Statistics in Medicine最新文献

Semiparametric Estimation of Relative Causal Effects in Randomized Controlled Trials With Noncompliance. 非依从性随机对照试验中相对因果效应的半参数估计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-06-01 DOI: 10.1002/sim.70153

Wenli Liu, Jing Qin, Yukun Liu

{"title":"Semiparametric Estimation of Relative Causal Effects in Randomized Controlled Trials With Noncompliance.","authors":"Wenli Liu, Jing Qin, Yukun Liu","doi":"10.1002/sim.70153","DOIUrl":"https://doi.org/10.1002/sim.70153","url":null,"abstract":"Randomized controlled trials (RCTs) are the gold standard for causal inference and are widely used. However, valid analyses of RCTs are often complicated by non-compliance, which can lead to confounding bias and biased causal effect estimation. The main challenge comes from compliance datasets in both the treatment and control groups both following two-component mixture models. The maximum nonparametric likelihood estimator is inconsistent in a two-component mixture model even if the mixture proportion and one of the components are completely known, but the other component is unknown. In this paper, we instead assume parametric models for the ratios of risks among compliers assigned treatment, never-takers and always-takers, and leave the baseline compliers not assigned treatment unspecified. We develop a novel two-step maximum likelihood estimation procedure by making full use of the observed covariates and latent compliance classes, which theoretically can produce asymptotic root <math> <semantics><mrow><mi>n</mi></mrow> <annotation>$$ n $$</annotation></semantics> </math> consistent estimators. In particular, our proposed estimator for the conditional local risk ratio always lies within the range of the parameter. Our numerical results show that the proposed method is generally more reliable than existing alternatives.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 13-14","pages":"e70153"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144249659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring Causal Effects of Hormone- and Radio-Treatments in an Observational Study of Breast Cancer Using Copula-Based Semi-Competing Risks Models. 在一项基于copula的半竞争风险模型的乳腺癌观察性研究中，探索激素和放射治疗的因果效应。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-06-01 DOI: 10.1002/sim.70131

Tonghui Yu, Mengjiao Peng, Yifan Cui, Elynn Chen, Chixiang Chen

{"title":"Exploring Causal Effects of Hormone- and Radio-Treatments in an Observational Study of Breast Cancer Using Copula-Based Semi-Competing Risks Models.","authors":"Tonghui Yu, Mengjiao Peng, Yifan Cui, Elynn Chen, Chixiang Chen","doi":"10.1002/sim.70131","DOIUrl":"https://doi.org/10.1002/sim.70131","url":null,"abstract":"Breast cancer patients may experience relapse or death after surgery during the follow-up period, leading to dependent censoring of relapse. This phenomenon, known as semi-competing risk, imposes challenges in analyzing treatment effects on breast cancer and necessitates advanced statistical tools for unbiased analysis. Despite progress in estimation and inference within semi-competing risks regression, its application to causal inference is still in its early stages. This article aims to propose a frequentist and semi-parametric framework based on copula models that can facilitate valid causal inference, net quantity estimation and interpretation, and sensitivity analysis for unmeasured factors under right-censored semi-competing risks data. We also propose novel procedures to enhance parameter estimation and its applicability in practice. After that, we apply the proposed framework to a breast cancer study and detect the time-varying causal effects of hormone- and radio-treatments on patients' relapse and overall survival. Moreover, extensive numerical evaluations demonstrate the method's feasibility, highlighting minimal estimation bias and reliable statistical inference.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 13-14","pages":"e70131"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144216987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bland-Altman Plot for Censored Variables. 删减变量的Bland-Altman图。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-06-01 DOI: 10.1002/sim.70147

Anne Lotz, Thomas Behrens, Karl-Heinz Jöckel, Dirk Taeger

引用次数: 0

Optimal Allocation of Observations in Stepped-Wedge and Other Cluster Studies With Correlated Cluster-Period Effects. 具有相关聚类周期效应的阶梯楔形和其他聚类研究中观测值的最优分配。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-06-01 DOI: 10.1002/sim.70100

