Biometrics最新文献_第3页

A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data. 具有潜在随机过程的贝叶斯联合纵向生存模型。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf052

Madeline R Abbott, Walter H Dempsey, Inbal Nahum-Shani, Lindsey N Potter, David W Wetter, Cho Y Lam, Jeremy M G Taylor

{"title":"A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data.","authors":"Madeline R Abbott, Walter H Dempsey, Inbal Nahum-Shani, Lindsey N Potter, David W Wetter, Cho Y Lam, Jeremy M G Taylor","doi":"10.1093/biomtc/ujaf052","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf052","url":null,"abstract":"The availability of mobile health (mHealth) technology has enabled increased collection of intensive longitudinal data (ILD). ILD have potential to capture rapid fluctuations in outcomes that may be associated with changes in the risk of an event. However, existing methods for jointly modeling longitudinal and event-time outcomes are not well-equipped to handle ILD due to the high computational cost. We propose a joint longitudinal and time-to-event model suitable for analyzing ILD. In this model, we summarize a multivariate longitudinal outcome as a smaller number of time-varying latent factors. These latent factors, which are modeled using an Ornstein-Uhlenbeck stochastic process, capture the risk of a time-to-event outcome in a parametric hazard model. We take a Bayesian approach to fit our joint model and conduct simulations to assess its performance. We use it to analyze data from an mHealth study of smoking cessation. We summarize the longitudinal self-reported intensity of 9 emotions as the psychological states of positive and negative affect. These time-varying latent states capture the risk of the first smoking lapse after attempted quit. Understanding factors associated with smoking lapse is of keen interest to smoking cessation researchers.","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/PMC12050977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143967240","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

Uncovering mortality patterns and hospital effects in COVID-19 heart failure patients: a novel multilevel logistic cluster-weighted modeling approach. 揭示COVID-19心力衰竭患者的死亡率模式和医院影响：一种新的多层次logistic聚类加权建模方法

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf046

Luca Caldera, Chiara Masci, Andrea Cappozzo, Marco Forlani, Barbara Antonelli, Olivia Leoni, Francesca Ieva

{"title":"Uncovering mortality patterns and hospital effects in COVID-19 heart failure patients: a novel multilevel logistic cluster-weighted modeling approach.","authors":"Luca Caldera, Chiara Masci, Andrea Cappozzo, Marco Forlani, Barbara Antonelli, Olivia Leoni, Francesca Ieva","doi":"10.1093/biomtc/ujaf046","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf046","url":null,"abstract":"Evaluating hospital performance and its relationship to patients' characteristics is of utmost importance to ensure timely, effective, and optimal treatment. This is particularly relevant in areas and situations where the healthcare system must deal with an unexpected surge in hospitalizations, such as heart failure patients in the Lombardy Region of Italy during the COVID-19 pandemic. Motivated by this issue, the paper introduces a novel multilevel logistic cluster-weighted model for predicting 45-day mortality following hospitalization due to COVID-19. The methodology flexibly accommodates dependence patterns among continuous and dichotomous variables; effectively accounting for group-specific effects in distinct subgroups showing different attributes. A tailored classification expectation-maximization algorithm is developed for parameter estimation, and extensive simulation studies are conducted to evaluate its performance against competing models. The novel approach is applied to administrative data from the Lombardy Region, with the aim of profiling heart failure patients hospitalized for COVID-19 and investigating the hospital-level impact on their overall mortality. A scenario analysis demonstrates the model's efficacy in managing multiple sources of heterogeneity, thereby yielding promising results in aiding healthcare providers and policymakers in the identification of patient-specific treatment pathways.","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":"143960546","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

Doubly robust omnibus sensitivity analysis of externally controlled trials with intercurrent events. 具有并发事件的外部对照试验的双鲁棒综合敏感性分析。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf047

Chenyin Gao, Xiang Zhang, Shu Yang

{"title":"Doubly robust omnibus sensitivity analysis of externally controlled trials with intercurrent events.","authors":"Chenyin Gao, Xiang Zhang, Shu Yang","doi":"10.1093/biomtc/ujaf047","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf047","url":null,"abstract":"Externally controlled trials are crucial in clinical development when randomized controlled trials are unethical or impractical. These trials consist of a full treatment arm with the experimental treatment and a full external control arm. However, they present significant challenges in learning the treatment effect due to the lack of randomization and a parallel control group. Besides baseline incomparability, outcome mean non-exchangeability, caused by differences in conditional outcome distributions between external controls and counterfactual concurrent controls, is infeasible to test and may introduce biases in evaluating the treatment effect. Sensitivity analysis of outcome mean non-exchangeability is thus critically important to assess the robustness of the study's conclusions against such assumption violations. Moreover, intercurrent events, which are ubiquitous and inevitable in clinical studies, can further confound the treatment effect and hinder the interpretation of the estimated treatment effects. This paper establishes a semi-parametric framework for externally controlled trials with intercurrent events, offering doubly robust and locally optimal estimators for primary and sensitivity analyses. We develop an omnibus sensitivity analysis that accounts for both outcome mean non-exchangeability and the impacts of intercurrent events simultaneously, ensuring root-n consistency and asymptotic normality under specified conditions. The performance of the proposed sensitivity analysis is evaluated in simulation studies and a real-data problem.","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":"143973753","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

