Biostatistics最新文献

{"title":"Semi-supervised calibration of inferred outcomes for estimating average treatment effects with validated outcomes.","authors":"Daniel A Xu, Katherine P Liao, Tianxi Cai, David Cheng","doi":"10.1093/biostatistics/kxag023","DOIUrl":"10.1093/biostatistics/kxag023","url":null,"abstract":"<p><p>Estimating treatment effects with electronic health record data is complicated by the lack of validated outcome measures collected under standard protocols. Typically, outcomes can only be approximated or inferred using putative rules or phenotyping models trained on small subsamples where validated outcomes are available. But biases in the inferred outcomes can introduce biases in downstream treatment effect estimates. To address this issue, we develop a semisupervised method to calibrate inferred outcomes for treatment effect estimation when a subsample is labeled at random with validated outcomes. The calibration ensures that the subsequent treatment effect estimator remains consistent despite errors in the inferred outcomes. This problem can be viewed to be analogous to that of estimating mean outcomes in a longitudinal study subject to monotone missingness, and we demonstrate connections with existing augmented inverse probability weighting estimators. The proposed estimator is multiply robust and locally semiparametric efficient. The finite sample performance is assessed through simulations. We also find that the estimator can achieve efficiency gains in finite samples in the presence of extreme labeling propensity scores owing to an effective normalization of implicit augmentation terms. The method is illustrated through an example analysis evaluating the comparative effectiveness of 2 anti-TNF therapies on disease activity status in patients with rheumatoid arthritis.</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/PMC13407498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148610241","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":"Penalized likelihood optimization for adaptive neighborhood clustering in time-to-event data with group-level heterogeneity.","authors":"Alessandra Ragni, Lara Cavinato, Francesca Ieva","doi":"10.1093/biostatistics/kxag039","DOIUrl":"10.1093/biostatistics/kxag039","url":null,"abstract":"<p><p>The identification of patient subgroups with comparable event-risk dynamics plays a key role in supporting informed decision-making in clinical research. In such settings, it is important to account for the inherent dependence that arises when individuals are nested within higher-level units, such as hospitals. Existing survival models account for group-level heterogeneity through frailty terms but do not uncover latent patient subgroups, while most clustering methods ignore hierarchical structure and are not estimated jointly with survival outcomes. In this work, we introduce a new framework that simultaneously performs patient clustering and shared-frailty survival modeling through a penalized likelihood approach. The proposed methodology adaptively learns a patient-to-patient similarity matrix via a modified version of spectral clustering, enabling cluster formation directly from estimated risk profiles while accounting for group membership. A simulation study highlights the proposed model's ability to recover latent clusters and to correctly estimate hazard parameters. We apply our method to a large cohort of heart-failure patients hospitalized with COVID-19 between 2020 and 2021 in the Lombardy region (Italy), identifying clinically meaningful subgroups characterized by distinct risk profiles and highlighting the role of respiratory comorbidities and hospital-level variability in shaping mortality outcomes. This framework provides a flexible and interpretable tool for risk-based patient stratification in hierarchical data settings. Accompanying software is available at https://github.com/alessandragni/HazClust.</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/PMC13623659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149082996","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":"Modeling complex measurement error in microbiome experiments to estimate relative abundances and detection effects.","authors":"David S Clausen, Amy D Willis","doi":"10.1093/biostatistics/kxag036","DOIUrl":"10.1093/biostatistics/kxag036","url":null,"abstract":"<p><p>Accurate estimates of microbial species abundances are needed to advance our understanding of the role that microbiomes play in human and environmental health. However, laboratory-constructed microbiomes demonstrate that intuitive estimators of microbial relative abundances are biased. To address this, we propose a method to estimate relative abundances, species detection effects, and/or cross-sample contamination in microbiome experiments. We show that certain experimental designs result in identifiable model parameters, and present consistent estimators and asymptotically valid inference procedures that are robust to misspecification of the working likelihood. Notably, our procedure can estimate relative abundances on the boundary of the simplex. We demonstrate the utility of the method for comparing experimental protocols, removing cross-sample contamination, and estimating species' detectability.