Statistics in Medicine最新文献

{"title":"Synergy Area With FDR-Controlled Evaluation (SAFE) to Robustly Assess Safety Profile in Clinical Trials.","authors":"Tianyu Zhan, Yabing Mai, Yihua Gu, Thao Doan, Xun Chen","doi":"10.1002/sim.70592","DOIUrl":"https://doi.org/10.1002/sim.70592","url":null,"abstract":"<p><p>Safety assessment plays a fundamental role in developing a new drug via clinical trials for ethical considerations. Due to complexity, manual review is typically conducted on the totality of data to draw safety conclusions. There are some existing quantitative methods to facilitate or tailor further medical review, with a controlled error rate and integration of clinical knowledge. In addition to those two key aspects, we emphasize the importance of relying on substantial evidence to draw robust conclusions on safety. Motivated by these three important properties, we propose a two-layer Synergy Area with FDR-controlled Evaluation (SAFE) structural framework to robustly assess the safety profile in clinical trials. In the first layer of SAFE, we investigate each clinically meaningful Synergy Area (SA) based on compelling evidence. In the next layer, the false discovery rate (FDR) is controlled for potential findings across all SAs. Simulation studies show that SAFE properly controls error rates within and across SAs at the nominal level. We further apply the proposed approach to two case studies based on real data from the Historical Trial Data (HTD) Sharing Initiative of the DataCelerate platform. As compared to some direct methods, SAFE demonstrates an appealing feature of screening out extreme data and reaching solid safety conclusions. It can act as either a building block in another framework, or a platform to incorporate additional components.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70592"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">A Unified Framework for Statistical Inference and Power Analysis of Single and Comparative <ns0:math> <ns0:semantics> <ns0:mrow><ns0:msub><ns0:mi>F</ns0:mi> <ns0:mi>β</ns0:mi></ns0:msub> </ns0:mrow> <ns0:annotation>$$ {F}_{beta } $$</ns0:annotation></ns0:semantics> </ns0:math> Scores.","authors":"Chih-Yuan Hsu, Qi Liu, Yu Shyr","doi":"10.1002/sim.70557","DOIUrl":"10.1002/sim.70557","url":null,"abstract":"<p><p>Machine learning and artificial intelligence are increasingly applied to medical diagnostics and clinical decision-making. To evaluate model performance, the <math> <semantics> <mrow><msub><mi>F</mi> <mn>1</mn></msub> </mrow> <annotation>$$ {F}_1 $$</annotation></semantics> </math> score and its generalized form, the <math> <semantics> <mrow><msub><mi>F</mi> <mi>β</mi></msub> </mrow> <annotation>$$ {F}_{beta } $$</annotation></semantics> </math> score, are widely used as they balance precision and sensitivity. However, rigorous statistical inference and power analysis for the <math> <semantics> <mrow><msub><mi>F</mi> <mn>1</mn></msub> </mrow> <annotation>$$ {F}_1 $$</annotation></semantics> </math> and <math> <semantics> <mrow><msub><mi>F</mi> <mi>β</mi></msub> </mrow> <annotation>$$ {F}_{beta } $$</annotation></semantics> </math> scores remain limited. In this study, we propose psF1, a unified and comprehensive framework for interval estimation, hypothesis testing, and power and sample size calculation for both single and comparative <math> <semantics> <mrow><msub><mi>F</mi> <mn>1</mn></msub> </mrow> <annotation>$$ {F}_1 $$</annotation></semantics> </math> and <math> <semantics> <mrow><msub><mi>F</mi> <mi>β</mi></msub> </mrow> <annotation>$$ {F}_{beta } $$</annotation></semantics> </math> scores. psF1 leverages exact probability distributions as well as approximations for large sample sizes to provide valid statistical inference and power analyses. Extensive simulations demonstrate the accuracy and robustness of psF1 across a range of sensitivity, precision, and sample size scenarios. We further showcase its practical utility through real-world biomedical classification tasks. This framework enables principled evaluation and comparison of classifiers using <math> <semantics> <mrow><msub><mi>F</mi> <mn>1</mn></msub> </mrow> <annotation>$$ {F}_1 $$</annotation></semantics> </math> and <math> <semantics> <mrow><msub><mi>F</mi> <mi>β</mi></msub> </mrow> <annotation>$$ {F}_{beta } $$</annotation></semantics> </math> scores with reliable uncertainty quantification and informed sample size planning. psF1 is freely available at http://github.com/cyhsuTN/psF1.