Biometrics最新文献_第5页

Flagging unusual clusters based on linear mixed models using weighted and self-calibrated predictors. 基于线性混合模型，使用加权和自校准预测因子标记异常群集。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae022

Charles E McCulloch, John M Neuhaus, Ross D Boylan

{"title":"Flagging unusual clusters based on linear mixed models using weighted and self-calibrated predictors.","authors":"Charles E McCulloch, John M Neuhaus, Ross D Boylan","doi":"10.1093/biomtc/ujae022","DOIUrl":"10.1093/biomtc/ujae022","url":null,"abstract":"Statistical models incorporating cluster-specific intercepts are commonly used in hierarchical settings, for example, observations clustered within patients or patients clustered within hospitals. Predicted values of these intercepts are often used to identify or \"flag\" extreme or outlying clusters, such as poorly performing hospitals or patients with rapid declines in their health. We consider a variety of flagging rules, assessing different predictors, and using different accuracy measures. Using theoretical calculations and comprehensive numerical evaluation, we show that previously proposed rules based on the 2 most commonly used predictors, the usual best linear unbiased predictor and fixed effects predictor, perform extremely poorly: the incorrect flagging rates are either unacceptably high (approaching 0.5 in the limit) or overly conservative (eg, much <0.05 for reasonable parameter values, leading to very low correct flagging rates). We develop novel methods for flagging extreme clusters that can control the incorrect flagging rates, including very simple-to-use versions that we call \"self-calibrated.\" The new methods have substantially higher correct flagging rates than previously proposed methods for flagging extreme values, while controlling the incorrect flagging rates. We illustrate their application using data on length of stay in pediatric hospitals for children admitted for asthma diagnoses.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140334556","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 Bayesian convolutional neural network-based generalized linear model. 基于贝叶斯卷积神经网络的广义线性模型。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae057

Yeseul Jeon, Won Chang, Seonghyun Jeong, Sanghoon Han, Jaewoo Park

{"title":"A Bayesian convolutional neural network-based generalized linear model.","authors":"Yeseul Jeon, Won Chang, Seonghyun Jeong, Sanghoon Han, Jaewoo Park","doi":"10.1093/biomtc/ujae057","DOIUrl":"https://doi.org/10.1093/biomtc/ujae057","url":null,"abstract":"Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the prediction uncertainty, is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we propose a new Bayesian approach by embedding CNNs within the generalized linear models (GLMs) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo (MC) dropout as informative covariates in GLM. This improves accuracy in prediction and regression coefficient inference, allowing for the interpretation of coefficients and uncertainty quantification. By fitting ensemble GLMs across multiple realizations from MC dropout, we can account for uncertainties in extracting the features. We apply our methods to biological and epidemiological problems, which have both high-dimensional correlated inputs and vector covariates. Specifically, we consider malaria incidence data, brain tumor image data, and fMRI data. By extracting information from correlated inputs, the proposed method can provide an interpretable Bayesian analysis. The algorithm can be broadly applicable to image regressions or correlated data analysis by enabling accurate Bayesian inference quickly.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141417569","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 Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease. 学习阿尔茨海默病临床前阶段生物标记物轨迹和变化点的贝叶斯半参数模型。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae048

Kunbo Wang, William Hua, MeiCheng Wang, Yanxun Xu

{"title":"A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease.","authors":"Kunbo Wang, William Hua, MeiCheng Wang, Yanxun Xu","doi":"10.1093/biomtc/ujae048","DOIUrl":"10.1093/biomtc/ujae048","url":null,"abstract":"It has become consensus that mild cognitive impairment (MCI), one of the early symptoms onset of Alzheimer's disease (AD), may appear 10 or more years after the emergence of neuropathological abnormalities. Therefore, understanding the progression of AD biomarkers and uncovering when brain alterations begin in the preclinical stage, while patients are still cognitively normal, are crucial for effective early detection and therapeutic development. In this paper, we develop a Bayesian semiparametric framework that jointly models the longitudinal trajectory of the AD biomarker with a changepoint relative to the occurrence of symptoms onset, which is subject to left truncation and right censoring, in a heterogeneous population. Furthermore, unlike most existing methods assuming that everyone in the considered population will eventually develop the disease, our approach accounts for the possibility that some individuals may never experience MCI or AD, even after a long follow-up time. We evaluate the proposed model through simulation studies and demonstrate its clinical utility by examining an important AD biomarker, ptau181, using a dataset from the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11110494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074619","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

Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes. 采用非参数方法估计零膨胀计数结果移动健康中的因果偏移效应。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae054

Xueqing Liu, Tianchen Qian, Lauren Bell, Bibhas Chakraborty

{"title":"Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes.","authors":"Xueqing Liu, Tianchen Qian, Lauren Bell, Bibhas Chakraborty","doi":"10.1093/biomtc/ujae054","DOIUrl":"https://doi.org/10.1093/biomtc/ujae054","url":null,"abstract":"In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the \"gold-standard\" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The \"causal excursion effect,\" a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141260409","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

Addressing age measurement errors in fish growth estimation from length-stratified samples. 利用长度分层样本估算鱼类生长过程中的年龄测量误差。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae029

