Biometrics最新文献_第10页

Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard. 使用不完善的金标准进行非参数接收器工作特征曲线分析。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae063

Jiarui Sun, Chao Tang, Wuxiang Xie, Xiao-Hua Zhou

{"title":"Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard.","authors":"Jiarui Sun, Chao Tang, Wuxiang Xie, Xiao-Hua Zhou","doi":"10.1093/biomtc/ujae063","DOIUrl":"https://doi.org/10.1093/biomtc/ujae063","url":null,"abstract":"This article addresses the challenge of estimating receiver operating characteristic (ROC) curves and the areas under these curves (AUC) in the context of an imperfect gold standard, a common issue in diagnostic accuracy studies. We delve into the nonparametric identification and estimation of ROC curves and AUCs when the reference standard for disease status is prone to error. Our approach hinges on the known or estimable accuracy of this imperfect reference standard and the conditional independent assumption, under which we demonstrate the identifiability of ROC curves and propose a nonparametric estimation method. In cases where the accuracy of the imperfect reference standard remains unknown, we establish that while ROC curves are unidentifiable, the sign of the difference between two AUCs is identifiable. This insight leads us to develop a hypothesis-testing method for assessing the relative superiority of AUCs. Compared to the existing methods, the proposed methods are nonparametric so that they do not rely on the parametric model assumptions. In addition, they are applicable to both the ROC/AUC analysis of continuous biomarkers and the AUC analysis of ordinal biomarkers. Our theoretical results and simulation studies validate the proposed methods, which we further illustrate through application in two real-world diagnostic studies.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141589542","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

Absolute risk from double nested case-control designs: cause-specific proportional hazards models with and without augmented estimating equations. 双嵌套病例对照设计的绝对风险：使用和不使用增强估计方程的特定病因比例危险模型。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae062

Minjung Lee, Mitchell H Gail

{"title":"Absolute risk from double nested case-control designs: cause-specific proportional hazards models with and without augmented estimating equations.","authors":"Minjung Lee, Mitchell H Gail","doi":"10.1093/biomtc/ujae062","DOIUrl":"https://doi.org/10.1093/biomtc/ujae062","url":null,"abstract":"We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141589541","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

Factor-augmented transformation models for interval-censored failure time data. 用于间隔删失故障时间数据的因子增强变换模型。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae078

Hongxi Li, Shuwei Li, Liuquan Sun, Xinyuan Song

{"title":"Factor-augmented transformation models for interval-censored failure time data.","authors":"Hongxi Li, Shuwei Li, Liuquan Sun, Xinyuan Song","doi":"10.1093/biomtc/ujae078","DOIUrl":"https://doi.org/10.1093/biomtc/ujae078","url":null,"abstract":"Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This work proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates. We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event. Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation. We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study is provided. An R package ICTransCFA is also available for practitioners. Data used in preparation of this article were obtained from the ADNI database.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142035125","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

Propensity weighting plus adjustment in proportional hazards model is not doubly robust. 比例危险模型中的倾向加权加调整不具有双重稳健性。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae069

Erin E Gabriel, Michael C Sachs, Ingeborg Waernbaum, Els Goetghebeur, Paul F Blanche, Stijn Vansteelandt, Arvid Sjölander, Thomas Scheike

{"title":"Propensity weighting plus adjustment in proportional hazards model is not doubly robust.","authors":"Erin E Gabriel, Michael C Sachs, Ingeborg Waernbaum, Els Goetghebeur, Paul F Blanche, Stijn Vansteelandt, Arvid Sjölander, Thomas Scheike","doi":"10.1093/biomtc/ujae069","DOIUrl":"https://doi.org/10.1093/biomtc/ujae069","url":null,"abstract":"Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulation this lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and a simple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. We provide a novel proof that the combination of propensity score weighting and a proportional hazards survival model, fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcome under particular censoring mechanisms if either the propensity score or the outcome model is correctly specified and contains all confounders. Given our results suggesting that double robustness only exists under the null, we outline 2 simple alternative estimators that are doubly robust for the survival difference at a given time point (in the above sense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimation for the full survival curve. We provide R code to use these estimators for estimation and inference in the supporting information.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141733497","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

Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators. 通过整合来自异质种群的外部信息改进线性回归模型的预测：詹姆斯-斯坦估计器

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae072

Peisong Han, Haoyue Li, Sung Kyun Park, Bhramar Mukherjee, Jeremy M G Taylor

{"title":"Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.","authors":"Peisong Han, Haoyue Li, Sung Kyun Park, Bhramar Mukherjee, Jeremy M G Taylor","doi":"10.1093/biomtc/ujae072","DOIUrl":"10.1093/biomtc/ujae072","url":null,"abstract":"We consider the setting where (1) an internal study builds a linear regression model for prediction based on individual-level data, (2) some external studies have fitted similar linear regression models that use only subsets of the covariates and provide coefficient estimates for the reduced models without individual-level data, and (3) there is heterogeneity across these study populations. The goal is to integrate the external model summary information into fitting the internal model to improve prediction accuracy. We adapt the James-Stein shrinkage method to propose estimators that are no worse and are oftentimes better in the prediction mean squared error after information integration, regardless of the degree of study population heterogeneity. We conduct comprehensive simulation studies to investigate the numerical performance of the proposed estimators. We also apply the method to enhance a prediction model for patella bone lead level in terms of blood lead level and other covariates by integrating summary information from published literature.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141888418","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

Post-selection inference in regression models for group testing data. 分组测试数据回归模型中的后选择推断。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae101

