Biostatistics最新文献

{"title":"Flexible nonparametric assessment of time-lag bias in meta-analysis.","authors":"Xing Xing, Zhiyuan Yu, Mengli Xiao, Jiayi Tong, Lifeng Lin","doi":"10.1093/biostatistics/kxag033","DOIUrl":"10.1093/biostatistics/kxag033","url":null,"abstract":"<p><p>Time-lag bias occurs when studies with larger or statistically significant effects appear in print sooner than those with smaller or null effects, which can inflate early meta-analytic estimates and mislead decision makers. Existing diagnostics typically rely on cumulative meta-analysis or meta-regression of effects on publication time. These approaches are vulnerable to repeated-testing inflation and loss of power when temporal trends are nonlinear. We propose a family of flexible nonparametric statistics that summarize standardized pairwise differences between early and later studies, each emphasizing different magnitudes of temporal contrast, and a hybrid test that adaptively combines evidence across these differences via permutation. The procedure controls type I error under exchangeability of publication order and is sensitive to diverse temporal patterns, including linear trends, early outliers, and oscillations in effect sizes over time. Simulations across 9 scenarios, with and without heterogeneity, show that the hybrid test maintains the nominal error rate and improves statistical power relative to linear meta-regression when temporal trends are nonlinear. In 2 clinical meta-analyses, our method detects pronounced time-lag patterns that standard regression misses. We provide guidance for practice and discuss practical considerations for detecting and interpreting time-lag bias in meta-analyses.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13598947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026827","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":"Data-adaptive identification of effect modifiers through stochastic shift interventions and cross-validated targeted learning.","authors":"David McCoy, Wenxin Zhang, Alan Hubbard, Mark van der Laan, Alejandro Schuler","doi":"10.1093/biostatistics/kxag020","DOIUrl":"10.1093/biostatistics/kxag020","url":null,"abstract":"<p><p>In epidemiology, identifying subpopulations that are particularly vulnerable to exposures and those who may benefit differently from exposure-reducing interventions is essential. Factors such as age, gender-specific vulnerabilities, and physiological states such as pregnancy are critical for policymakers when setting regulatory guidelines. However, current semiparametric methods for estimating heterogeneous treatment effects are often limited to binary exposures and can function as black boxes, lacking clear, interpretable rules for subpopulation-specific policy interventions. This study introduces a novel method that uses cross-validated targeted minimum loss-based estimation (TMLE) paired with a data-adaptive target parameter strategy to identify subpopulations with the most significant differential impact of simulated policy interventions that reduce exposure. Our approach is assumption-lean, allowing for the integration of machine learning while still yielding valid confidence intervals. We demonstrate the robustness of our methodology through simulations and an application to data from the National Health and Nutrition Examination Survey. Our analysis of NHANES data on persistent organic pollutants (POPs) and leukocyte telomere length (LTL) identified age as a significant effect modifier. Specifically, we found that exposure to 3,3',4,4',5-pentachlorobiphenyl (PCB; NHANES analyte LBXPCBLA) consistently had a differential impact on LTL, with a 1-SD reduction in exposure leading to a more pronounced increase in LTL among younger populations than in older ones. We offer our method as an open-source software package, EffectXshift, enabling researchers to investigate the effect modification of continuous exposures. The EffectXshift package provides clear and interpretable results, informing targeted public health interventions and policydecisions.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13349617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148426781","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":"Bayesian nonparametric mixtures of categorical directed graphs for personalized causal inference.","authors":"Federico Castelletti, Laura Ferrini","doi":"10.1093/biostatistics/kxag026","DOIUrl":"10.1093/biostatistics/kxag026","url":null,"abstract":"<p><p>Quantifying the causal effect of a treatment on a disease is a crucial task in medical science for the administration of effective therapies. Typically, such causal effects are inferred from multivariate data that are collected on patients and recorded in the form of categorical variables, including risk factors involved in disease progression, treatment assignments, and disease status. This feature motivates an approach to causal inference based on categorical Directed Acyclic Graphs (DAGs), which provide an effective framework for causal reasoning in complex multivariate settings. In this context, traditional DAG-based methods assume population homogeneity and accordingly attribute a unique causal effect to all subjects. However, this assumption is often unrealistic in clinical contexts, since patients may exhibit heterogeneous characteristics, possibly linked to unmeasured features. To address this issue, we propose a Bayesian nonparametric methodology based on a Dirichlet Process mixture of categorical DAGs, which allows treatment effects to vary across individuals because of underlying clustering structures in the data. We develop a Markov chain Monte Carlo algorithm for Bayesian posterior inference and evaluate our methodology through simulation studies. We then analyze patients affected by HER2+ breast cancer undergoing therapies that may cause cardiotoxic side effects. Importantly, our findings show that approaches neglecting population heterogeneity may produce biased results, since they can over- or under-estimate the risk of cardiotoxicity across patients.