Nan Zheng, Atefeh Kheirollahi, Yildiz Yilmaz

{"title":"Addressing age measurement errors in fish growth estimation from length-stratified samples.","authors":"Nan Zheng, Atefeh Kheirollahi, Yildiz Yilmaz","doi":"10.1093/biomtc/ujae029","DOIUrl":"https://doi.org/10.1093/biomtc/ujae029","url":null,"abstract":"Fish growth models are crucial for fisheries stock assessments and are commonly estimated using fish length-at-age data. This data is widely collected using length-stratified age sampling (LSAS), a cost-effective two-phase response-selective sampling method. The data may contain age measurement errors (MEs). We propose a methodology that accounts for both LSAS and age MEs to accurately estimate fish growth. The proposed methods use empirical proportion likelihood methodology for LSAS and the structural errors in variables methodology for age MEs. We provide a measure of uncertainty for parameter estimates and standardized residuals for model validation. To model the age distribution, we employ a continuation ratio-logit model that is consistent with the random nature of the true age distribution. We also apply a discretization approach for age and length distributions, which significantly improves computational efficiency and is consistent with the discrete age and length data typically encountered in practice. Our simulation study shows that neglecting age MEs can lead to significant bias in growth estimation, even with small but non-negligible age MEs. However, our new approach performs well regardless of the magnitude of age MEs and accurately estimates SEs of parameter estimators. Real data analysis demonstrates the effectiveness of the proposed model validation device. Computer codes to implement the methodology are provided.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140849395","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

Case weighted power priors for hybrid control analyses with time-to-event data. 利用时间到事件数据进行混合控制分析的案例加权幂先验。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae019

Evan Kwiatkowski, Jiawen Zhu, Xiao Li, Herbert Pang, Grazyna Lieberman, Matthew A Psioda

{"title":"Case weighted power priors for hybrid control analyses with time-to-event data.","authors":"Evan Kwiatkowski, Jiawen Zhu, Xiao Li, Herbert Pang, Grazyna Lieberman, Matthew A Psioda","doi":"10.1093/biomtc/ujae019","DOIUrl":"10.1093/biomtc/ujae019","url":null,"abstract":"We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCTs) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g., unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10968526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140304678","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

Doubly robust estimation and sensitivity analysis for marginal structural quantile models. 边际结构量化模型的双稳健估计和敏感性分析。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae045

Chao Cheng, Liangyuan Hu, Fan Li

{"title":"Doubly robust estimation and sensitivity analysis for marginal structural quantile models.","authors":"Chao Cheng, Liangyuan Hu, Fan Li","doi":"10.1093/biomtc/ujae045","DOIUrl":"https://doi.org/10.1093/biomtc/ujae045","url":null,"abstract":"The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence function for the MSQM, from which a new doubly robust estimator is proposed for point estimation and inference. We show that the doubly robust estimator is consistent if either of the models associated with treatment assignment or the potential outcome distributions is correctly specified, and is semiparametric efficient if both models are correct. To implement the doubly robust MSQM estimator, we propose to solve a smoothed estimating equation to facilitate efficient computation of the point and variance estimates. In addition, we develop a confounding function approach to investigate the sensitivity of several MSQM estimators when the sequential ignorability assumption is violated. Extensive simulations are conducted to examine the finite-sample performance characteristics of the proposed methods. We apply the proposed methods to the Yale New Haven Health System Electronic Health Record data to study the effect of antihypertensive medications to patients with severe hypertension and assess the robustness of the findings to unmeasured baseline and time-varying confounding.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141330357","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

Dissecting the colocalized GWAS and eQTLs with mediation analysis for high-dimensional exposures and confounders. 通过对高维暴露和混杂因素进行中介分析，剖析定位的 GWAS 和 eQTL。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae050

Qi Zhang, Zhikai Yang, Jinliang Yang

{"title":"Dissecting the colocalized GWAS and eQTLs with mediation analysis for high-dimensional exposures and confounders.","authors":"Qi Zhang, Zhikai Yang, Jinliang Yang","doi":"10.1093/biomtc/ujae050","DOIUrl":"https://doi.org/10.1093/biomtc/ujae050","url":null,"abstract":"To leverage the advancements in genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping for traits and molecular phenotypes to gain mechanistic understanding of the genetic regulation, biological researchers often investigate the expression QTLs (eQTLs) that colocalize with QTL or GWAS peaks. Our research is inspired by 2 such studies. One aims to identify the causal single nucleotide polymorphisms that are responsible for the phenotypic variation and whose effects can be explained by their impacts at the transcriptomic level in maize. The other study in mouse focuses on uncovering the cis-driver genes that induce phenotypic changes by regulating trans-regulated genes. Both studies can be formulated as mediation problems with potentially high-dimensional exposures, confounders, and mediators that seek to estimate the overall indirect effect (IE) for each exposure. In this paper, we propose MedDiC, a novel procedure to estimate the overall IE based on difference-in-coefficients approach. Our simulation studies find that MedDiC offers valid inference for the IE with higher power, shorter confidence intervals, and faster computing time than competing methods. We apply MedDiC to the 2 aforementioned motivating datasets and find that MedDiC yields reproducible outputs across the analysis of closely related traits, with results supported by external biological evidence. The code and additional information are available on our GitHub page (https://github.com/QiZhangStat/MedDiC).","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155115","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

Deep partially linear cox model for current status data. 针对现状数据的深度部分线性 Cox 模型。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae024

Qiang Wu, Xingwei Tong, Xingqiu Zhao

引用次数: 0

Causal inference for time-to-event data with a cured subpopulation. 具有固化子群的时间到事件数据的因果推理。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae028

Yi Wang, Yuhao Deng, Xiao-Hua Zhou

{"title":"Causal inference for time-to-event data with a cured subpopulation.","authors":"Yi Wang, Yuhao Deng, Xiao-Hua Zhou","doi":"10.1093/biomtc/ujae028","DOIUrl":"https://doi.org/10.1093/biomtc/ujae028","url":null,"abstract":"When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140856016","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