Biometrics最新文献_第2页

Addressing confounding and continuous exposure measurement error using corrected score functions. 使用校正分数函数解决混淆和连续曝光测量误差。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf045

Brian D Richardson, Bryan S Blette, Peter B Gilbert, Michael G Hudgens

{"title":"Addressing confounding and continuous exposure measurement error using corrected score functions.","authors":"Brian D Richardson, Bryan S Blette, Peter B Gilbert, Michael G Hudgens","doi":"10.1093/biomtc/ujaf045","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf045","url":null,"abstract":"Confounding and exposure measurement error can introduce bias when drawing inference about the marginal effect of an exposure on an outcome of interest. While there are broad methodologies for addressing each source of bias individually, confounding and exposure measurement error frequently co-occur, and there is a need for methods that address them simultaneously. In this paper, corrected score methods are derived under classical additive measurement error to draw inference about marginal exposure effects using only measured variables. Three estimators are proposed based on g-formula, inverse probability weighting, and doubly-robust estimation techniques. The estimators are shown to be consistent and asymptotically normal, and the doubly-robust estimator is shown to exhibit its namesake property. The methods, which are implemented in the R package mismex, perform well in finite samples under both confounding and measurement error as demonstrated by simulation studies. The proposed doubly-robust estimator is applied to study the effects of two biomarkers on HIV-1 infection using data from the HVTN 505 preventative vaccine trial.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12038274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962773","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 weighted quantile treatment effects with missing outcome data by double sampling. 通过双重抽样估计缺少结果数据的加权分位数治疗效果。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf038

Shuo Sun, Sebastien Haneuse, Alexander W Levis, Catherine Lee, David E Arterburn, Heidi Fischer, Susan Shortreed, Rajarshi Mukherjee

{"title":"Estimating weighted quantile treatment effects with missing outcome data by double sampling.","authors":"Shuo Sun, Sebastien Haneuse, Alexander W Levis, Catherine Lee, David E Arterburn, Heidi Fischer, Susan Shortreed, Rajarshi Mukherjee","doi":"10.1093/biomtc/ujaf038","DOIUrl":"10.1093/biomtc/ujaf038","url":null,"abstract":"Causal weighted quantile treatment effects (WQTEs) complement standard mean-focused causal contrasts when interest lies at the tails of the counterfactual distribution. However, existing methods for estimating and inferring causal WQTEs assume complete data on all relevant factors, which is often not the case in practice, particularly when the data are not collected for research purposes, such as electronic health records (EHRs) and disease registries. Furthermore, these data may be particularly susceptible to the outcome data being missing-not-at-random (MNAR). This paper proposes to use double sampling, through which the otherwise missing data are ascertained on a sub-sample of study units, as a strategy to mitigate bias due to MNAR data in estimating causal WQTEs. With the additional data, we present identifying conditions that do not require missingness assumptions in the original data. We then propose a novel inverse-probability weighted estimator and derive its asymptotic properties, both pointwise at specific quantiles and uniformly across quantiles over some compact subset of (0,1), allowing the propensity score and double-sampling probabilities to be estimated. For practical inference, we develop a bootstrap method that can be used for both pointwise and uniform inference. A simulation study is conducted to examine the finite sample performance of the proposed estimators. We illustrate the proposed method using EHR data examining the relative effects of 2 bariatric surgery procedures on BMI loss 3 years post-surgery.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11973573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143794495","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

Rejoinder to the discussion on "Continuous-space occupancy models". 回答关于“连续空间占用模式”的讨论。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf058

Wilson J Wright, Mevin B Hooten

引用次数: 0

Discussion on "Continuous-space occupancy models" by Wilson J. Wright and Mevin B. Hooten. Wilson J. Wright和Mevin B. Hooten关于“连续空间占用模式”的讨论。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf057

Léa Pautrel, Marie-Pierre Etienne, Olivier Gimenez

引用次数: 0

Discussion on "Continuous-space occupancy models" by Wilson J. Wright and Mevin B. Hooten. Wilson J. Wright和Mevin B. Hooten关于“连续空间占用模式”的讨论。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf056

Jeffrey W Doser, Krishna Pacifici

引用次数: 0

Power-enhanced two-sample mean tests for high-dimensional microbiome compositional data. 高维微生物组组成数据的功率增强双样本均值测试。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf034

Danning Li, Lingzhou Xue, Haoyi Yang, Xiufan Yu

{"title":"Power-enhanced two-sample mean tests for high-dimensional microbiome compositional data.","authors":"Danning Li, Lingzhou Xue, Haoyi Yang, Xiufan Yu","doi":"10.1093/biomtc/ujaf034","DOIUrl":"10.1093/biomtc/ujaf034","url":null,"abstract":"Testing differences in mean vectors is a fundamental task in the analysis of high-dimensional microbiome compositional data. Existing methods may suffer from low power if the underlying signal pattern is in a situation that does not favor the deployed test. In this work, we develop 2-sample power-enhanced mean tests for high-dimensional compositional data based on the combination of $P$-values, which integrates strengths from 2 popular types of tests: the maximum-type test and the quadratic-type test. We provide rigorous theoretical guarantees on the proposed tests, showing accurate Type-I error rate control and enhanced testing power. Our method boosts the testing power toward a broader alternative space, which yields robust performance across a wide range of signal pattern settings. Our methodology and theory also contribute to the literature on power enhancement and Gaussian approximation for high-dimensional hypothesis testing. We demonstrate the performance of our method on both simulated data and real-world microbiome data, showing that our proposed approach improves the testing power substantially compared to existing methods.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11962435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143762714","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

