Biometrics最新文献_第2页

Pseudo-observations for bivariate survival data.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf006

Yael Travis-Lumer, Micha Mandel, Rebecca A Betensky

{"title":"Pseudo-observations for bivariate survival data.","authors":"Yael Travis-Lumer, Micha Mandel, Rebecca A Betensky","doi":"10.1093/biomtc/ujaf006","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf006","url":null,"abstract":"The pseudo-observations approach has been gaining popularity as a method to estimate covariate effects on censored survival data. It is used regularly to estimate covariate effects on quantities such as survival probabilities, restricted mean life, cumulative incidence, and others. In this work, we propose to generalize the pseudo-observations approach to situations where a bivariate failure-time variable is observed, subject to right censoring. The idea is to first estimate the joint survival function of both failure times and then use it to define the relevant pseudo-observations. Once the pseudo-observations are calculated, they are used as the response in a generalized linear model. We consider 2 common nonparametric estimators of the joint survival function: the estimator of Lin and Ying (1993) and the Dabrowska estimator (Dabrowska, 1988). For both estimators, we show that our bivariate pseudo-observations approach produces regression estimates that are consistent and asymptotically normal. Our proposed method enables estimation of covariate effects on quantities such as the joint survival probability at a fixed bivariate time point or simultaneously at several time points and, consequentially, can estimate covariate-adjusted conditional survival probabilities. We demonstrate the method using simulations and an analysis of 2 real-world datasets.","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":"143188046","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

Feature screening for metric space-valued responses based on Fréchet regression with its applications.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf007

Bing Tian, Jian Kang, Wei Zhong

{"title":"Feature screening for metric space-valued responses based on Fréchet regression with its applications.","authors":"Bing Tian, Jian Kang, Wei Zhong","doi":"10.1093/biomtc/ujaf007","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf007","url":null,"abstract":"In various applications, we need to handle more general types of responses, such as distributional data and matrix-valued data, rather than a scalar variable. When the dimension of predictors is ultrahigh, it is necessarily important to identify the relevant predictors for such complex types of responses. For example, in our Alzheimer's disease neuroimaging study, we need to select the relevant single nucleotide polymorphisms out of 582 591 candidates for the distribution of voxel-level intensities in each of 42 brain regions. To this end, we propose a new sure independence screening (SIS) procedure for general metric space-valued responses based on global Fréchet regression, termed as Fréchet-SIS. The marginal general residual sum of squares is utilized to serve as a marginal utility for evaluating the importance of predictors, where only a distance between data objects is needed. We theoretically show that the proposed Fréchet-SIS procedure enjoys the sure screening property under mild regularity conditions. Monte Carlo simulations are conducted to demonstrate its excellent finite-sample performance. In Alzheimer's disease neuroimaging study, we identify important genes that correlate with brain activity across different stages of the disease and brain regions. In addition, we also include an economic case study to illustrate our proposal.","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":"143397821","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

Distributed lag models for retrospective cohort data with application to a study of built environment and body weight.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae166

Jennifer F Bobb, Stephen J Mooney, Maricela Cruz, Anne Vernez Moudon, Adam Drewnowski, David Arterburn, Andrea J Cook

{"title":"Distributed lag models for retrospective cohort data with application to a study of built environment and body weight.","authors":"Jennifer F Bobb, Stephen J Mooney, Maricela Cruz, Anne Vernez Moudon, Adam Drewnowski, David Arterburn, Andrea J Cook","doi":"10.1093/biomtc/ujae166","DOIUrl":"10.1093/biomtc/ujae166","url":null,"abstract":"Distributed lag models (DLMs) estimate the health effects of exposure over multiple time lags prior to the outcome and are widely used in time series studies. Applying DLMs to retrospective cohort studies is challenging due to inconsistent lengths of exposure history across participants, which is common when using electronic health record databases. A standard approach is to define subcohorts of individuals with some minimum exposure history, but this limits power and may amplify selection bias. We propose alternative full-cohort methods that use all available data while simultaneously enabling examination of the longest time lag estimable in the cohort. Through simulation studies, we find that restricting to a subcohort can lead to biased estimates of exposure effects due to confounding by correlated exposures at more distant lags. By contrast, full-cohort methods that incorporate multiple imputation of complete exposure histories can avoid this bias to efficiently estimate lagged and cumulative effects. Applying full-cohort DLMs to a study examining the association between residential density (a proxy for walkability) over 12 years and body weight, we find evidence of an immediate effect in the prior 1-2 years. We also observed an association at the maximal lag considered (12 years prior), which we posit reflects an earlier ($ge$12 years) or incrementally increasing prior effect over time. DLMs can be efficiently incorporated within retrospective cohort studies to identify critical windows of exposure.","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/PMC11760659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031922","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

