IF 1.7 4区数学

Biometrics Pub Date : 2026-05-05 DOI: 10.1093/biomtc/ujag069

Susanne Dandl, Torsten Hothorn

引用次数: 0

Nonparanormal adjusted marginal inference. 非超自然调整边际推断。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-29 DOI: 10.1093/biomtc/ujag065

Susanne Dandl, Torsten Hothorn

{"title":"Nonparanormal adjusted marginal inference.","authors":"Susanne Dandl, Torsten Hothorn","doi":"10.1093/biomtc/ujag065","DOIUrl":"https://doi.org/10.1093/biomtc/ujag065","url":null,"abstract":"Although treatment effects can be estimated from observed outcome distributions obtained from proper randomization in clinical trials, covariate adjustment is recommended to increase precision. For important treatment effects, such as odds or hazard ratios, conditioning on covariates in binary logistic or proportional hazards models changes the interpretation of the treatment effect, and conditioning on different sets of covariates renders the resulting effect estimates incomparable. We propose a novel nonparanormal model formulation for adjusted marginal inference. This model for the joint distribution of outcome and covariates directly features a marginally defined treatment effect parameter, such as a marginal odds or hazard ratio. Not only the marginal treatment effect of interest can be estimated based on this model, it also provides an overall coefficient of determination and covariate-specific measures of prognostic strength. For the special case of Cohen's standardized mean difference d, we theoretically show that adjusting for an informative prognostic variable improves the precision of the marginal, noncollapsible effect. Empirical results confirm this not only for Cohen's d but also for odds and hazard ratios in simulations and four applications. A reference implementation is available in the R add-on package tram.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147761408","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

Discussion on ''Nonparanormal Adjusted Marginal Inference'' by Susanne Dandl and Torsten Hothorn. 论Susanne Dandl和Torsten Hothorn的“非超自然调整边际推论”。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-29 DOI: 10.1093/biomtc/ujag066

Shirin Golchi

引用次数: 0

Discussion on ''Nonparanormal Adjusted Marginal Inference'' by Susanne Dandl and Torsten Hothorn. 论Susanne Dandl和Torsten Hothorn的“非超自然调整边际推论”。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-29 DOI: 10.1093/biomtc/ujag067

Kelly Van Lancker, Oliver Dukes

引用次数: 0

Measurement error-robust causal inference via constructed instrumental variables. 测量误差-通过构造的工具变量进行稳健的因果推理。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag057

Caleb H Miles, Linda Valeri, Brent Coull

{"title":"Measurement error-robust causal inference via constructed instrumental variables.","authors":"Caleb H Miles, Linda Valeri, Brent Coull","doi":"10.1093/biomtc/ujag057","DOIUrl":"https://doi.org/10.1093/biomtc/ujag057","url":null,"abstract":"Measurement error can often be harmful when estimating causal effects. Two scenarios in which this is the case are in the estimation of (a) the average treatment effect when confounders are measured with error, and (b) the natural indirect effect when the exposure and/or confounders are measured with error. Methods adjusting for measurement error typically require external data or knowledge about the measurement error distribution. Here, we propose methodology not requiring any such information. Instead, we show that when the outcome regression is linear in the error-prone variables, consistent estimation of these causal effects can be recovered using constructed instrumental variables (IVs) under certain conditions. These variables, which are functions of only the observed data, behave like IVs for the error-prone variables. Using data from a study of the effects of prenatal exposure to heavy metals on growth and neurodevelopment in Bangladeshi mother-infant pairs, we apply our methodology to estimate (a) the effect of lead exposure on birth length while controlling for maternal protein intake, and (b) lead exposure's role in mediating the effect of maternal protein intake on birth length. Protein intake is calculated from food journal entries, and is suspected to be highly prone to measurement error.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697423","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

Two-phase designs for cost-effective evaluation of cancer screening tests. 两阶段设计的成本效益评估癌症筛查试验。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag058

Fangya Mao, Richard J Cook, Thomas Lorey, Nicolas Wentzensen, Li C Cheung

{"title":"Two-phase designs for cost-effective evaluation of cancer screening tests.","authors":"Fangya Mao, Richard J Cook, Thomas Lorey, Nicolas Wentzensen, Li C Cheung","doi":"10.1093/biomtc/ujag058","DOIUrl":"https://doi.org/10.1093/biomtc/ujag058","url":null,"abstract":"Screening tests are crucial for detecting diseases at preclinical stages when timely intervention can prevent progression to more severe conditions. Advances in technology have facilitated developing screening tests based on novel markers, but evaluating their performance using biospecimens from large cohorts remains logistically complex and financially demanding. Two-phase designs offer a cost-effective solution by allowing inference when expensive marker measurements are collected on only a carefully selected subsample. While traditional two-phase designs have primarily targeted estimation of marker-outcome associations, they can effectively be extended to evaluate the clinical performance of a test, including estimation of positive predictive value (the risk in test positives) and complementary negative predictive value (the risk in test negatives). We propose a novel two-phase design for efficiently evaluating the risk-stratification utility of screening tests in distinguishing between high- and low-risk individuals for both current and future disease. Designed for use with biospecimens collected from screening studies, our methodology accommodates cohorts that include both pre-existing cases at an initial screening visit and new cases identified during follow-up. We demonstrate the efficiency gains of our proposed design compared to other subsampling schemes through simulation and illustrate its application in a motivating study evaluating the p16/ki-67 dual-stain test for managing human papillomavirus-positive women in cervical cancer screening. Data and stored biospecimen samples from Kaiser Permanente Northern California are used in this analysis.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13112528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147760699","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