Robust and efficient semi-supervised learning for Ising model. 伊辛模型的鲁棒高效半监督学习。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf060

Daiqing Wu, Molei Liu

{"title":"Robust and efficient semi-supervised learning for Ising model.","authors":"Daiqing Wu, Molei Liu","doi":"10.1093/biomtc/ujaf060","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf060","url":null,"abstract":"In biomedical studies, it is often desirable to characterize the interactive mode of multiple disease outcomes beyond their marginal risk. Ising model is one of the most popular choices serving this purpose. Nevertheless, learning efficiency of Ising models can be impeded by the scarcity of accurate disease labels, which is a prominent problem in contemporary studies driven by electronic health records (EHRs). Semi-supervised learning (SSL) leverages the large unlabeled sample with auxiliary EHR features to assist the learning with labeled data only and is a potential solution to this issue. In this paper, we develop a novel SSL method for efficient inference of Ising model. Our method first models the outcomes against the auxiliary features, then uses it to project the score function of the supervised estimator onto the EHR features, and incorporates the unlabeled sample to augment the supervised estimator for variance reduction without introducing bias. For the key step of conditional modeling, we propose strategies that can effectively leverage the auxiliary EHR information while maintaining moderate model complexity. In addition, we introduce approaches including intrinsic efficient updates and ensemble, to overcome the potential misspecification of the conditional model that may cause efficiency loss. Our method is justified by asymptotic theory and shown to outperform existing SSL methods through simulation studies. We also illustrate its utility in a real example about several key phenotypes related to frequent intensive care unit (ICU) admission on MIMIC-III data set.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075635","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

Multiple bias calibration for valid statistical inference under nonignorable nonresponse. 不可忽略非响应下有效统计推断的多偏差校准。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf044

Seonghun Cho, Jae Kwang Kim, Yumou Qiu

引用次数: 0

Inference with approximate local false discovery rates. 近似局部错误发现率的推理。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf035

Rajesh Karmakar, Ruth Heller, Saharon Rosset

{"title":"Inference with approximate local false discovery rates.","authors":"Rajesh Karmakar, Ruth Heller, Saharon Rosset","doi":"10.1093/biomtc/ujaf035","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf035","url":null,"abstract":"Efron's 2-group model is widely used in large-scale multiple testing. This model assumes that test statistics are drawn independently from a mixture of a null and a non-null distribution. The marginal local false discovery rate (locFDR) is the probability that the hypothesis is null given its test statistic. The procedure that rejects null hypotheses with marginal locFDRs below a fixed threshold maximizes power (the expected number of non-nulls rejected) while controlling the marginal false discovery rate in this model. However, in realistic settings the test statistics are dependent, and taking the dependence into account can boost power. Unfortunately, the resulting calculations are typically exponential in the number of hypotheses, which is impractical. Instead, we propose using $textrm {locFDR}_N$, which is the probability that the hypothesis is null given the test statistics in its $N$-neighborhood. We prove that rejecting for small $textrm {locFDR}_N$ is optimal in the restricted class where the decision for each hypothesis is only guided by its $N$-neighborhood, and that power increases with $N$. The computational complexity of computing the $mathrm{ locFDR}_N$s increases with $N$, so the analyst should choose the largest $N$-neighborhood that is still computationally feasible. We show through extensive simulations that our proposed procedure can be substantially more powerful than alternative practical approaches, even with small $N$-neighborhoods. We demonstrate the utility of our method in a genome-wide association study of height.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143976983","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 semiparametric quantile regression rank score test for zero-inflated data. 零膨胀数据的半参数分位数回归秩得分检验。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf050

Zirui Wang, Wodan Ling, Tianying Wang

{"title":"A semiparametric quantile regression rank score test for zero-inflated data.","authors":"Zirui Wang, Wodan Ling, Tianying Wang","doi":"10.1093/biomtc/ujaf050","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf050","url":null,"abstract":"Zero-inflated data commonly arise in various fields, including economics, healthcare, and environmental sciences, where measurements frequently include an excess of zeros due to structural or sampling mechanisms. Traditional approaches, such as Zero-Inflated Poisson and Zero-Inflated Negative Binomial models, have been widely used to handle excess zeros in count data, but they rely on strong parametric assumptions that may not hold in complex real-world applications. In this paper, we propose a zero-inflated quantile single-index rank-score-based test (ZIQ-SIR) to detect associations between zero-inflated outcomes and covariates, particularly when nonlinear relationships are present. ZIQ-SIR offers a flexible, semi-parametric approach that accounts for the zero-inflated nature of the data and avoids the restrictive assumptions of traditional parametric models. Through simulations, we show that ZIQ-SIR outperforms existing methods by achieving higher power and better Type I error control, owing to its flexibility in modeling zero-inflated and overdispersed data. We apply our method to the real-world dataset: microbiome abundance from the Columbian Gut study. In this application, ZIQ-SIR identifies more significant associations than alternative approaches, while maintaining accurate type I error control.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12050976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962446","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