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70557"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13125741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147780606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moving Toward Best Practice When Using Propensity Score Weighting in Survey Observational Studies.","authors":"Yukang Zeng, Fan Li, Guangyu Tong","doi":"10.1002/sim.70555","DOIUrl":"https://doi.org/10.1002/sim.70555","url":null,"abstract":"<p><p>Propensity score weighting is a common method for estimating treatment effects with observational data, by addressing confounding due to measured baseline covariates. However, when the observational data sample is drawn based on a survey, the existing literature does not reach a consensus on the optimal use of survey weights for population-level causal inference. Under the balancing weights framework, we provide a unified solution for incorporating survey weights and derive a set of weighting and augmented weighting estimators for different target populations, including the combined, treated, controlled, and overlap populations. We also develop closed-form sandwich variance estimators for each estimator via the theory of M-estimators. Through an extensive series of simulation studies, we examined the performance of our estimators and compared the results to those of alternative methods. We carried out two case studies to illustrate the application of the different propensity score methods with complex survey data. We concluded with a discussion of our findings and provided some practical recommendations for propensity score weighting analysis of survey observational data.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70555"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Borrowing From Historical Controls via the Synthetic Prior With Covariates in Randomized Clinical Trials.","authors":"Daniel E Schwartz, Yuan Ji, Li Wang","doi":"10.1002/sim.70567","DOIUrl":"https://doi.org/10.1002/sim.70567","url":null,"abstract":"<p><p>Motivated by a rheumatoid arthritis clinical trial, we propose a new Bayesian method called SPx, standing for synthetic prior with covariates, to borrow information from historical trials to reduce the control group size in a new trial. The method involves a novel use of Bayesian model averaging to balance between multiple possible relationships between the historical and new trial data, allowing the historical data to be dynamically trusted or discounted as appropriate. We require only trial-level summary statistics, which are available more often than patient-level data. Through simulations and an application to the rheumatoid arthritis trial we show that SPx can substantially reduce the control group size while maintaining Frequentist properties.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70567"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13148236/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.","authors":"Zhuoqun Wang, Jialiang Mao, Li Ma","doi":"10.1002/sim.70584","DOIUrl":"10.1002/sim.70584","url":null,"abstract":"<p><p>Microbiome compositional data are often high-dimensional, sparse, and exhibit pervasive cross-sample heterogeneity. We introduce the \"logistic-tree normal\" (LTN) model, a generative model that allows flexible covariance among the microbiome taxa, enables scalable computation, and effectively captures other key characteristics of microbiome compositional data such as the abundance of zeros. LTN incorporates a tree-based decomposition for effective aggregation over sparse taxa counts and models the relative abundance at the tree splits jointly using a (multivariate) logistic-normal distribution. The latent Gaussian structure allows a wide range of multivariate analysis and modeling tools for high-dimensional data-such as those enforcing sparsity or low-rank assumptions on the covariance structure-to be readily incorporated. As a general-purpose, fully generative model, LTN can be applied in a wide range of contexts, while at the same time, efficient computational recipes for Bayesian inference under LTN are available through conjugate blocked Gibbs sampling enabled by pólya-gamma augmentation. We demonstrate the use of LTN in a compositional mixed-effects model for differential abundance analysis through both numerical experiments and a reanalysis of the infant cohort in the DIABIMMUNE study. We explain and showcase through numerical experiments and the case study how LTN, through adequately accounting for the cross-sample heterogeneity, is capable of generating the appropriate proportion of zeros without incurring an explicit zero-inflation component. This confirms a recent viewpoint that \"zero-inflation\" in count-based sequencing data are often results of unaccounted cross-sample variation.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70584"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13155195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Latent-factor MCACE Model for Multidimensional Outcomes and Treatment Noncompliance with Application to a Longitudinal Trial of Arthritis Health Journal.","authors":"Lulu Guo, Yi Qian, Diane Lacaille, Hui Xie","doi":"10.1002/sim.70532","DOIUrl":"https://doi.org/10.1002/sim.70532","url":null,"abstract":"<p><p>Real-world randomized controlled trials (RCTs) evaluating multifaceted interventions often employ multiple study outcomes to measure treatment effects on a small set of underlying constructs. Motivated by a longitudinal RCT evaluating a behavioural intervention, the Arthritis Health Journal (AHJ), we propose