Biometrics最新文献_第9页

Design of platform trials with a change in the control treatment arm. 改变控制治疗臂的平台试验设计。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf073

Peter Greenstreet, Thomas Jaki, Alun Bedding, Pavel Mozgunov

{"title":"Design of platform trials with a change in the control treatment arm.","authors":"Peter Greenstreet, Thomas Jaki, Alun Bedding, Pavel Mozgunov","doi":"10.1093/biomtc/ujaf073","DOIUrl":"10.1093/biomtc/ujaf073","url":null,"abstract":"Platform trials are an efficient way of testing multiple treatments. We consider platform trials where, if a treatment is found to be superior to the control, it will become the new standard of care. The remaining treatments are then tested against this new control. In this setting, one can either keep the information on both the new standard of care and the other active treatments before the control is changed or discard this information when testing for benefit of the remaining treatments. We show analytically and numerically, retaining the information collected before the change in control can be detrimental to the power in a frequentist multi-arm multi-stage trial. Specifically, we consider the overall power, the probability that the active treatment with the greatest treatment effect is found during the trial, and the conditional power, the probability a given treatment is found superior against the current control. Also studied is the conditional type I error, the probability a given treatment is incorrectly found superior against the current control. We prove when retaining the information decreases both the overall and conditional power but also decreases the conditional type I error. A motivating example is then studied. Based on these observations, we discuss different aspects to consider when deciding whether to run a continuous platform trial or run an inherently new trial using the same trial infrastructure.","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/PMC12204708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332418","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 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

Regularized principal spline functions to mitigate spatial confounding. 正则化主样条函数以减轻空间混淆。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf076

Carlo Zaccardi, Pasquale Valentini, Luigi Ippoliti, Alexandra M Schmidt

{"title":"Regularized principal spline functions to mitigate spatial confounding.","authors":"Carlo Zaccardi, Pasquale Valentini, Luigi Ippoliti, Alexandra M Schmidt","doi":"10.1093/biomtc/ujaf076","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf076","url":null,"abstract":"This paper proposes a new approach to address the problem of unmeasured confounding in spatial designs. Spatial confounding occurs when some confounding variables are unobserved and not included in the model, leading to distorted inferential results about the effect of an exposure on an outcome. We show the relationship existing between the confounding bias of a non-spatial model and that of a semi-parametric model that includes a basis matrix to represent the unmeasured confounder conditional on the exposure. This relationship holds for any basis expansion; however, it is shown that using the semi-parametric approach guarantees a reduction in the confounding bias only under certain circumstances, which are related to the spatial structures of the exposure and the unmeasured confounder, the type of basis expansion utilized, and the regularization mechanism. To adjust for spatial confounding, and therefore try to recover the effect of interest, we propose a Bayesian semi-parametric regression model, where an expansion matrix of principal spline basis functions is used to approximate the unobserved factor, and spike-and-slab priors are imposed on the respective expansion coefficients in order to select the most important bases. From the results of an extensive simulation study, we conclude that our proposal is able to reduce the confounding bias more than competing approaches, and it also seems more robust to bias amplification.","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":"144494100","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 1.7 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":"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":1.7,"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

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

Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes. 在重复结果的结构嵌套平均模型中对效果修饰符选择的惩罚g估计。

IF 1.4 4区数学

Biometrics Pub Date : 2025-01-07 DOI: 10.1093/biomtc/ujae165

Ajmery Jaman, Guanbo Wang, Ashkan Ertefaie, Michèle Bally, Renée Lévesque, Robert W Platt, Mireille E Schnitzer

{"title":"Penalized G-estimation for effect modifier selection in a structural nested mean model for repeated outcomes.","authors":"Ajmery Jaman, Guanbo Wang, Ashkan Ertefaie, Michèle Bally, Renée Lévesque, Robert W Platt, Mireille E Schnitzer","doi":"10.1093/biomtc/ujae165","DOIUrl":"10.1093/biomtc/ujae165","url":null,"abstract":"Effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. Modeling these effect differences is important for etiological goals and for purposes of optimizing treatment. Structural nested mean models (SNMMs) are useful causal models for estimating the potentially heterogeneous effect of a time-varying exposure on the mean of an outcome in the presence of time-varying confounding. A data-adaptive selection approach is necessary if the effect modifiers are unknown a priori and need to be identified. Although variable selection techniques are available for estimating the conditional average treatment effects using marginal structural models or for developing optimal dynamic treatment regimens, all of these methods consider a single end-of-follow-up outcome. In the context of an SNMM for repeated outcomes, we propose a doubly robust penalized G-estimator for the causal effect of a time-varying exposure with a simultaneous selection of effect modifiers and prove the oracle property of our estimator. We conduct a simulation study for the evaluation of its performance in finite samples and verification of its double-robustness property. Our work is motivated by the study of hemodiafiltration for treating patients with end-stage renal disease at the Centre Hospitalier de l'Université de Montréal. We apply the proposed method to investigate the effect heterogeneity of dialysis facility on the repeated session-specific hemodiafiltration outcomes.","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":"142999234","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