</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/PMC13649368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149138934","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":"Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data.","authors":"Cristian Castiglione, Alexandre Segers, Lieven Clement, Davide Risso","doi":"10.1093/biostatistics/kxag010","DOIUrl":"10.1093/biostatistics/kxag010","url":null,"abstract":"<p><p>Single-cell RNA sequencing allows the quantification of gene expression at the individual cell level, enabling the study of cellular heterogeneity and gene expression dynamics. Dimensionality reduction is a common preprocessing step critical for the visualization, clustering, and phenotypic characterization of samples. This step, often performed using principal component analysis or closely related methods, is challenging because of the size and complexity of the data. In this work, we present a generalized matrix factorization model assuming a general exponential dispersion family distribution and we show that many of the proposed approaches in the single-cell dimensionality reduction literature can be seen as special cases of this model. Furthermore, we propose a scalable adaptive stochastic gradient descent algorithm that allows us to estimate the model efficiently, enabling the analysis of millions of cells. We benchmark the proposed algorithm through extensive numerical experiments against state-of-the-art methods and showcase its use in real-world biological applications. The proposed method systematically outperforms existing methods of both generalized and non-negative matrix factorization, demonstrating faster execution times and parsimonious memory usage, while maintaining, or even enhancing, matrix reconstruction fidelity and accuracy in biological signal extraction. On real data, we show that our method scales seamlessly to millions of cells, enabling dimensionality reduction in large single-cell datasets. Finally, all the methods discussed here are implemented in an efficient open-source R package, sgdGMF, available on CRAN.</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/PMC13143404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846569","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 Bayesian multivariate spatial point pattern model: application to oral microbiome FISH image data.","authors":"Kyu Ha Lee, Brent A Coull, Suman Majumder, Patrick J La Riviere, Jessica L Mark Welch, Jacqueline R Starr","doi":"10.1093/biostatistics/kxag028","DOIUrl":"10.1093/biostatistics/kxag028","url":null,"abstract":"<p><p>Advances in cellular imaging technologies, especially those based on fluorescence in situ hybridization (FISH), now allow detailed visualization of the spatial organization of human or bacterial cells. Quantifying this spatial organization is crucial for understanding the function of multicellular tissues or biofilms, with implications for human health and disease. To address the need for better methods to achieve such quantification, we propose a flexible multivariate point process model that characterizes and estimates complex spatial interactions among multiple cell types. The proposed Bayesian framework is appealing due to its unified estimation process and the ability to directly quantify uncertainty in key estimates of interest, such as those of inter-type correlation and the proportion of variance due to inter-type relationships. To ensure stable and interpretable estimation, we consider shrinkage priors for coefficients associated with latent processes that induce dependencies among point patterns. Model selection and comparison are conducted using a deviance information criterion designed for models with latent variables, providing a practical criterion for balancing model fit and complexity. Furthermore, we use a Bayesian hierarchical pooling model to synthesize image-specific posterior summaries, allowing inference at both the global- (across subjects) and subject-specific levels. An R package, mspatPPM, implements an efficient computational scheme based on Hamiltonian Monte Carlo and adaptive Metropolis-Hastings algorithms. Numerical studies evaluate the practical performance of the proposed framework for model selection and for estimating quantities that characterize the multivariate spatial distribution of cell types. We apply the proposed method to microbial biofilm image data from the human tongue dorsum and find that specific taxon pairs, such as Streptococcus mitis-Streptococcus salivarius and S. mitis-Veillonella, exhibit strong positive spatial correlations, while others, such as Actinomyces-Rothia, show slight negative correlations. For most of the taxa, a substantial portion of spatial variance can be attributed to inter-taxon relationships.