a latent-factor multivariate complier average causal effects (MCACE) model for multidimensional longitudinal outcomes with principal strata of compliance types for parsimonious estimation of intervention effects in RCTs with treatment noncompliance. Within each compliance type, a factor regression model relates multiple outcomes to latent constructs, which follow hierarchical regression models. Under the model, high dimensional outcomes are reduced to low dimensional latent factors. This dimension reduction leads to a parsimonious and efficient test of overall CACEs on multiple outcomes, mitigating the multiple testing issues associated with multidimensional outcomes and weak instrumental variable problems associated with low compliance rates. Simulation studies demonstrate that the latent-factor MCACE model outperforms univariate CACE analysis in terms of both statistical power and Type I error control. The application to the AHJ study selects two underlying factors (self-efficacy and interaction with health care providers). Significant and beneficial treatment effects are detected on both factors. Overall, our analysis directly answers the main scientific questions posed by the RCT and yields novel findings not discovered previously.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70532"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13111790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Heterogeneity Adjustment for Gaussian Graphical Model With Latent Variables.","authors":"Linxi Li, Rong Li, Shuangge Ma, Qingzhao Zhang","doi":"10.1002/sim.70571","DOIUrl":"10.1002/sim.70571","url":null,"abstract":"<p><p>Graphical models serve as fundamental tools for encoding conditional dependence structures in multivariate biological data, with latent variable Gaussian graphical models playing a pivotal role in capturing complex dependencies in the presence of unobserved confounding variables. However, practical implementations often face two critical challenges: systematic heterogeneity arising from unobserved subpopulations (e.g., tumor subtypes, cell clusters, or patient stratifications) and outliers (e.g., technical artifacts or rare phenotypic variations), both of which can substantially distort the underlying network structure. To address these issues, we extend the latent variable Gaussian graphical model by integrating a mixture model, proposing a robust framework tailored for data heterogeneity. The proposed method can simultaneously achieve network structure estimation (after removing shared effects from latent variables), outlier detection, and subgroup membership identification. An effective computational algorithm is developed. Extensive experimental evaluations demonstrate that the proposed method offers a reliable graphical estimate in the presence of heterogeneity, maintaining robustness even against a significant proportion of outliers. The heterogeneity analysis of a breast cancer dataset further illustrates the practical applicability of the proposed approach and its sound biological implications.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70571"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fiducial Confidence Intervals for Agreement Measures Among Raters Under a Generalized Linear Mixed Effects Model.","authors":"Soumya Sahu, Thomas Mathew, Dulal K Bhaumik","doi":"10.1002/sim.70578","DOIUrl":"https://doi.org/10.1002/sim.70578","url":null,"abstract":"<p><p>A generalization of the classical concordance correlation coefficient (CCC) is considered under a three-level design where multiple raters rate every subject over time, and each rater is rating every subject multiple times at each measuring time point. The ratings can be discrete or continuous. A methodology is developed for the interval estimation of the CCC based on a suitable linearization of the model along with an adaptation of the fiducial inference approach. The resulting confidence intervals have satisfactory coverage probabilities and shorter expected widths compared to the interval based on Fisher's Z-transformation, even under moderate sample sizes. Two real applications available in the literature are discussed. The first application is based on a clinical trial to determine if various treatments are more effective than a placebo for treating knee pain associated with osteoarthritis. The CCC was used to assess agreement among the manual measurements of the joint space widths on plain radiographs by two raters, and the computer-generated measurements of digitalized radiographs. The second example is on a corticospinal tractography and the CCC was once again applied in order to evaluate the agreement between a well-trained technologist and a neuroradiologist regarding the measurements of fiber number in both the right and left corticospinal tracts. Other relevant applications of our general approach are highlighted in many areas including artificial intelligence.