Instrumental variable estimation of complier casual treatment effects with interval-censored competing risks data.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf010

Yichen Lou, Yuqing Ma, Jianguo Sun, Peijie Wang, Zhisheng Ye

{"title":"Instrumental variable estimation of complier casual treatment effects with interval-censored competing risks data.","authors":"Yichen Lou, Yuqing Ma, Jianguo Sun, Peijie Wang, Zhisheng Ye","doi":"10.1093/biomtc/ujaf010","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf010","url":null,"abstract":"This paper discusses the assessment of causal treatment effects on a time-to-event outcome, a crucial part of many scientific investigations. Although some methods have been developed for the problem, they are not applicable to situations where there exist both interval censoring and competing risks. We fill in this critical gap under a class of transformation models for cumulative incidence functions by developing an instrumented variable (IV) estimation approach. The IV is a valuable tool commonly used to mitigate the impact of endogenous treatment selection and to determine causal treatment effects in an unbiased manner. The proposed method is flexible as the model includes many commonly used models such as the sub-distributional proportional odds and hazards models (ie, the Fine-Gray model) as special cases. The resulting estimator for the regression parameter is shown to be consistent and asymptotically normal. A simulation study is conducted to evaluate finite sample performance of the proposed approach and suggests that it works well in practice. It is applied to a breast cancer screening study.","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":"143432303","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

Distributed model building and recursive integration for big spatial data modeling. 大空间数据建模的分布式模型构建与递归集成。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae159

Emily C Hector, Brian J Reich, Ani Eloyan

引用次数: 0

Potential outcome simulation for efficient head-to-head comparison of adaptive dose-finding designs.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf012

Michael Sweeting, Daniel Slade, Dan Jackson, Kristian Brock

{"title":"Potential outcome simulation for efficient head-to-head comparison of adaptive dose-finding designs.","authors":"Michael Sweeting, Daniel Slade, Dan Jackson, Kristian Brock","doi":"10.1093/biomtc/ujaf012","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf012","url":null,"abstract":"Dose-finding trials are a key component of the drug development process and rely on a statistical design to help inform dosing decisions. Triallists wishing to choose a design require knowledge of operating characteristics of competing methods. This is often assessed using a large-scale simulation study with multiple designs and configurations investigated, which can be time-consuming and therefore limits the scope of the simulation. We introduce a new approach to the design of simulation studies of dose-finding trials. The approach simulates all potential outcomes that individuals could experience at each dose level in the trial. Datasets are simulated in advance and then applied to each of the competing methods to enable a more efficient head-to-head comparison. Furthermore, individual trial datasets can be interrogated to understand when designs deviate in their decision making. In three case-studies, we show sizeable reductions in Monte Carlo error for comparing a performance metric between two competing designs. Efficiency gains depend on the similarity of the designs. Comparing two Phase I/II design variants, with high correlation of recommending the same optimal biologic dose, we show that the new approach requires a simulation study that is approximately 48 times smaller than the conventional approach. Furthermore, advance-simulated trial datasets can be reused to assess the performance of designs across multiple configurations. We recommend researchers consider this more efficient simulation approach in their dose-finding studies and we have updated the R package escalation to help facilitate implementation.","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":"143482057","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 simple and powerful method for large-scale composite null hypothesis testing with applications in mediation analysis.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf011

Yaowu Liu

引用次数: 0

The subtype-free average causal effect for heterogeneous disease etiology.

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf016

A Sasson, M Wang, S Ogino, D Nevo

{"title":"The subtype-free average causal effect for heterogeneous disease etiology.","authors":"A Sasson, M Wang, S Ogino, D Nevo","doi":"10.1093/biomtc/ujaf016","DOIUrl":"10.1093/biomtc/ujaf016","url":null,"abstract":"Studies have shown that the effect an exposure may have on a disease can vary for different subtypes of the same disease. However, existing approaches to estimate and compare these effects largely overlook causality. In this paper, we study the effect smoking may have on having colorectal cancer subtypes defined by a trait known as microsatellite instability (MSI). We use principal stratification to propose an alternative causal estimand, the Subtype-Free Average Causal Effect (SF-ACE). The SF-ACE is the causal effect of the exposure among those who would be free from other disease subtypes under any exposure level. We study non-parametric identification of the SF-ACE and discuss different monotonicity assumptions, which are more nuanced than in the standard setting. As is often the case with principal stratum effects, the assumptions underlying the identification of the SF-ACE from the data are untestable and can be too strong. Therefore, we also develop sensitivity analysis methods that relax these assumptions. We present 3 different estimators, including a doubly robust estimator, for the SF-ACE. We implement our methodology for data from 2 large cohorts to study the heterogeneity in the causal effect of smoking on colorectal cancer with respect to MSI subtypes.","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/PMC11848129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143482137","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

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

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