Double robust variance estimation with parametric working models. 参数工作模型的双稳健方差估计。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf054

Bonnie E Shook-Sa, Paul N Zivich, Chanhwa Lee, Keyi Xue, Rachael K Ross, Jessie K Edwards, Jeffrey S A Stringer, Stephen R Cole

{"title":"Double robust variance estimation with parametric working models.","authors":"Bonnie E Shook-Sa, Paul N Zivich, Chanhwa Lee, Keyi Xue, Rachael K Ross, Jessie K Edwards, Jeffrey S A Stringer, Stephen R Cole","doi":"10.1093/biomtc/ujaf054","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf054","url":null,"abstract":"Doubly robust estimators have gained popularity in the field of causal inference due to their ability to provide consistent point estimates when either an outcome or an exposure model is correctly specified. However, for nonrandomized exposures, the influence function based variance estimator frequently used with doubly robust estimators of the average causal effect is only consistent when both working models (ie, outcome and exposure models) are correctly specified. Here, the empirical sandwich variance estimator and the nonparametric bootstrap are demonstrated to be doubly robust variance estimators. That is, they are expected to provide valid estimates of the variance leading to nominal confidence interval coverage when only 1 working model is correctly specified. Simulation studies illustrate the properties of the influence function based, empirical sandwich, and nonparametric bootstrap variance estimators in the setting where parametric working models are assumed. Estimators are applied to data from the Improving Pregnancy Outcomes with Progesterone (IPOP) study to estimate the effect of maternal anemia on birth weight among women with HIV.","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/PMC12050975/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143975971","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

Causal inference with cross-temporal design. 跨时间设计的因果推理。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae163

Yi Cao, Pedro L Gozalo, Roee Gutman

{"title":"Causal inference with cross-temporal design.","authors":"Yi Cao, Pedro L Gozalo, Roee Gutman","doi":"10.1093/biomtc/ujae163","DOIUrl":"10.1093/biomtc/ujae163","url":null,"abstract":"When many participants in a randomized trial do not comply with their assigned intervention, the randomized encouragement design is a possible solution. In this design, the causal effects of the intervention can be estimated among participants who would have experienced the intervention if encouraged. For many policy interventions, encouragements cannot be randomized and investigators need to rely on observational data. To address this, we propose a cross-temporal design, which uses time to mimic a randomized encouragement experiment. However, time may be confounded with temporal trends that influence the outcomes. To disentangle these trends from the intervention effects, we replace the commonly used exclusion restrictions with temporal assumptions. We develop Bayesian procedures to estimate the causal effects and compare it to instrumental variables and matching approaches in simulations. The Bayesian approach outperforms the other 2 approaches in terms of estimation accuracy, and it is relatively robust to various violations of the common trends assumption. Taking advantage of the expansion of the Medicare Advantage (MA) program between 2011 and 2017, we implement the proposed method to estimate the effects of MA enrollment on the risk of skilled nursing facility residents being re-hospitalized within 30 days after discharge from the hospital.","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/PMC11725568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969461","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

Change surface regression for nonlinear subgroup identification with application to warfarin pharmacogenomics data. 变化面回归非线性亚群识别在华法林药物基因组学数据中的应用。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae169

Pan Liu, Yaguang Li, Jialiang Li

{"title":"Change surface regression for nonlinear subgroup identification with application to warfarin pharmacogenomics data.","authors":"Pan Liu, Yaguang Li, Jialiang Li","doi":"10.1093/biomtc/ujae169","DOIUrl":"10.1093/biomtc/ujae169","url":null,"abstract":"Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic variations on inter-individual outcome variability. Despite its promise, the intricate landscape of drug metabolism introduces complexity, where the correlation between drug response and genes can be shaped by numerous nongenetic factors, often exhibiting heterogeneity across diverse subpopulations. This challenge is particularly pronounced in datasets such as the International Warfarin Pharmacogenetic Consortium (IWPC), which encompasses diverse patient information from multiple nations. To capture the between-patient heterogeneity in dosing requirement, we formulate a novel change surface model as a model-based approach for multiple subgroup identification in complex datasets. A key feature of our approach is its ability to accommodate nonlinear subgroup divisions, providing a clearer understanding of dynamic drug-gene associations. Furthermore, our model effectively handles high-dimensional data through a doubly penalized approach, ensuring both interpretability and adaptability. We propose an iterative 2-stage method that combines a change point detection technique in the first stage with a smoothed local adaptive majorize-minimization algorithm for surface regression in the second stage. Performance of the proposed methods is evaluated through extensive numerical studies. Application of our method to the IWPC dataset leads to significant new findings, where 3 subgroups subject to different pharmacogenomic relationships are identified, contributing valuable insights into the complex dynamics of drug-gene associations in patients.","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":"142999226","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

Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates. 带不可忽略缺失协变量的最优动态治疗方案加权q学习。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae161

Jian Sun, Bo Fu, Li Su

{"title":"Weighted Q-learning for optimal dynamic treatment regimes with nonignorable missing covariates.","authors":"Jian Sun, Bo Fu, Li Su","doi":"10.1093/biomtc/ujae161","DOIUrl":"https://doi.org/10.1093/biomtc/ujae161","url":null,"abstract":"Dynamic treatment regimes (DTRs) formalize medical decision-making as a sequence of rules for different stages, mapping patient-level information to recommended treatments. In practice, estimating an optimal DTR using observational data from electronic medical record (EMR) databases can be complicated by nonignorable missing covariates resulting from informative monitoring of patients. Since complete case analysis can provide consistent estimation of outcome model parameters under the assumption of outcome-independent missingness, Q-learning is a natural approach to accommodating nonignorable missing covariates. However, the backward induction algorithm used in Q-learning can introduce challenges, as nonignorable missing covariates at later stages can result in nonignorable missing pseudo-outcomes at earlier stages, leading to suboptimal DTRs, even if the longitudinal outcome variables are fully observed. To address this unique missing data problem in DTR settings, we propose 2 weighted Q-learning approaches where inverse probability weights for missingness of the pseudo-outcomes are obtained through estimating equations with valid nonresponse instrumental variables or sensitivity analysis. The asymptotic properties of the weighted Q-learning estimators are derived, and the finite-sample performance of the proposed methods is evaluated and compared with alternative methods through extensive simulation studies. Using EMR data from the Medical Information Mart for Intensive Care database, we apply the proposed methods to investigate the optimal fluid strategy for sepsis patients in intensive care units.","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":"142943773","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

Regression to the mean for bivariate distributions. 二元分布的均值回归。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf033

Manzoor Khan, Jake Olivier

{"title":"Regression to the mean for bivariate distributions.","authors":"Manzoor Khan, Jake Olivier","doi":"10.1093/biomtc/ujaf033","DOIUrl":"10.1093/biomtc/ujaf033","url":null,"abstract":"Regression to the mean is said to have occurred when subjects having relatively high or low measurements are remeasured closer to the population mean. This phenomenon can influence the conclusion about the effectiveness of a treatment in a pre-post study design. The mean difference of the pre- and post-variables, conditioned on the initial variable being above or below a cut-point, is the sum of regression to the mean and treatment effects. Expressions for regression to the mean are available for the bivariate normal distribution under restrictive assumptions, and for the bivariate Poisson and binomial distributions, more generally. This article derives expressions for regression to the mean for any bivariate distribution while making fewer restrictive assumptions than previous methods. Maximum likelihood estimators are derived, and the unbiasedness, consistency, and asymptotic normality of these estimators are shown for exponential families, where possible. Data on the cholesterol levels in men aged 35-39 are used for decomposing the conditional mean difference in cholesterol level on pre-post occasions into regression to the mean and treatment effects. In another example, data on diastolic blood pressure for 341 patients are used to demonstrate the fraction of change due to regression to the mean and the treatment effects, respectively.","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":"143699241","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

High-dimensional partially linear functional Cox models. 高维部分线性泛函Cox模型。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae164

Xin Chen, Hua Liu, Jiaqi Men, Jinhong You

{"title":"High-dimensional partially linear functional Cox models.","authors":"Xin Chen, Hua Liu, Jiaqi Men, Jinhong You","doi":"10.1093/biomtc/ujae164","DOIUrl":"10.1093/biomtc/ujae164","url":null,"abstract":"As a commonly employed method for analyzing time-to-event data involving functional predictors, the functional Cox model assumes a linear relationship between the functional principal component (FPC) scores of the functional predictors and the hazard rates. However, in practical scenarios, such as our study on the survival time of kidney transplant recipients, this assumption often fails to hold. To address this limitation, we introduce a class of high-dimensional partially linear functional Cox models, which accommodates the non-linear effects of functional predictors on the response and allows for diverging numbers of scalar predictors and FPCs as the sample size increases. We employ the group smoothly clipped absolute deviation method to select relevant scalar predictors and FPCs, and use B-splines to obtain a smoothed estimate of the non-linear effect. The finite sample performance of the estimates is evaluated through simulation studies. The model is also applied to a kidney transplant dataset, allowing us to make inferences about the non-linear effects of functional predictors on patients' hazard rates, as well as to identify significant scalar predictors for long-term survival time.","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":"142977394","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

Jointly modeling means and variances for nonlinear mixed effects models with measurement errors and outliers. 具有测量误差和异常值的非线性混合效应模型的均值和方差联合建模。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujaf018

Qian Ye, Lang Wu, Viviane Dias Lima

引用次数: 0