Biometrics最新文献_第4页

Statistical inference on change points in generalized semiparametric segmented models.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf022

Guangyu Yang, Baqun Zhang, Min Zhang

{"title":"Statistical inference on change points in generalized semiparametric segmented models.","authors":"Guangyu Yang, Baqun Zhang, Min Zhang","doi":"10.1093/biomtc/ujaf022","DOIUrl":"10.1093/biomtc/ujaf022","url":null,"abstract":"The segmented model has significant applications in scientific research when the change-point effect exists. In this article, we propose a comprehensive semiparametric framework in segmented models to test the existence and estimate the location of change points in the generalized outcome setting. The proposed framework is based on a semismooth estimating equation for the change-point estimation and an average score-type test for hypothesis testing. The root-n consistency, asymptotic normality, and asymptotic efficiency of estimators for all parameters in the segmented model are rigorously studied. The distribution of the average score-type test statistics under the null hypothesis is rigorously derived. Extensive simulation studies are conducted to assess the numerical performance of the proposed change-point estimation method and the average score-type test. We investigate change-point effects of baseline glomerular filtration rate and body mass index on bleeding after intervention using data from Blue Cross Blue Shield. This application study successfully identifies statistically significant change-point effects, with the estimated values providing clinically meaningful insights.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603115","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

Estimating hypothetical estimands with causal inference and missing data estimators in a diabetes trial case study.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae167

Camila Olarte Parra, Rhian M Daniel, David Wright, Jonathan W Bartlett

{"title":"Estimating hypothetical estimands with causal inference and missing data estimators in a diabetes trial case study.","authors":"Camila Olarte Parra, Rhian M Daniel, David Wright, Jonathan W Bartlett","doi":"10.1093/biomtc/ujae167","DOIUrl":"https://doi.org/10.1093/biomtc/ujae167","url":null,"abstract":"The ICH E9 addendum on estimands in clinical trials provides a framework for precisely defining the treatment effect that is to be estimated, but says little about estimation methods. Here, we report analyses of a clinical trial in type 2 diabetes, targeting the effects of randomized treatment, handling rescue treatment and discontinuation of randomized treatment using the so-called hypothetical strategy. We show how this can be estimated using mixed models for repeated measures, multiple imputation, inverse probability of treatment weighting, G-formula, and G-estimation. We describe their assumptions and practical details of their implementation using packages in R. We report the results of these analyses, broadly finding similar estimates and standard errors across the estimators. We discuss various considerations relevant when choosing an estimation approach, including computational time, how to handle missing data, whether to include post intercurrent event data in the analysis, whether and how to adjust for additional time-varying confounders, and whether and how to model different types of intercurrent event data separately.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051435","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 nonparametric trees for principal causal effects.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf024

Chanmin Kim, Corwin Zigler

{"title":"Bayesian nonparametric trees for principal causal effects.","authors":"Chanmin Kim, Corwin Zigler","doi":"10.1093/biomtc/ujaf024","DOIUrl":"10.1093/biomtc/ujaf024","url":null,"abstract":"Principal stratification analysis evaluates how causal effects of a treatment on a primary outcome vary across strata of units defined by their treatment effect on some intermediate quantity. This endeavor is substantially challenged when the intermediate variable is continuously scaled and there are infinitely many basic principal strata. We employ a Bayesian nonparametric approach to flexibly evaluate treatment effects across flexibly modeled principal strata. The approach uses Bayesian Causal Forests (BCF) to simultaneously specify 2 Bayesian Additive Regression Tree models; one for the principal stratum membership and one for the outcome, conditional on principal strata. We show how the capability of BCF for capturing treatment effect heterogeneity is particularly relevant for assessing how treatment effects vary across the surface defined by continuously scaled principal strata, in addition to other benefits relating to targeted selection and regularization-induced confounding. The capabilities of the proposed approach are illustrated with a simulation study, and the methodology is deployed to investigate how causal effects of power plant emissions control technologies on ambient particulate pollution vary as a function of the technologies' impact on sulfur dioxide emissions.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11911721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647144","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 unified combination framework for dependent tests with applications to microbiome association studies.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf001

