}

{"title":"Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity.","authors":"Fan Bu, Allison E Aiello, Alexander Volfovsky, Jason Xu","doi":"10.1093/biostatistics/kxae018","DOIUrl":"10.1093/biostatistics/kxae018","url":null,"abstract":"<p><p>We develop a stochastic epidemic model progressing over dynamic networks, where infection rates are heterogeneous and may vary with individual-level covariates. The joint dynamics are modeled as a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is in turn influenced by individual disease statuses. To accommodate partial epidemic observations commonly seen in real-world data, we propose a stochastic EM algorithm for inference, introducing key innovations that include efficient conditional samplers for imputing missing infection and recovery times which respect the dynamic contact network. Experiments on both synthetic and real datasets demonstrate that our inference method can accurately and efficiently recover model parameters and provide valuable insight at the presence of unobserved disease episodes in epidemic data.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903694","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}

{"title":"The winner's curse under dependence: repairing empirical Bayes using convoluted densities.","authors":"Stijn Hawinkel, Olivier Thas, Steven Maere","doi":"10.1093/biostatistics/kxaf025","DOIUrl":"https://doi.org/10.1093/biostatistics/kxaf025","url":null,"abstract":"<p><p>The winner's curse is a form of selection bias that arises when estimates are obtained for a large number of features, but only a subset of most extreme estimates is reported. It occurs in large scale significance testing as well as in rank-based selection, and imperils reproducibility of findings and follow-up study design. Several methods correcting for this selection bias have been proposed, but questions remain on their susceptibility to dependence between features since theoretical analyses and comparative studies are few. We prove that estimation through Tweedie's formula is biased in presence of strong dependence, and propose a convolution of its density estimator to restore its competitive performance, which also aids other empirical Bayes methods. Furthermore, we perform a comprehensive simulation study comparing different classes of winner's curse correction methods for point estimates as well as confidence intervals under dependence. We find a bootstrap method and empirical Bayes methods with density convolution to perform best at correcting the selection bias, although this correction generally does not improve the feature ranking. Finally, we apply the methods to a comparison of single-feature versus multi-feature prediction models in predicting Brassica napus phenotypes from gene expression data, demonstrating that the superiority of the best single-feature model may be illusory.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979577","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}