A novel exact confidence interval for the difference of proportions in paired data using a restricted most probable statistic. 用限制最可能统计量对成对数据的比例差提出了一种新的精确置信区间。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag061

Xingyun Cao, Weizhen Wang, Tianfa Xie

{"title":"A novel exact confidence interval for the difference of proportions in paired data using a restricted most probable statistic.","authors":"Xingyun Cao, Weizhen Wang, Tianfa Xie","doi":"10.1093/biomtc/ujag061","DOIUrl":"https://doi.org/10.1093/biomtc/ujag061","url":null,"abstract":"Inference on the difference between two proportions in paired data is a key issue, particularly in biomedical research and clinical trials. Numerous methods exist for constructing confidence intervals for this difference. However, approximate methods that rely on asymptotic normality can be unreliable, underscoring the need for exact confidence intervals to improve reliability. In this paper, we develop a novel interval based on the restricted most probable method, which is further optimized using the h-function method to yield an optimal exact interval, ensuring both reliability and precision. We compare the proposed interval with other exact intervals developed through methodologies such as the score method, two Tang methods, the Wang method, the adjusted Wald method, and the score method with continuity correction. Our comparative analysis, utilizing the infimum coverage probability and total interval length as evaluation metrics, demonstrates the uniformly superior performance of the proposed interval. Additionally, an example illustrates its practical application in real-world scenarios. Supplementary Materials provide another example, numerical results on coverage and non-coverage probabilities, and R code.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147761413","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

Personalized treatment design in the context of functional confounding. 功能混淆背景下的个性化治疗设计。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag056

Zhixian Yang, Peijun Sang, Yixin Han, Bei Jiang, Linglong Kong, Xingcai Zhou

引用次数: 0

Bayesian adaptive randomization in the I-SPY2 sequential multiple assignment randomized trial. I-SPY2顺序多任务随机试验中的贝叶斯自适应随机化。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag063

Peter Norwood, Christina Yau, Denise Wolf, Philip Beineke, Andrew Chapple, Anastasios Tsiatis, Marie Davidian

{"title":"Bayesian adaptive randomization in the I-SPY2 sequential multiple assignment randomized trial.","authors":"Peter Norwood, Christina Yau, Denise Wolf, Philip Beineke, Andrew Chapple, Anastasios Tsiatis, Marie Davidian","doi":"10.1093/biomtc/ujag063","DOIUrl":"10.1093/biomtc/ujag063","url":null,"abstract":"I-SPY2 is a long-running phase 2 platform trial that evaluates neoadjuvant treatments for locally advanced breast cancer to identify those with high efficacy that are likely to be successful in phase 3 trials, assigning patients to novel agents using response-adaptive randomization (RAR). Recently, I-SPY2 was reconfigured as a sequential multiple assignment randomized trial (SMART), with up to three stages of therapy. At the first stage, a patient is assigned to a tumor-subtype-specific therapy. If the patient fails to show a satisfactory response, the patient is assigned to a second subtype-specific therapy, and receives a third, rescue therapy if response is still not achieved. The I-SPY2 SMART thus supports identification of highly efficacious entire treatment regimes. The transition of I-SPY2 to a SMART required development of a RAR scheme that updates randomization probabilities at each stage, aligned with the goal of maximizing the number of patients who achieve a pathological complete response (pCR). We present our Bayesian RAR approach, which updates randomization probabilities based on the posterior probability that treatments are part of the optimal regime. Empirical studies demonstrate that it results in more patients having treatment experience consistent with highly efficacious regimes, improves overall within-trial pCR rates, and identifies optimal regimes post trial at rates similar to or exceeding those under simple, uniform, nonadaptive randomization.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13126647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147760532","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

Bayesian shrinkage priors for penalized synthetic control estimators in the presence of spillovers. 存在溢出时惩罚综合控制估计器的贝叶斯收缩先验。

IF 1.7 4区数学

Biometrics Pub Date : 2026-04-09 DOI: 10.1093/biomtc/ujag054

Esteban Fernández-Morales, Arman Oganisian, Youjin Lee

{"title":"Bayesian shrinkage priors for penalized synthetic control estimators in the presence of spillovers.","authors":"Esteban Fernández-Morales, Arman Oganisian, Youjin Lee","doi":"10.1093/biomtc/ujag054","DOIUrl":"10.1093/biomtc/ujag054","url":null,"abstract":"Synthetic control (SC) methods are widely used to estimate the effects of policy interventions, especially those targeting specific geographic regions, referred to as units. These methods construct a weighted combination of untreated units, forming a \"synthetic\" control that approximates the counterfactual outcomes of the treated unit had the intervention not occurred. Although neighboring areas are often selected as controls due to their similarity in observed and unobserved characteristics, their proximity can lead to spillover effects, where the intervention indirectly impacts control units, potentially biasing causal estimates. To address this challenge, we introduce a Bayesian SC framework with utility-based shrinkage priors. Our approach extends traditional penalization techniques (i.e., horseshoe, spike-and-slab) by incorporating a utility function that combines covariate similarity and spatial distance. This provides a metric that guides the data-driven selection of control units based on their relevance and spillover risk, which is assumed to increase with spatial proximity. Rather than outright excluding neighboring units, the method balances bias and variance by reducing the importance of potentially contaminated controls by spillovers. We evaluate the proposed method through simulation studies at varying spillover levels and apply it to assess the impact of Philadelphia's 2017 beverage tax on the sales of sugar-sweetened and artificially sweetened beverages in mass merchandise stores.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"82 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13097100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697368","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

Biometrics最新文献