Xiufan Yu, Linjun Zhang, Arun Srinivasan, Min-Ge Xie, Lingzhou Xue

{"title":"A unified combination framework for dependent tests with applications to microbiome association studies.","authors":"Xiufan Yu, Linjun Zhang, Arun Srinivasan, Min-Ge Xie, Lingzhou Xue","doi":"10.1093/biomtc/ujaf001","DOIUrl":"10.1093/biomtc/ujaf001","url":null,"abstract":"We introduce a novel meta-analysis framework to combine dependent tests under a general setting, and utilize it to synthesize various microbiome association tests that are calculated from the same dataset. Our development builds upon the classical meta-analysis methods of aggregating P-values and also a more recent general method of combining confidence distributions, but makes generalizations to handle dependent tests. The proposed framework ensures rigorous statistical guarantees, and we provide a comprehensive study and compare it with various existing dependent combination methods. Notably, we demonstrate that the widely used Cauchy combination method for dependent tests, referred to as the vanilla Cauchy combination in this article, can be viewed as a special case within our framework. Moreover, the proposed framework provides a way to address the problem when the distributional assumptions underlying the vanilla Cauchy combination are violated. Our numerical results demonstrate that ignoring the dependence among the to-be-combined components may lead to a severe size distortion phenomenon. Compared to the existing P-value combination methods, including the vanilla Cauchy combination method and other methods, the proposed combination framework is flexible and can be adapted to handle the dependence accurately and utilizes the information efficiently to construct tests with accurate size and enhanced power. The development is applied to the microbiome association studies, where we aggregate information from multiple existing tests using the same dataset. The combined tests harness the strengths of each individual test across a wide range of alternative spaces, enabling more efficient and meaningful discoveries of vital microbiome associations.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143063363","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

Positive-definite regularized estimation for high-dimensional covariance on scalar regression.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf017

Jie He, Yumou Qiu, Xiao-Hua Zhou

{"title":"Positive-definite regularized estimation for high-dimensional covariance on scalar regression.","authors":"Jie He, Yumou Qiu, Xiao-Hua Zhou","doi":"10.1093/biomtc/ujaf017","DOIUrl":"10.1093/biomtc/ujaf017","url":null,"abstract":"Covariance is an important measure of marginal dependence among variables. However, heterogeneity in subject covariances and regression models for high-dimensional covariance matrices is not well studied. Compared to regression analysis for conditional means, modeling high-dimensional covariances is much more challenging due to the large set of free parameters and the intrinsic positive-definite property that puts constraints on the regression parameters. In this paper, we propose a regularized estimation method for the regression coefficients of covariances under sufficient and necessary constraints for the positive definiteness of the conditional average covariance matrices given covariates. The proposed estimator satisfies the sparsity and positive-definite properties simultaneously. An alternating direction method of multipliers (ADMM) algorithm is proposed to solve the constrained and regularized optimization problem. We show the convergence of the proposed ADMM algorithm and derive the convergence rates of the proposed estimators for the regression coefficients and the heterogeneous covariances. The proposed method is evaluated by simulation studies, and its practical application is demonstrated by a case study on brain connectivity.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143582171","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

Combining covariate adjustment with group sequential, information-adaptive designs to improve randomized trial efficiency.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf020

Kelly Van Lancker, Joshua F Betz, Michael Rosenblum

{"title":"Combining covariate adjustment with group sequential, information-adaptive designs to improve randomized trial efficiency.","authors":"Kelly Van Lancker, Joshua F Betz, Michael Rosenblum","doi":"10.1093/biomtc/ujaf020","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf020","url":null,"abstract":"Group sequential designs (GSDs), which involve preplanned interim analyses that allow for early stopping for efficacy or futility, are commonly used for ethical and efficiency reasons. Covariate adjustment, which involves appropriately adjusting for prespecified prognostic baseline variables, can improve precision and is generally recommended by regulators. Combining these, that is, using adjusted estimators at interim and final analyses of a GSD, has potential for dual benefits. We address 2 challenges involved in combining these methods. First, adjusted estimators may lack the independent increments structure (asymptotically) that is required to directly apply standard stopping boundaries for GSDs. We address this by applying a linear transformation to the sequence of adjusted estimators across analysis times, resulting in a new sequence of consistent, asymptotically normal estimators with the independent increments property that either improves or leaves precision unchanged. This approach generalizes foundational results on GSDs with semiparametric efficient estimators to any sequence of regular, asymptotically linear estimators. Second, we address the practical problem of handling uncertainty about how much (if any) precision gain will result from covariate adjustment. This is important for trial planning, since an incorrect projection of a covariate's prognostic value risks an over- or underpowered trial. We propose using information-adaptive designs, that is, continuing the trial until the required information level is achieved. This design enables faster, more efficient trials, without sacrificing validity or power.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596007","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 regularized Bayesian Dirichlet-multinomial regression model for integrating single-cell-level omics and patient-level clinical study data.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf005

