Biometrics最新文献_第2页

A latent factor panel approach to spatiotemporal causal inference. 时空因果推理的潜在因子面板方法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag153

Jiaxi Wu, Alexander Franks

引用次数: 0

P3LS: Point Process Partial Least Squares. P3LS：点过程偏最小二乘法。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag136

Jamshid Namdari, Robert T Krafty, Amita Manatunga

引用次数: 0

Multiply robust inference of average treatment effects by high-dimensional empirical likelihood. 将平均治疗效果的稳健推断乘以高维经验似然。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag149

Xintao Xia, Yumou Qiu

{"title":"Multiply robust inference of average treatment effects by high-dimensional empirical likelihood.","authors":"Xintao Xia, Yumou Qiu","doi":"10.1093/biomtc/ujag149","DOIUrl":"https://doi.org/10.1093/biomtc/ujag149","url":null,"abstract":"In this paper, we develop a multiply robust inference procedure for the average treatment effect (ATE) for data with high-dimensional covariates. We consider the case where it is difficult to correctly specify a single parametric model for the propensity scores (PS). We propose a novel high-dimensional empirical likelihood weighting method under soft covariate balancing constraints to combine multiple working PS models. An extended set of calibration functions is used, and a regularized augmented outcome regression (OR) is developed to correct the bias due to non-exact covariate balancing. Those two approaches provide a new way to construct the Neyman orthogonal score of the ATE. The proposed confidence interval for the ATE achieves asymptotically valid nominal coverage under high-dimensional covariates if any of the PS models, their linear combination, or the OR model is correctly specified. The proposed method is extended to generalized linear models for the outcome variable and data with unknown clusters. Specifically, we consider estimating the ATE for data with unknown clusters, where multiple working PS models can be fitted based on the estimated clusters. We demonstrate the advantages of the proposed approach over existing doubly robust inference methods under high-dimensional covariates via simulation studies. We analyze the right heart catheterization dataset, collected in two study phases across five medical centers, to demonstrate the effectiveness of the proposed method.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148910676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian adaptive digital twin design for avatar-driven cancer clinical trials. 一种用于avatar驱动的癌症临床试验的贝叶斯自适应数字孪生设计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag165

Peng Yang, Yeonhee Park, Suyu Liu

{"title":"A Bayesian adaptive digital twin design for avatar-driven cancer clinical trials.","authors":"Peng Yang, Yeonhee Park, Suyu Liu","doi":"10.1093/biomtc/ujag165","DOIUrl":"https://doi.org/10.1093/biomtc/ujag165","url":null,"abstract":"The premise of personalized medicine is that patients exhibit significant heterogeneity and respond differently to treatments. The best treatment for one patient may not be suitable for another. In this paper, we propose a Bayesian adaptive design for an avatar-driven clinical trial, using mouse or laboratory animal grafts to create personalized tumor models that guide individualized patient treatment while accounting for translational discrepancy between avatars and patients. We jointly model the tumor growth of avatars and the clinical outcomes of patients, including toxicity and progression-free survival, to capture the interplay between avatars and their source patients. This joint model enables us to leverage both avatar treatment responses and patient data to predict the treatment utility for each patient and inform the selection of personalized optimal treatments, thereby serving as a digital twin of the patient. To address the fact that avatars may only serve as valid surrogates for treatment effects in a subset of patients, we incorporate a latent-class model that accounts for this translational discrepancy. Through simulation studies, we demonstrate that the proposed design outperforms conventional clinical trials lacking avatar information. Under this design, patients are significantly more likely to receive personalized optimal treatments, leading to improved treatment outcomes.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149057444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian network meta-regression models for multivariate aggregate responses with partially observed or completely missing within-treatment sample covariance matrices. 处理样本协方差矩阵部分观察或完全缺失的多变量聚合响应的贝叶斯网络元回归模型。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag144

Simiao Gao, Sungduk Kim, Ming-Hui Chen, Arvind K Shah, Jianxin Lin, Joseph G Ibrahim