Alan J Girling, Samuel I Watson

{"title":"Optimal Allocation of Observations in Stepped-Wedge and Other Cluster Studies With Correlated Cluster-Period Effects.","authors":"Alan J Girling, Samuel I Watson","doi":"10.1002/sim.70100","DOIUrl":"10.1002/sim.70100","url":null,"abstract":"Stepped-wedge studies usually entail regular sampling of clusters over time. Yet the precision of the treatment effect estimator can sometimes be improved if the regular sampling scheme is replaced by one with preferential allocation of observations to particular time-epochs within each cluster. We present some exact results for optimizing the allocation for a general experimental layout under a mixed effects model with a time-varying cluster-autocorrelation structure, together with an algorithm for generating optimal allocations. An index of cluster variation is introduced, an increasing function of both the intra-class correlation and the total sample size, which encapsulates the influence of cluster-level variation on the optimal allocation. For any specified layout there is a sampling scheme (the 'best natural allocation') that solves the optimization problem for all values of this index up to a threshold value which depends only on the cluster autocorrelations. Under such a scheme the treatment effect estimator is equal to a simple difference between the means of the treated and control observations. Best natural allocations stand alongside conventional parallel and cross-over designs in giving equal weight to observations from all participants, even under stepped-wedge layouts with irreversible interventions. When applied to a recent study of primary care training programmes in low- and middle- income countries (The REaCH study), the results lead to substantial reductions in total sample size, without loss of precision. For stepped-wedge layouts with block-exchangeable or time-decaying cluster autocorrelations, we present explicit conditions for the optimality of staircase-type sampling schemes, which can arise as best natural allocations in such cases.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 13-14","pages":"e70100"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12184762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Negative Control Outcome Adjustment in Early-Phase Randomized Trials: Estimating Vaccine Effects on Immune Responses in HIV Exposed Uninfected Infants. 早期随机试验的阴性对照结果调整：估计疫苗对暴露于HIV的未感染婴儿免疫反应的影响

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-06-01 DOI: 10.1002/sim.70142

Ethan Ashby, Bo Zhang, Genevieve G Fouda, Youyi Fong, Holly Janes

{"title":"Negative Control Outcome Adjustment in Early-Phase Randomized Trials: Estimating Vaccine Effects on Immune Responses in HIV Exposed Uninfected Infants.","authors":"Ethan Ashby, Bo Zhang, Genevieve G Fouda, Youyi Fong, Holly Janes","doi":"10.1002/sim.70142","DOIUrl":"https://doi.org/10.1002/sim.70142","url":null,"abstract":"Adjustment for prognostic baseline variables can reduce bias due to covariate imbalance and increase efficiency in randomized trials. While the use of covariate adjustment in late-phase trials is justified by favorable large-sample properties, it is seldom used in small, early-phase studies, due to uncertainty in which variables are prognostic and the potential for precision loss, type I error rate inflation, and undercoverage of confidence intervals. To address this problem, we consider adjustment for a valid negative control outcome (NCO), or an auxiliary post-randomization outcome believed to be completely unaffected by treatment but more highly correlated with the primary outcome than baseline covariates. We articulate the assumptions that permit adjustment for NCOs without producing post-randomization selection bias, and describe plausible data-generating models where NCO adjustment can improve upon adjustment for baseline covariates alone. In numerical experiments, we illustrate performance and provide practical recommendations regarding model selection and finite-sample variance corrections. We apply our methods to the reanalysis of two early-phase vaccine trials in HIV exposed uninfected (HEU) infants, where we demonstrate that adjustment for auxiliary post-baseline immunological parameters can enhance the precision of vaccine effect estimates relative to standard approaches that avoid adjustment or adjust for baseline covariates alone.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 13-14","pages":"e70142"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144258949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Seamless Hybrid Phase II/III Design With Bayesian Interim Subgroup Selection. 基于贝叶斯中间子群选择的无缝混合II/III阶段设计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-06-01 DOI: 10.1002/sim.70144

Benjamin Duputel, Nigel Stallard, François Montestruc, Sarah Zohar, Moreno Ursino