</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/PMC13485354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802124","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 two-stage approach for segmenting spatial point patterns applied to multiplex imaging.","authors":"Alvin Sheng, Brian J Reich, Ana-Maria Staicu, Santhoshi N Krishnan, Arvind Rao, Timothy L Frankel","doi":"10.1093/biostatistics/kxaf049","DOIUrl":"10.1093/biostatistics/kxaf049","url":null,"abstract":"<p><p>Recent advances in multiplex imaging have enabled researchers to locate different types of cells within a tissue sample. This is especially relevant for tumor immunology, as clinical regimes corresponding to different stages of disease or responses to treatment may manifest as different spatial arrangements of tumor and immune cells. Spatial point pattern modeling can be used to partition multiplex tissue images according to these regimes. To this end, we propose a two-stage approach: first, local intensities and pair correlation functions are estimated from the spatial point pattern of cells within each image, and the pair correlation functions are reduced in dimension via spectral decomposition of the covariance function. Second, the estimates are clustered in a Bayesian hierarchical model with spatially-dependent cluster labels. The clusters correspond to regimes of interest that are present across subjects; the cluster labels segment the spatial point patterns according to those regimes. Through Markov Chain Monte Carlo sampling, we jointly estimate and quantify uncertainty in the cluster assignment and spatial characteristics of each cluster. Simulations demonstrate the performance of the method, and it is applied to a set of multiplex immunofluorescence images of diseased pancreatic tissue.</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/PMC12815891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146004726","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 Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.","authors":"Brody Erlandson, Ander Wilson, Matthew D Koslovsky","doi":"10.1093/biostatistics/kxag019","DOIUrl":"10.1093/biostatistics/kxag019","url":null,"abstract":"<p><p>The infant microbiome undergoes rapid changes in composition over time and is associated with long-term risks of conditions such as immune strength, allergy, asthma, and other health outcomes. Modeling the associations between exposures or treatments and microbial composition over time is essential for understanding the factors that drive these changes. Estimating these temporal dynamics has several challenges including repeated measures, overdispersion, compositionality, high-dimensional parameter spaces, and zero-inflation. Many longitudinal regression models used in human microbiome research assume constant effects over time that cannot capture time-varying or functional effects of exposures, ignore the compositional structure of the data by modeling each taxon separately, and are not equipped to handle potential zero-inflation. Dirichlet-multinomial (DM) regression models inherently accommodate overdispersion and the compositional structure of the data and have been extended to account for excess zeros. However, existing DM-based regression models are unable to additionally handle repeated measures designs. To fill this gap, we propose a functional concurrent zero-inflated Dirichlet-multinomial regression model which is designed to model time-varying relations between observed covariates and microbial taxa while accounting for zero-inflation, compositionality, and repeated measures. Through simulation, we demonstrate that the model can accurately estimate the underlying functional relations and scale to large compositional spaces. We apply our model to investigate time-varying associations between infant microbiome composition and observed covariates during the 11-wk postnatal period. We found that $ boldsymbol{alpha} $-diversity (ie the diversity of the microbiome within an individual) is positively associated with a higher gestational age and percentage of breast milk in the diet. We provide an accompanying R package and shiny app to implement the method and generate plots.</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/PMC13318497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148363852","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":"Borrowing from historical control data in a Bayesian time-to-event model with flexible baseline hazard function.","authors":"Darren A V Scott, Alex Lewin","doi":"10.1093/biostatistics/kxag006","DOIUrl":"10.1093/biostatistics/kxag006","url":null,"abstract":"<p><p>Currently, there is a focus on statistical methods that can use historical trial information to help accelerate the discovery, development, and delivery of medicines. Bayesian methods enable \"dynamic\" borrowing, allowing the similarity between current and historical data to help determine the extent of information incorporated. In the time-to-event setting, Bayesian borrowing methods typically impose a constraint on the shape of the baseline hazard, simplifying the modelling and improving the computational speed of