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13407445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148610251","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":"A monotone single-index modal regression powered by deep neural networks for non-Gaussian periodontal data.","authors":"Qingyang Liu, Shijie Wang, Ray Bai, Dipankar Bandyopadhyay","doi":"10.1093/biostatistics/kxag040","DOIUrl":"https://doi.org/10.1093/biostatistics/kxag040","url":null,"abstract":"<p><p>Pocket depth (PD) is a widely used biomarker for diagnosing risk of periodontal disease (PrD). However, PD typically exhibits skewness and heavy-tailedness, and its relationship with clinical risk factors is often nonlinear. Motivated by PrD studies, this paper develops a robust single-index modal regression framework for analyzing skewed and heavy-tailed data. Our method has the following novel features: (i) a flexible 2-piece scale Student-$ boldsymbol{t} $ error distribution that generalizes both normal and 2-piece scale normal distributions; (ii) a neural network with guaranteed monotonicity constraints to estimate the unknown single-index function; and (iii) theoretical support, including model identifiability and a universal approximation theorem. Our single-index model combines the flexibility of neural networks and the 2-piece scaled Student-$ boldsymbol{t} $ distribution, delivering robust mode-based estimation that is resistant to outliers, while retaining clinical interpretability through parametric index coefficients. We demonstrate the performance of our method through simulation studies and an application to PrD electronic health records obtained from the HealthPartners Institute of Minnesota. The proposed methodology is implemented in the R package DNNSIM.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149118813","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":"Risk functions with outcome measurement error.","authors":"Jessie K Edwards, Stephen R Cole, Paul N Zivich, Benjamin Ackerman, Sonia Napravnik, Heather Henderson, Timothy Lash, Bonnie E Shook-Sa","doi":"10.1093/biostatistics/kxaf052","DOIUrl":"10.1093/biostatistics/kxaf052","url":null,"abstract":"<p><p>Mortality risk estimated from studies that ascertain date of death through linkage to vital statistics registries may be subject to outcome measurement error. As a result, some deaths among study participants may not be captured, some study participants who are alive may be falsely categorized as deceased, and some deaths may be recorded at incorrect times, leading to bias in estimates of mortality risk and survival. Here, we illustrate an extension of the Rogan-Gladen estimator to account for outcome measurement error in risk and survival functions in settings with right censoring. As a motivating application, we consider and account for outcome measurement error that could be induced by incomplete and/or incorrect linkage to death registries when estimating mortality risk among people entering care for HIV in the University of North Carolina Center for AIDS Research HIV Clinical Cohort between 2001 and 2022. A series of simulation studies demonstrates that the approach performed well even when participants selected into the validation study were at higher mortality risk than the main study. The proposed approach may be parameterized using internal or external validation data or used as a form of quantitative bias analysis.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"27 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12815892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146004702","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":"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":"Shared parameter modeling of longitudinal data allowing for possibly informative visiting process and terminal event.","authors":"Christos Thomadakis, Loukia Meligkotsidou, Nikos Pantazis, Giota Touloumi","doi":"10.1093/biostatistics/kxae041","DOIUrl":"10.1093/biostatistics/kxae041","url":null,"abstract":"<p><p>Joint modeling of longitudinal and time-to-event data, particularly through shared parameter models (SPMs), is a common approach for handling longitudinal marker data with an informative terminal event. A critical but often neglected assumption in this context is that the visiting/observation process is noninformative, depending solely on past marker values and visit times. When this assumption fails, the visiting process becomes informative, resulting potentially to biased SPM estimates. Existing methods generally rely on a conditional independence assumption, positing that the marker model, visiting process, and time-to-event model are independent given shared or correlated random effects. Moreover, they are typically built on an intensity-based visiting process using calendar time. This study introduces a unified approach for jointly modeling a normally distributed marker, the visiting process, and time-to-event data in the form of competing risks. Our model conditions on the history of observed marker values, prior visit times, the marker's random effects, and possibly a frailty term independent of the random effects. While our approach aligns with the shared-parameter framework, it does not presume conditional independence between the processes. Additionally, the visiting process can be defined on either a gap time scale, via proportional hazard models, or a calendar time scale, via proportional intensity models. Through extensive simulation studies, we assess the performance of our proposed methodology. We demonstrate that disregarding an informative visiting process can yield significantly biased marker estimates. However, misspecification of the visiting process can also lead to biased estimates. The gap time formulation exhibits greater robustness compared to the intensity-based model when the visiting process is misspecified. In general, enriching the visiting process with prior visit history enhances performance. We further apply our methodology to real longitudinal data from HIV, where visit frequency varies substantially among individuals.