Biostatistics最新文献

{"title":"Bipartite interference and air pollution transport: estimating health effects of power plant interventions.","authors":"Corwin Zigler, Vera Liu, Fabrizia Mealli, Laura Forastiere","doi":"10.1093/biostatistics/kxae051","DOIUrl":"10.1093/biostatistics/kxae051","url":null,"abstract":"<p><p>Evaluating air quality interventions is confronted with the challenge of interference since interventions at a particular pollution source likely impact air quality and health at distant locations, and air quality and health at any given location are likely impacted by interventions at many sources. The structure of interference in this context is dictated by complex atmospheric processes governing how pollution emitted from a particular source is transformed and transported across space and can be cast with a bipartite structure reflecting the two distinct types of units: (i) interventional units on which treatments are applied or withheld to change pollution emissions; and (ii) outcome units on which outcomes of primary interest are measured. We propose new estimands for bipartite causal inference with interference that construe two components of treatment: a \"key-associated\" (or \"individual\") treatment and an \"upwind\" (or \"neighborhood\") treatment. Estimation is carried out using a covariate adjustment approach based on a joint propensity score. A reduced-complexity atmospheric model characterizes the structure of the interference network by modeling the movement of air parcels through time and space. The new methods are deployed to evaluate the effectiveness of installing flue-gas desulfurization scrubbers on 472 coal-burning power plants (the interventional units) in reducing Medicare hospitalizations among 21,577,552 Medicare beneficiaries residing across 25,553 ZIP codes in the United States (the outcome units).</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048850","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":"Unveiling Schizophrenia: a study with generalized functional linear mixed model via the investigation of functional random effects.","authors":"Rongxiang Rui, Wei Xiong, Jianxin Pan, Maozai Tian","doi":"10.1093/biostatistics/kxae049","DOIUrl":"10.1093/biostatistics/kxae049","url":null,"abstract":"<p><p>Previous studies have identified attenuated pre-speech activity and speech sound suppression in individuals with Schizophrenia, with similar patterns observed in basic tasks entailing button-pressing to perceive a tone. However, it remains unclear whether these patterns are uniform across individuals or vary from person to person. Motivated by electroencephalographic (EEG) data from a Schizophrenia study, we develop a generalized functional linear mixed model (GFLMM) for repeated measurements by incorporating subject-specific functional random effects associated with multiple functional predictors. To assess the significance of these functional effects, we employ two different multivariate functional principal component analysis methods, which transform the GFLMM into a conventional generalized linear mixed model, thereby facilitating its implementation with standard software. Furthermore, we introduce a cutting-edge testing approach utilizing working responses to detect both subject-specific and predictor-specific functional random effects. Monte Carlo simulation studies demonstrate the effectiveness of our proposed testing method. Application of the proposed methods to the Schizophrenia data reveals significant subject-specific effects of human brain activity in the frontal zone (Fz) and the central zone (Cz), providing valuable insights into the potential variations among individuals, from healthy controls to those diagnosed with Schizophrenia.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933522","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 prior information in gene expression network-based cancer heterogeneity analysis.","authors":"Rong Li, Shaodong Xu, Yang Li, Zuojian Tang, Di Feng, James Cai, Shuangge Ma","doi":"10.1093/biostatistics/kxae028","DOIUrl":"10.1093/biostatistics/kxae028","url":null,"abstract":"<p><p>Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as \"direct\" and \"indirect,\" where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12550826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141794124","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":"Estimation and inference for causal spillover effects in egocentric-network randomized trials in the presence of network membership misclassification.","authors":"Ariel Chao, Donna Spiegelman, Ashley Buchanan, Laura Forastiere","doi":"10.1093/biostatistics/kxaf009","DOIUrl":"10.1093/biostatistics/kxaf009","url":null,"abstract":"<p><p>To leverage peer influence and increase population behavioral changes, behavioral interventions often rely on peer-based strategies. A common study design that assesses such strategies is the egocentric-network randomized trial (ENRT), where index participants receive a behavioral training and are encouraged to disseminate information to their peers. Under this design, a crucial estimand of interest is the Average Spillover Effect (ASpE), which measures the impact of the intervention on participants who do not receive it, but whose outcomes may be affected by others who do. The assessment of the ASpE relies on assumptions about, and correct measurement of, interference sets within which individuals may influence one another's