estimation. However, we show that this approximation comes at a cost when trying to borrow historical information. We propose a Bayesian borrowing semiparametric model for one historical dataset, which allows the baseline hazard to take any form through an ensemble average. We introduce priors to smooth the shape of the posterior baseline hazard, improving both model estimation and borrowing characteristics. By accurately modelling the baseline hazard rather than approximating its form, power is improved and bias of the estimated treatment effect reduced when the borrowing assumption of parameter exchangeability holds. In the presence of prior-data conflict, the type I error inflation is reduced. We explore a variety of prior borrowing structures within our proposed model and evaluate their performance against established approaches. We show the benefit of a lump-and-smear borrowing prior in our joint model for improving type I error in the presence of prior-data conflict and increased power. A principled approach is proposed for the choice of hyperparameters to control the dynamic borrowing, based on the tolerated difference between the historical and log-baseline hazards. We have developed accompanying software available in R enabling easy implementation of our approach in clinical trial 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":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678583","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 sensitivity analysis approach to principal stratification with a continuous longitudinal intermediate outcome: applications to a cohort stepped wedge trial.","authors":"Lei Yang, Michael J Daniels, Fan Li","doi":"10.1093/biostatistics/kxag004","DOIUrl":"10.1093/biostatistics/kxag004","url":null,"abstract":"<p><p>Causal inference in the presence of intermediate variables is a challenging problem in many applications. Principal stratification (PS) provides a framework to estimate principal causal effects (PCE) in such settings. However, existing PS methods primarily focus on settings with binary intermediate variables. We propose a novel approach to estimate PCE with continuous intermediate variables in the context of stepped wedge cluster randomized trials (SW-CRTs). Our method leverages the time-varying treatment assignment in SW-CRTs to calibrate sensitivity parameters and identify the PCE under realistic assumptions. We demonstrate the application of our approach using data from a cohort SW-CRT evaluating the effect of a crowdsourcing intervention on HIV testing uptake among men who have sex with men in China, with social norms as a continuous intermediate variable. The proposed methodology expands the scope of PS to accommodate continuous variables and provides a practical tool for causal inference in SW-CRTs.</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/PMC13143403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846601","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":"Joint modeling of high-dimensional longitudinal data and survival using supervised low-rank tensor decomposition.","authors":"Mohammad Samsul Alam, Rima Kaddurah-Daouk, Sheng Luo","doi":"10.1093/biostatistics/kxag007","DOIUrl":"10.1093/biostatistics/kxag007","url":null,"abstract":"<p><p>High-dimensional longitudinal data are increasingly available in biomedical research, especially from omics platforms, but pose substantial challenges for joint modeling with survival outcomes. These challenges include modeling complex temporal dynamics, accommodating cross-feature dependencies, and maintaining computational feasibility. We propose a novel joint modeling framework that addresses these issues using supervised low-rank functional tensor decomposition to capture latent structure in multivariate longitudinal data and proportional hazards modeling for time-to-event outcomes. The longitudinal process is represented as a multivariate functional tensor, with a low-rank approximation that incorporates supervision from baseline covariates. Estimation is performed using a likelihood-based Monte Carlo Expectation-Maximization algorithm, enabling coherent inference and individualized prediction. Our method produces dynamic predictions of both longitudinal feature trajectories and survival probabilities. Simulation studies demonstrate substantial improvements in estimation accuracy and predictive performance over a standard two-stage approach, particularly under high censoring and limited sample sizes. In application to the Alzheimer's Disease Neuroimaging Initiative lipidomics data, the proposed model explains over 99% of variation with four components, and identifies significant subject-level latent predictors of dementia onset. This framework provides a scalable and interpretable strategy for integrating high-dimensional longitudinal biomarkers into joint models for disease progression and risk stratification.</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/PMC13070533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678730","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}