</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/PMC11911807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513409","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":"Recoverability of causal effects under presence of missing data: a longitudinal case study.","authors":"Anastasiia Holovchak, Helen McIlleron, Paolo Denti, Michael Schomaker","doi":"10.1093/biostatistics/kxae044","DOIUrl":"10.1093/biostatistics/kxae044","url":null,"abstract":"<p><p>Missing data in multiple variables is a common issue. We investigate the applicability of the framework of graphical models for handling missing data to a complex longitudinal pharmacological study of children with HIV treated with an efavirenz-based regimen as part of the CHAPAS-3 trial. Specifically, we examine whether the causal effects of interest, defined through static interventions on multiple continuous variables, can be recovered (estimated consistently) from the available data only. So far, no general algorithms are available to decide on recoverability, and decisions have to be made on a case-by-case basis. We emphasize the sensitivity of recoverability to even the smallest changes in the graph structure, and present recoverability results for three plausible missingness-directed acyclic graphs (m-DAGs) in the CHAPAS-3 study, informed by clinical knowledge. Furthermore, we propose the concept of a \"closed missingness mechanism\": if missing data are generated based on this mechanism, an available case analysis is admissible for consistent estimation of any statistical or causal estimand, even if data are missing not at random. Both simulations and theoretical considerations demonstrate how, in the assumed MNAR setting of our study, a complete or available case analysis can be superior to multiple imputation, and estimation results vary depending on the assumed missingness DAG. Our analyses demonstrate an innovative application of missingness DAGs to complex longitudinal real-world data, while highlighting the sensitivity of the results with respect to the assumed causal model.</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/PMC7617375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649856","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":"Random forest for dynamic risk prediction of recurrent events: a pseudo-observation approach.","authors":"Abigail Loe, Susan Murray, Zhenke Wu","doi":"10.1093/biostatistics/kxaf007","DOIUrl":"10.1093/biostatistics/kxaf007","url":null,"abstract":"<p><p>Recurrent events are common in clinical, healthcare, social, and behavioral studies, yet methods for dynamic risk prediction of these events are limited. To overcome some long-standing challenges in analyzing censored recurrent event data, a recent regression analysis framework constructs a censored longitudinal dataset consisting of times to the first recurrent event in multiple pre-specified follow-up windows of length $ tau $(XMT models). Traditional regression models struggle with nonlinear and multiway interactions, with success depending on the skill of the statistical programmer. With a staggering number of potential predictors being generated from genetic, -omic, and electronic health records sources, machine learning approaches such as the random forest regression are growing in popularity, as they can nonparametrically incorporate information from many predictors with nonlinear and multiway interactions involved in prediction. In this article, we (i) develop a random forest approach for dynamically predicting probabilities of remaining event-free during a subsequent $ tau $-duration follow-up period from a reconstructed censored longitudinal data set, (ii) modify the XMT regression approach to predict these same probabilities, subject to the limitations that traditional regression models typically have, and (iii) demonstrate how to incorporate patient-specific history of recurrent events for prediction in settings where this information may be partially missing. We show the increased ability of our random forest algorithm for predicting the probability of remaining event-free over a $ tau $-duration follow-up window when compared to our modified XMT method for prediction in settings where association between predictors and recurrent event outcomes is complex in nature. We also show the importance of incorporating past recurrent event history in prediction algorithms when event times are correlated within a subject. The proposed random forest algorithm is demonstrated using recurrent exacerbation data from the trial of Azithromycin for the Prevention of Exacerbations of Chronic Obstructive Pulmonary Disease.</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":"143626883","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 marginal structural model for normal tissue complication probability.","authors":"Thai-Son Tang, Zhihui Liu, Ali Hosni, John Kim, Olli Saarela","doi":"10.1093/biostatistics/kxae019","DOIUrl":"10.1093/biostatistics/kxae019","url":null,"abstract":"<p><p>The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modeling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.</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/PMC11823140/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141565187","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}