outcomes. It can be challenging to properly specify interference sets, such as networks in ENRTs, and when mismeasured, intervention effects estimated by existing methods will be biased. In studies where social networks play an important role in disease transmission or behavior change, correcting ASpE estimates for bias due to network misclassification is critical for accurately evaluating the full impact of interventions. We combined measurement error and causal inference methods to bias-correct the ASpE estimate for network misclassification in ENRTs, when surrogate networks are recorded in place of true ones, and validation data that relate the misclassified to the true networks are available. We investigated finite sample properties of our methods in an extensive simulation study and illustrated our methods in the HIV Prevention Trials Network (HPTN) 037 study.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755648","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":"Sensitivity analysis for the probability of benefit in randomized controlled trials with a binary treatment and a binary outcome.","authors":"Iuliana Ciocănea-Teodorescu, Erin E Gabriel, Arvid Sjölander","doi":"10.1093/biostatistics/kxaf011","DOIUrl":"10.1093/biostatistics/kxaf011","url":null,"abstract":"<p><p>For a comprehensive understanding of the effect of a given treatment on an outcome of interest, quantification of individual treatment heterogeneity is essential, alongside estimation of the average causal effect. However, even in randomized controlled trials, quantities such as the probability of benefit or the probability of harm are not identifiable, since multiple potential outcomes cannot be observed simultaneously for the same individual. We propose a sensitivity analysis for the probability of benefit in randomized controlled trial settings with a binary treatment and a binary outcome, by quantifying the deviation from conditional independence of the two potential outcomes, given a set of measured prognostic baseline covariates. We do this using a marginal sensitivity analysis parameter that does not depend on the number or complexity of the measured covariates. We provide a guide to estimation and interpretation, and illustrate our method in simulations, as well as using a real data example from a randomized controlled trial studying the effect of umbilical vein oxytocin administration on the need for manual removal of the placenta during birth.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12129078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210358","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":"Determining vaccine responders in the presence of baseline immunity using single-cell assays and paired control samples.","authors":"Zhe Chen, Siyu Heng, Asa Tapley, Stephen De Rosa, Bo Zhang","doi":"10.1093/biostatistics/kxaf045","DOIUrl":"10.1093/biostatistics/kxaf045","url":null,"abstract":"<p><p>A key objective in vaccine studies is to evaluate vaccine-induced immunogenicity and determine whether participants have mounted a response to the vaccine. Cellular immune responses are essential for assessing vaccine-induced immunogenicity, and single-cell assays, such as intracellular cytokine staining (ICS) and B-cell phenotyping (BCP), are commonly employed to profile individual immune cell phenotypes and the cytokines they produce after stimulation. In this article, we introduce a novel statistical framework for identifying vaccine responders using ICS data collected before and after vaccination. This framework incorporates paired control data to account for potential unintended variations between assay runs, such as batch effects, that could lead to misclassification of participants as vaccine responders or non-responders. To formally integrate paired control data for accounting for assay variation across different time points (ie before and after vaccination), our proposed framework calculates and reports two $ P $-values, both adjusting for paired control data but in distinct ways: (i) the maximally adjusted $ P $-value, which applies the most conservative adjustment to the unadjusted $ P $-value, ensuring validity over all plausible batch effects consistent with the paired control samples' data, and (ii) the minimally adjusted $ P $-value, which imposes only the minimal adjustment to the unadjusted $ P $-value, such that the adjusted $ P $-value cannot be falsified by the paired control samples' data. Minimally and maximally adjusted $ P $-values offer a balanced approach to managing Type I error rates and statistical power in the presence of batch effects. We apply this framework to analyze ICS data collected at baseline and 4 wks post-vaccination from the COVID-19 Prevention Network (CoVPN) 3008 study. Our analysis helps address two clinical questions: (i) which participants exhibited evidence of an incident Omicron infection between baseline and 4 wks after receiving the final dose of the primary vaccination series, and (ii) which participants showed vaccine-induced T cell responses against the Omicron BA.4/5 Spike protein.