Qinyan Shen, Karl Gregory, Xianzheng Huang

引用次数: 0

A Gaussian-process approximation to a spatial SIR process using moment closures and emulators. 使用矩闭合和仿真器的空间 SIR 过程的高斯过程近似。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae068

Parker Trostle, Joseph Guinness, Brian J Reich

{"title":"A Gaussian-process approximation to a spatial SIR process using moment closures and emulators.","authors":"Parker Trostle, Joseph Guinness, Brian J Reich","doi":"10.1093/biomtc/ujae068","DOIUrl":"10.1093/biomtc/ujae068","url":null,"abstract":"The dynamics that govern disease spread are hard to model because infections are functions of both the underlying pathogen as well as human or animal behavior. This challenge is increased when modeling how diseases spread between different spatial locations. Many proposed spatial epidemiological models require trade-offs to fit, either by abstracting away theoretical spread dynamics, fitting a deterministic model, or by requiring large computational resources for many simulations. We propose an approach that approximates the complex spatial spread dynamics with a Gaussian process. We first propose a flexible spatial extension to the well-known SIR stochastic process, and then we derive a moment-closure approximation to this stochastic process. This moment-closure approximation yields ordinary differential equations for the evolution of the means and covariances of the susceptibles and infectious through time. Because these ODEs are a bottleneck to fitting our model by MCMC, we approximate them using a low-rank emulator. This approximation serves as the basis for our hierarchical model for noisy, underreported counts of new infections by spatial location and time. We demonstrate using our model to conduct inference on simulated infections from the underlying, true spatial SIR jump process. We then apply our method to model counts of new Zika infections in Brazil from late 2015 through early 2016.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11261348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141733496","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 generalized outcome-adaptive sequential multiple assignment randomized trial design. 广义结果适应性顺序多重分配随机试验设计。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae073

Xue Yang, Yu Cheng, Peter F Thall, Abdus S Wahed

{"title":"A generalized outcome-adaptive sequential multiple assignment randomized trial design.","authors":"Xue Yang, Yu Cheng, Peter F Thall, Abdus S Wahed","doi":"10.1093/biomtc/ujae073","DOIUrl":"https://doi.org/10.1093/biomtc/ujae073","url":null,"abstract":"A dynamic treatment regime (DTR) is a mathematical representation of a multistage decision process. When applied to sequential treatment selection in medical settings, DTRs are useful for identifying optimal therapies for chronic diseases such as AIDs, mental illnesses, substance abuse, and many cancers. Sequential multiple assignment randomized trials (SMARTs) provide a useful framework for constructing DTRs and providing unbiased between-DTR comparisons. A limitation of SMARTs is that they ignore data from past patients that may be useful for reducing the probability of exposing new patients to inferior treatments. In practice, this may result in decreased treatment adherence or dropouts. To address this problem, we propose a generalized outcome-adaptive (GO) SMART design that adaptively unbalances stage-specific randomization probabilities in favor of treatments observed to be more effective in previous patients. To correct for bias induced by outcome adaptive randomization, we propose G-estimators and inverse-probability-weighted estimators of DTR effects embedded in a GO-SMART and show analytically that they are consistent. We report simulation results showing that, compared to a SMART, Response-Adaptive SMART and SMART with adaptive randomization, a GO-SMART design treats significantly more patients with the optimal DTR and achieves a larger number of total responses while maintaining similar or better statistical power.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141896689","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

Planning cost-effective operational forest inventories. 规划具有成本效益的实用森林资源调查。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae104

Santeri Karppinen, Liviu Ene, Lovisa Engberg Sundström, Juha Karvanen

{"title":"Planning cost-effective operational forest inventories.","authors":"Santeri Karppinen, Liviu Ene, Lovisa Engberg Sundström, Juha Karvanen","doi":"10.1093/biomtc/ujae104","DOIUrl":"https://doi.org/10.1093/biomtc/ujae104","url":null,"abstract":"We address a Bayesian two-stage decision problem in operational forestry where the inner stage considers scheduling the harvesting to fulfill demand targets and the outer stage considers selecting the accuracy of pre-harvest inventories that are used to estimate the timber volumes of the forest tracts. The higher accuracy of the inventory enables better scheduling decisions but also implies higher costs. We focus on the outer stage, which we formulate as a maximization of the posterior value of the inventory decision under a budget constraint. The posterior value depends on the solution to the inner stage problem and its computation is analytically intractable, featuring an NP-hard binary optimization problem within a high-dimensional integral. In particular, the binary optimization problem is a special case of a generalized quadratic assignment problem. We present a practical method that solves the outer stage problem with an approximation which combines Monte Carlo sampling with a greedy, randomized method for the binary optimization problem. We derive inventory decisions for a dataset of 100 Swedish forest tracts across a range of inventory budgets and estimate the value of the information to be obtained.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142340683","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

Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood. 基于精确似然法的稀疏数据荟萃分析中出版偏差的敏感性分析。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae092

Taojun Hu, Yi Zhou, Satoshi Hattori

{"title":"Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood.","authors":"Taojun Hu, Yi Zhou, Satoshi Hattori","doi":"10.1093/biomtc/ujae092","DOIUrl":"10.1093/biomtc/ujae092","url":null,"abstract":"Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analyses of sparse data, which may arise when the event rate is low for binary or count outcomes, pose a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal-normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the $t$-statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analyses and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal-normal model. The proposed method would give useful guidance to address publication bias in the meta-analysis of sparse data.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280084","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