{"title":"A Seamless Hybrid Phase II/III Design With Bayesian Interim Subgroup Selection.","authors":"Benjamin Duputel, Nigel Stallard, François Montestruc, Sarah Zohar, Moreno Ursino","doi":"10.1002/sim.70144","DOIUrl":"10.1002/sim.70144","url":null,"abstract":"Population selection is a crucial subject in clinical development nowadays as personalized medicine is growing in interest. Evolution in biomarker scanning techniques allows for the composition and detection of sub-populations of interest when analyzing new drug responses in a disease. Seamless adaptive trials could allow for subgroup analysis with the selection of the most promising population at interim analysis. We propose a hybrid Bayesian design for seamless Phase II/III trials with binary and time-to-event outcomes for the first and second phases, respectively. In this work, at interim analysis, several prior distributions, including shrinkage prior, are compared to possibly select/discard a population, and a final test using a conditional error function as a combination method testing procedure to control the frequentist type I error is used. Simulation studies showed that the logistic regression model performs better than frequentist testing for the population selection problem when the subgroup should be selected. Shrinkage prior distributions tend to be more conservative than simpler normal distributions as studies that would have ended positively are stopped at interim analysis.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 13-14","pages":"e70144"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12134527/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144216985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Dimensional Multiresponse Partially Functional Linear Regression. 高维多响应部分泛函线性回归。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-06-01 DOI: 10.1002/sim.70140

Xiong Cai, Jiguo Cao, Xingyu Yan, Peng Zhao

{"title":"High-Dimensional Multiresponse Partially Functional Linear Regression.","authors":"Xiong Cai, Jiguo Cao, Xingyu Yan, Peng Zhao","doi":"10.1002/sim.70140","DOIUrl":"10.1002/sim.70140","url":null,"abstract":"We propose a new class of high-dimensional multiresponse partially functional linear regressions (MR-PFLRs) to investigate the relationship between scalar responses and a set of explanatory variables, which include both functional and scalar types. In this framework, both the dimensionality of the responses and the number of scalar covariates can diverge to infinity. To account for within-subject correlation, we develop a functional principal component analysis (FPCA)-based penalized weighted least squares estimation procedure. In this approach, the precision matrix is estimated using penalized likelihoods, and the regression coefficients are then estimated through the penalized weighted least squares method, with the precision matrix serving as the weight. This method allows for the simultaneous estimation of both functional and scalar regression coefficients, as well as the precision matrix, while identifying significant features. Under mild conditions, we establish the consistency, rates of convergence, and oracle properties of the proposed estimators. Simulation studies demonstrate the finite-sample performance of our estimation method. Additionally, the practical utility of the MR-PFLR model is showcased through an application to Alzheimer's disease neuroimaging initiative (ADNI) data.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 13-14","pages":"e70140"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12138746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144226773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","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 Two-Stage Method for Extending Inferences From a Collection of Trials. 从试验集合中扩展推论的两阶段方法。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-06-01 DOI: 10.1002/sim.70146

Nicole Schnitzler, Eloise Kaizar

{"title":"A Two-Stage Method for Extending Inferences From a Collection of Trials.","authors":"Nicole Schnitzler, Eloise Kaizar","doi":"10.1002/sim.70146","DOIUrl":"10.1002/sim.70146","url":null,"abstract":"When considering the effect a treatment will cause in a population of interest, we often look to evidence from randomized controlled trials. In settings where multiple trials on a treatment are available, we may wish to synthesize the trials' participant data to obtain causally interpretable estimates of the average treatment effect in a specific target population. Traditional meta-analytic approaches to synthesizing data from multiple studies estimate the average effect among the studies. The resulting estimate is often not causally interpretable in any population, much less a particular target population, due to heterogeneity in the effect of treatment across studies. Inspired by traditional two-stage meta-analytic methods and methods for extending inferences from a single study, we propose a two-stage approach to extending inferences from a collection of randomized controlled trials that can be used to obtain causally interpretable estimates of treatment effects in a target population when there is between-study heterogeneity in conditional average treatment effects. We first introduce a collection of assumptions under which the target population's average treatment effect is identifiable when conditional average treatment effects are heterogeneous across studies. We then introduce an estimator that utilizes weighting in two stages, taking a weighted average of study-specific estimates of the treatment effect in the target population. We assess the performance of our proposed approach through simulation studies and two applications: A multi-center randomized clinical trial studying a Hepatitis-C treatment and a collection of studies on a therapy treatment for symptoms of pediatric traumatic brain injury.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 13-14","pages":"e70146"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12138745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144226771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