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70578"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147820944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Tutorial on Bayesian Multi-Study Factor Analysis With Applications in Nutrition and Genomics.","authors":"Mavis Liang, Blake Hansen, Alejandra Avalos-Pacheco, Roberta De Vito","doi":"10.1002/sim.70531","DOIUrl":"https://doi.org/10.1002/sim.70531","url":null,"abstract":"<p><p>High-dimensional data are crucial in biomedical research. Integrating such data from multiple studies is a critical process that relies on the choice of advanced statistical models, enhancing statistical power, reproducibility, and scientific insight compared to analyzing each study separately. Factor analysis (FA) is a core dimensionality reduction technique that models observed data through a small set of latent factors. Bayesian extensions of FA have recently emerged as powerful tools for multi-study integration, enabling researchers to disentangle shared biological signals from study-specific variability. In this tutorial, we provide a practical and comparative guide to seven advanced Bayesian integrative factor models: Perturbed Factor Analysis (PFA), Bayesian Factor Regression with non-local spike-and-slab priors (MOM-SS), Subspace Factor Analysis (SUFA), Bayesian Multi-study Factor Analysis (BMSFA), a variational-inference implementation of BMSFA (CAVI), Bayesian Latent Analysis through Spectral Training (BLAST), and Bayesian Combinatorial Multi-study Factor Analysis (Tetris). To contextualize these methods, we also include two benchmark approaches: Standard FA applied to pooled data (Stack FA), and FA applied separately to each study (Ind FA). We evaluate all methods through extensive simulations, assessing computational efficiency, accuracy in estimation of loadings, and the number of factors. To bridge theory and practice, we present a full analytical workflow-with detailed R code-demonstrating how to apply these models to real-world datasets in nutrition and genomics. This tutorial is designed to guide applied researchers through the landscape of Bayesian integrative factor analysis, offering insights and tools for extracting interpretable, robust patterns from complex multi-source data. Simulation code, R package \"bmfaToolkits\" and a user-friendly guidebook can be found at https://github.com/Mavis-Liang/Bayesian_integrative_FA_tutorial.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70531"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Test-Negative Designs With Multiple Testing Sources.","authors":"Mengxin Yu, Nicholas P Jewell","doi":"10.1002/sim.70525","DOIUrl":"10.1002/sim.70525","url":null,"abstract":"<p><p>Test-negative designs, a form of case-cohort studies, have been commonly used to assess infectious disease interventions. Early examples of the design included the evaluation of seasonal influenza vaccines in the field. Recently, they have also been widely used to evaluate the efficacy of COVID-19 vaccines in preventing symptomatic disease for different variants [32]. The design hinges on individuals being tested for the disease of interest; upon recruitment, such individuals are subjected to a definitive test for the presence of the disease of interest (test-positives) or not (test-negatives), along with the determination of whether the individual has been exposed to the intervention under study (e.g., vaccination). In most early TND studies, individuals were tested because they were suffering from symptoms consistent with the disease in question, and the TND was a tool to reduce confounding due to healthcare-seeking behavior. However, in many cases, such as COVID-19 and Ebola, testing results were available at healthcare facilities for individuals who presented for a variety of reasons in addition to symptoms (e.g., case contact tracing, etc.). Aggregating samples from symptomatic and asymptomatic test results leads to bias in the assessment of the efficacy of the intervention. Here we consider these issues in the context of a specific version of the 'multiple reasons for testing problem,' motivated by a vaccine trial designed to assess a new Ebola viral disease vaccine (EVD) [22]. Some participants are recruited in the usual TND fashion as they present for care suffering from symptoms consistent with an Ebola diagnosis (and are thus tested); in addition, however, any test-positive identified in this fashion leads to immediate testing for Ebola for all close contacts of the test-positive who are likely asymptomatic at that point. We examine a simple approach to estimate the common efficacy of the vaccine intervention based on these two sources of test positives and test negatives, complemented by an assessment of whether efficacy is the same for both sources. The EVD trial was not completed for the fortunate reason that the prevailing disease outbreak ended; nevertheless, the approach here will be important if this trial is ever recommenced or similar trials are conducted in the future.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70525"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}