{"title":"Bayesian network meta-regression models for multivariate aggregate responses with partially observed or completely missing within-treatment sample covariance matrices.","authors":"Simiao Gao, Sungduk Kim, Ming-Hui Chen, Arvind K Shah, Jianxin Lin, Joseph G Ibrahim","doi":"10.1093/biomtc/ujag144","DOIUrl":"https://doi.org/10.1093/biomtc/ujag144","url":null,"abstract":"In this paper, we propose a Bayesian multivariate network meta-regression model to compare multiple treatments used to treat cardiovascular and diabetes diseases, where the multivariate aggregate outcomes include low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides. We assume a log-linear regression model for the standard deviation of the treatment random effects to overcome the difficulty that some treatments may present only in a single study. As the within-study sample covariance matrix $boldsymbol {S}$ is partially observed or completely missing and the within-study sample correlations are not observed at all, we postulate a hierarchical structure on the unknown within-study covariance matrices. We further develop a Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution and a Monte Carlo procedure to rank the treatment effects for the multivariate outcomes. DIC is used for model comparison. Two variations of DIC are further developed to quantify (i) the overall improvement in the fit and (ii) the gain in the fit of each outcome due to the multivariate model versus the univariate model alone. A detailed analysis of the aggregate data from real randomized controlled trials is carried out to further demonstrate the proposed methodology.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148863550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast penalized generalized estimating equations for large longitudinal functional datasets. 大型纵向函数数据集的快速惩罚广义估计方程。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag106

Gabriel Loewinger, Alexander W Levis, Erjia Cui, Francisco Pereira

{"title":"Fast penalized generalized estimating equations for large longitudinal functional datasets.","authors":"Gabriel Loewinger, Alexander W Levis, Erjia Cui, Francisco Pereira","doi":"10.1093/biomtc/ujag106","DOIUrl":"10.1093/biomtc/ujag106","url":null,"abstract":"Longitudinal binary or count functional data are common in neuroscience, but are often too large to analyze with existing functional regression methods. We propose one-step penalized generalized estimating equations that support generalized functional outcomes (e.g., count, binary, proportion, continuous-valued) and is fast even when datasets have a large number of clusters and large cluster sizes. The method applies to functional and scalar covariates and the one-step estimation framework enables efficient smoothing parameter selection and joint confidence interval construction. Importantly, this semi-parametric approach yields coefficient confidence intervals that are provably valid asymptotically even under working correlation misspecification. By developing a general theory for adaptive one-step M-estimation, we prove that the coefficient estimates are asymptotically normal and as efficient as the fully-iterated estimator; we verify these theoretical properties in simulations. We illustrate the benefits of our approach for analyzing large-scale neural recordings by applying it to a recent calcium imaging dataset published in Nature. We show that our method reveals important timing effects obscured in non-functional analyses. In doing so, we also demonstrate scaling to common neuroscience dataset sizes: the one-step estimator fits to a dataset with 150,000 (binary) functional outcomes, each observed at 120 functional domain points, in only $sim 6.5$ minutes on a laptop without parallelization. We release our methods in the $tt {R}$ package $tt {fastFGEE}$, which supports a wide range of link functions and working covariances.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148359202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributionally balanced sampling designs. 分布平衡抽样设计。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag124

Anton Grafström, Wilmer Prentius

{"title":"Distributionally balanced sampling designs.","authors":"Anton Grafström, Wilmer Prentius","doi":"10.1093/biomtc/ujag124","DOIUrl":"https://doi.org/10.1093/biomtc/ujag124","url":null,"abstract":"We propose Distributionally Balanced Designs (DBD), a new class of probability sampling designs that target representativeness at the level of the full auxiliary distribution rather than for selected moments. In disciplines such as ecology, forestry, and environmental sciences, where field data collection is expensive, maximizing the information extracted from a limited sample is critical. More precisely, DBD can be viewed as minimum discrepancy designs that minimize the expected discrepancy between the sample and population auxiliary distributions. The key idea is to construct samples whose empirical auxiliary distribution closely matches that of the population. We present a first implementation of DBD for equal inclusion probabilities, based on an optimized circular ordering of the population and a random selection of a contiguous block of units. The ordering is chosen to minimize the design-expected energy distance, a discrepancy measure that captures differences between distributions beyond low-order moments. This criterion promotes strong spatial spread, and yields low variance for Horvitz-Thompson estimators of totals of functions that vary smoothly with respect to auxiliaries. Simulation results show that approximate DBD achieves better distributional fit than state-of-the-art methods such as the local pivotal and local cube designs. Hence, DBD can improve the reliability of estimates from costly field data, making distributional balancing effective for constructing representative surveys in resource-constrained applications.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bias reduction in g-computation for covariate adjustment in randomized clinical trials. 随机临床试验中协变量调整的g计算偏倚减少。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag129