Yanghong Guo, Lei Yu, Lei Guo, Lin Xu, Qiwei Li

{"title":"A regularized Bayesian Dirichlet-multinomial regression model for integrating single-cell-level omics and patient-level clinical study data.","authors":"Yanghong Guo, Lei Yu, Lei Guo, Lin Xu, Qiwei Li","doi":"10.1093/biomtc/ujaf005","DOIUrl":"10.1093/biomtc/ujaf005","url":null,"abstract":"The abundance of various cell types can vary significantly among patients with varying phenotypes and even those with the same phenotype. Recent scientific advancements provide mounting evidence that other clinical variables, such as age, gender, and lifestyle habits, can also influence the abundance of certain cell types. However, current methods for integrating single-cell-level omics data with clinical variables are inadequate. In this study, we propose a regularized Bayesian Dirichlet-multinomial regression framework to investigate the relationship between single-cell RNA sequencing data and patient-level clinical data. Additionally, the model employs a novel hierarchical tree structure to identify such relationships at different cell-type levels. Our model successfully uncovers significant associations between specific cell types and clinical variables across three distinct diseases: pulmonary fibrosis, COVID-19, and non-small cell lung cancer. This integrative analysis provides biological insights and could potentially inform clinical interventions for various diseases.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143063282","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

Individualized multi-treatment response curves estimation using RBF-net with shared neurons.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf019

Peter Chang, Arkaprava Roy

引用次数: 0

Combining experimental and observational data through a power likelihood.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf008

Xi Lin, Jens Magelund Tarp, Robin J Evans

{"title":"Combining experimental and observational data through a power likelihood.","authors":"Xi Lin, Jens Magelund Tarp, Robin J Evans","doi":"10.1093/biomtc/ujaf008","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf008","url":null,"abstract":"Randomized controlled trials are the gold standard for causal inference and play a pivotal role in modern evidence-based medicine. However, the sample sizes they use are often too limited to provide adequate power for drawing causal conclusions. In contrast, observational data are becoming increasingly accessible in large volumes but can be subject to bias as a result of hidden confounding. Given these complementary features, we propose a power likelihood approach to augmenting randomized controlled trials with observational data to improve the efficiency of treatment effect estimation. We provide a data-adaptive procedure for maximizing the expected log predictive density (ELPD) to select the learning rate that best regulates the information from the observational data. We validate our method through a simulation study that shows increased power while maintaining an approximate nominal coverage rate. Finally, we apply our method in a real-world data fusion study augmenting the PIONEER 6 clinical trial with a US health claims dataset, demonstrating the effectiveness of our method and providing detailed guidance on how to address practical considerations in its application.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143432300","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

Multiply robust difference-in-differences estimation of causal effect curves for continuous exposures.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf015

Gary Hettinger, Youjin Lee, Nandita Mitra

{"title":"Multiply robust difference-in-differences estimation of causal effect curves for continuous exposures.","authors":"Gary Hettinger, Youjin Lee, Nandita Mitra","doi":"10.1093/biomtc/ujaf015","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf015","url":null,"abstract":"Researchers commonly use difference-in-differences (DiD) designs to evaluate public policy interventions. While methods exist for estimating effects in the context of binary interventions, policies often result in varied exposures across regions implementing the policy. Yet, existing approaches for incorporating continuous exposures face substantial limitations in addressing confounding variables associated with intervention status, exposure levels, and outcome trends. These limitations significantly constrain policymakers' ability to fully comprehend policy impacts and design future interventions. In this work, we propose new estimators for causal effect curves within the DiD framework, accounting for multiple sources of confounding. Our approach accommodates misspecification of a subset of intervention, exposure, and outcome models while avoiding any parametric assumptions on the effect curve. We present the statistical properties of the proposed methods and illustrate their application through simulations and a study investigating the heterogeneous effects of a nutritional excise tax under different levels of accessibility to cross-border shopping.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482119","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