False Discovery Rate Control for Confounder Selection Using Mirror Statistics. 使用镜像统计控制混杂选择的错误发现率。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-06-01 DOI: 10.1002/sim.70116

Kazuharu Harada, Masataka Taguri

{"title":"False Discovery Rate Control for Confounder Selection Using Mirror Statistics.","authors":"Kazuharu Harada, Masataka Taguri","doi":"10.1002/sim.70116","DOIUrl":"https://doi.org/10.1002/sim.70116","url":null,"abstract":"While data-driven confounder selection requires careful consideration, it is frequently employed in observational studies. Widely recognized criteria for confounder selection include the minimal-set approach, which involves selecting variables relevant to both treatment and outcome, and the union-set approach, which involves selecting variables associated with either treatment or outcome. These approaches are often implemented using heuristics and off-the-shelf statistical methods, where the degree of uncertainty may not be clear. In this paper, we focus on the false discovery rate (FDR) to measure uncertainty in confounder selection. We define the FDR specific to confounder selection and propose methods based on the mirror statistic, a recently developed approach for FDR control that does not rely on p-values. The proposed methods are p-value-free and require only the assumption of some symmetry in the distribution of the mirror statistic. It can be combined with sparse estimation and other methods that involve difficulties in deriving p-values. The properties of the proposed methods are investigated through exhaustive numerical experiments. Particularly in high-dimensional data scenarios, the proposed methods effectively control FDR and perform better than the p-value-based methods.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 13-14","pages":"e70116"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144216988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Information Borrowing From External Data in Clinical Trials: The Elastic Commensurate Prior Approach. 临床试验中从外部数据中获取动态信息：弹性相称先验方法。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-06-01 DOI: 10.1002/sim.70129

Jike Huang, Fan Jia, Jiaxuan Li, Wanqiu Xie, Zhiwei Rong, Lan Mi, Yuqin Song, Yan Hou

{"title":"Dynamic Information Borrowing From External Data in Clinical Trials: The Elastic Commensurate Prior Approach.","authors":"Jike Huang, Fan Jia, Jiaxuan Li, Wanqiu Xie, Zhiwei Rong, Lan Mi, Yuqin Song, Yan Hou","doi":"10.1002/sim.70129","DOIUrl":"https://doi.org/10.1002/sim.70129","url":null,"abstract":"Integrating external data into a clinical trial can introduce systematic bias in estimates and inflate the study's type I error due to differences in study design and enrollment criteria. Existing prior designs for information borrowing lack the ability to dynamically adjust the weight based on the similarity between concurrent and external data. To address this challenge, we thereby introduce a novel method called the elastic commensurate prior (ECP), which combines the commensurate prior with the elastic prior method. By dynamically adjusting the weight of external data using a measure of congruence, this method demonstrates strong performance in maintaining power while providing adequate type I error control across different scenarios, including congruence, approximate congruence, and incongruence between external and concurrent data. Compared to existing methods such as the modified power prior, meta-analytic-predictive (MAP) prior, robust MAP prior, non-informative prior, and fully informative prior, the ECP method is flexible and performs well across all settings. Furthermore, our method also allows for the integration of covariates in estimating data congruence for dynamic information borrowing, achieving both strong performance in power and adequate control of type I error. Overall, the ECP represents a promising option for leveraging external data in clinical trials, reducing costs by decreasing the sample size requirement, and thereby accelerating research and drug development timelines.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 13-14","pages":"e70129"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144226772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0