Xin Zhang, Lin Liu, Haitao Chu

{"title":"Bias reduction in g-computation for covariate adjustment in randomized clinical trials.","authors":"Xin Zhang, Lin Liu, Haitao Chu","doi":"10.1093/biomtc/ujag129","DOIUrl":"https://doi.org/10.1093/biomtc/ujag129","url":null,"abstract":"G-computation is a powerful covariate adjustment method for analyzing randomized clinical trials. It typically relies on fitting canonical generalized linear models. However, this could be problematic when the sample size or event number is small relative to the number of covariates. Common issues include the underestimation of the variance and the potential nonexistence of maximum likelihood estimators. Bias reduction methods are commonly employed to address these issues, including Firth correction, which guarantees the existence of corresponding estimates. Yet, their application within g-computation remains underexplored. In this article, we analyze the asymptotic bias of g-computation estimators and propose a novel bias-reduction method that improves both estimation and inference. Our approach performs bias correction via generalized Oaxaca-Blinder estimators, and thus the resulting estimators are guaranteed to be bounded. The proposed debiased estimators use slightly modified versions of maximum likelihood or Firth correction estimators for nuisance parameters. We also introduce a simple small-sample bias adjustment for variance estimation to improve finite-sample inference validity. Through extensive simulations, we demonstrate that our method offers superior finite-sample performance, effectively addressing the bias-efficiency tradeoff. Finally, we illustrate its practical utility by reanalyzing a completed randomized clinical trial. In this example, our method improves precision in a small subgroup analysis for which the standard method fails to fit the regression model.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148429804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blinded sample size recalculation in randomized controlled trials with analysis of covariance. 随机对照试验的盲法样本量重新计算及协方差分析。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag147

Takumi Kanata, Yasuhiro Hagiwara, Koji Oba

{"title":"Blinded sample size recalculation in randomized controlled trials with analysis of covariance.","authors":"Takumi Kanata, Yasuhiro Hagiwara, Koji Oba","doi":"10.1093/biomtc/ujag147","DOIUrl":"https://doi.org/10.1093/biomtc/ujag147","url":null,"abstract":"In randomized controlled trials, covariate adjustment can improve statistical power and reduce the required sample size compared with unadjusted estimators. Analysis of covariance (ANCOVA) is often used to adjust for baseline covariates when outcomes are continuous. To design a sample size based on ANCOVA, it is necessary to prespecify the association between the outcome and baseline covariates, as well as among the baseline covariates themselves. However, determining these parameters at the design stage is challenging. Although it may be possible to adaptively assess these during the trial and recalculate the required sample size, existing sample size recalculation methods assume that the joint distribution of the outcome and baseline covariates is multivariate normal, which is not always the case in practice. In this study, we propose a blinded sample size recalculation method for the ANCOVA estimator based on the asymptotic relative efficiency under minimal distributional assumptions, thus accommodating arbitrary model misspecification. The proposed method is able to achieve the nominal power and reduce the required sample size without inflating the type I error rate in the final analysis. We conducted simulations to evaluate the performance of the proposed method under various scenarios. Applied to an HIV clinical trial, the proposed method reduced the required sample size by up to one-third compared to the unadjusted analysis, despite occasional slight increases.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148787768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Doubly robust proximal causal inference under confounded outcome-dependent sampling. 混杂结果依赖抽样下的双稳健近端因果推理。

IF 1.6 4区数学

Biometrics Pub Date : 2026-07-01 DOI: 10.1093/biomtc/ujag157

Kendrick Li, Xu Shi, Wang Miao, Eric Tchetgen Tchetgen

{"title":"Doubly robust proximal causal inference under confounded outcome-dependent sampling.","authors":"Kendrick Li, Xu Shi, Wang Miao, Eric Tchetgen Tchetgen","doi":"10.1093/biomtc/ujag157","DOIUrl":"https://doi.org/10.1093/biomtc/ujag157","url":null,"abstract":"Outcome-dependent sampling is widely used in epidemiological and socioeconomic research to efficiently and economically study the treatment-outcome relationship. However, skepticism about unmeasured confounding bias and selection bias poses challenges for causal inference. In particular, a hidden common cause of the treatment, outcome, and sample selection process will generally induce both (a) unmeasured confounding, and (b) selection bias, thus rendering standard causal parameters unidentifiable without additional assumptions. In this article, we review and extend a recently proposed proximal causal inference approach for test-negative design studies to general outcome-dependent sampling designs, which leverages a pair of proxies of hidden factors at the source of unmeasured confounding and selection bias to jointly correct for both biases. Our contributions include: (i) a new set of conditions for the proximal identification of a causal odds ratio parameter when (a) and (b) co-exist; and (ii) a characterization of new proximal doubly robust estimators for the odds ratio effect under (a) and (b). We illustrate the performance of our estimators through extensive simulations and data from an application to University of Michigan Health System.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 3","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148987787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0