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145643009","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":"A filtering approach for statistical inference in a stochastic SIR model with an application to Covid-19 data.","authors":"Katia Colaneri, Camilla Damian, Rüdiger Frey","doi":"10.1093/biostatistics/kxaf036","DOIUrl":"10.1093/biostatistics/kxaf036","url":null,"abstract":"<p><p>In this paper, we consider a discrete-time stochastic SIR model, where the transmission rate and the number of infectious individuals are random and unobservable. This model accounts for random fluctuations in infectiousness and for non-detected infections. Thus, statistical inference has to be performed in a partial information setting. We adopt a Bayesian approach and use nested particle filtering to estimate the state of the system and the parameters. Moreover, we discuss forecasts and model tests based on the posterior predictive distribution. As a case study, we apply our methodology to Austrian Covid-19 infection data.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12554006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145373268","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":"Understanding the opioid syndemic in North Carolina: A novel approach to modeling and identifying factors.","authors":"Eva Murphy, David Kline, Kathleen L Egan, Kathryn E Lancaster, William C Miller, Lance A Waller, Staci A Hepler","doi":"10.1093/biostatistics/kxae052","DOIUrl":"10.1093/biostatistics/kxae052","url":null,"abstract":"<p><p>The opioid epidemic is a significant public health challenge in North Carolina, but limited data restrict our understanding of its complexity. Examining trends and relationships among different outcomes believed to reflect opioid misuse provides an alternative perspective to understand the opioid epidemic. We use a Bayesian dynamic spatial factor model to capture the interrelated dynamics within six different county-level outcomes, such as illicit opioid overdose deaths, emergency department visits related to drug overdose, treatment counts for opioid use disorder, patients receiving prescriptions for buprenorphine, and newly diagnosed cases of acute and chronic hepatitis C virus and human immunodeficiency virus. We design the factor model to yield meaningful interactions among predefined subsets of these outcomes, causing a departure from the conventional lower triangular structure in the loadings matrix and leading to familiar identifiability issues. To address this challenge, we propose a novel approach that involves decomposing the loadings matrix within a Markov chain Monte Carlo algorithm, allowing us to estimate the loadings and factors uniquely. As a result, we gain a better understanding of the spatio-temporal dynamics of the opioid epidemic in North Carolina.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048855","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":"Functional quantile principal component analysis.","authors":"Álvaro Méndez-Civieta, Ying Wei, Keith M Diaz, Jeff Goldsmith","doi":"10.1093/biostatistics/kxae040","DOIUrl":"10.1093/biostatistics/kxae040","url":null,"abstract":"<p><p>This paper introduces functional quantile principal component analysis (FQPCA), a dimensionality reduction technique that extends the concept of functional principal components analysis (FPCA) to the examination of participant-specific quantiles curves. Our approach borrows strength across participants to estimate patterns in quantiles, and uses participant-level data to estimate loadings on those patterns. As a result, FQPCA is able to capture shifts in the scale and distribution of data that affect participant-level quantile curves, and is also a robust methodology suitable for dealing with outliers, heteroscedastic data or skewed data. The need for such methodology is exemplified by physical activity data collected using wearable devices. Participants often differ in the timing and intensity of physical activity behaviors, and capturing information beyond the participant-level expected value curves produced by FPCA is necessary for a robust quantification of diurnal patterns of activity. We illustrate our methods using accelerometer data from the National Health and Nutrition Examination Survey, and produce participant-level 10%, 50%, and 90% quantile curves over 24 h of activity. The proposed methodology is supported by simulation results, and is available as an R package.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513407","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":"Mediation analysis with graph mediator.","authors":"Yixi Xu, Yi Zhao","doi":"10.1093/biostatistics/kxaf004","DOIUrl":"10.1093/biostatistics/kxaf004","url":null,"abstract":"<p><p>This study introduces a mediation analysis framework when the mediator is a graph. A Gaussian covariance graph model is assumed for graph presentation. Causal estimands and assumptions are discussed under this presentation. With a covariance matrix as the mediator, a low-rank representation is introduced and parametric mediation models are considered under the structural equation modeling framework. Assuming Gaussian random errors, likelihood-based estimators are introduced to simultaneously identify the low-rank representation and causal parameters. An efficient computational algorithm is proposed and asymptotic properties of the estimators are investigated. Via simulation studies, the performance of the proposed approach is evaluated. Applying to a resting-state fMRI study, a brain network is identified within which functional connectivity mediates the sex difference in the performance of a motor task.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11979487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626882","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}