Statistical Methods in Medical Research最新文献_第5页

The relative efficiency of staircase and stepped wedge cluster randomised trial designs. 阶梯型和阶梯型楔形聚类随机试验设计的相对效率。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-02-16 DOI: 10.1177/09622802251317613

Kelsey L Grantham, Andrew B Forbes, Richard Hooper, Jessica Kasza

{"title":"The relative efficiency of staircase and stepped wedge cluster randomised trial designs.","authors":"Kelsey L Grantham, Andrew B Forbes, Richard Hooper, Jessica Kasza","doi":"10.1177/09622802251317613","DOIUrl":"10.1177/09622802251317613","url":null,"abstract":"The stepped wedge design is an appealing longitudinal cluster randomised trial design. However, it places a large burden on participating clusters by requiring all clusters to collect data in all periods of the trial. The staircase design may be a desirable alternative: treatment sequences consist of a limited number of measurement periods before and after the implementation of the intervention. In this article, we explore the relative efficiency of the stepped wedge design to several variants of the 'basic staircase' design, which has one control followed by one intervention period in each sequence. We model outcomes using linear mixed models and consider a sampling scheme where each participant is measured once. We first consider a basic staircase design embedded within the stepped wedge design, then basic staircase designs with either more clusters or larger cluster-period sizes, with the same total number of participants and with fewer total participants than the stepped wedge design. The relative efficiency of the designs depends on the intracluster correlation structure, correlation parameters and the trial configuration, including the number of sequences and cluster-period size. For a wide range of realistic trial settings, a basic staircase design will deliver greater statistical power than a stepped wedge design with the same number of participants, and in some cases, with even fewer total participants.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"701-716"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143433771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Simultaneous variable selection and estimation for a partially linear Cox model. 部分线性Cox模型的同时变量选择与估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251322988

Tingting Cai, Mengqi Xie, Tao Hu, Jianguo Sun

引用次数: 0

Generalized framework for identifying meaningful heterogenous treatment effects in observational studies: A parametric data-adaptive G-computation approach. 在观察性研究中识别有意义的异质性治疗效果的广义框架：参数数据自适应g计算方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-02-24 DOI: 10.1177/09622802251316969

Roch A Nianogo, Stephen O'Neill, Kosuke Inoue

{"title":"Generalized framework for identifying meaningful heterogenous treatment effects in observational studies: A parametric data-adaptive G-computation approach.","authors":"Roch A Nianogo, Stephen O'Neill, Kosuke Inoue","doi":"10.1177/09622802251316969","DOIUrl":"10.1177/09622802251316969","url":null,"abstract":"There has been a renewed interest in identifying heterogenous treatment effects (HTEs) to guide personalized medicine. The objective was to illustrate the use of a step-by-step transparent parametric data-adaptive approach (the generalized HTE approach) based on the G-computation algorithm to detect heterogenous subgroups and estimate meaningful conditional average treatment effects (CATE). The following seven steps implement the generalized HTE approach: Step 1: Select variables that satisfy the backdoor criterion and potential effect modifiers; Step 2: Specify a flexible saturated model including potential confounders and effect modifiers; Step 3: Apply a selection method to reduce overfitting; Step 4: Predict potential outcomes under treatment and no treatment; Step 5: Contrast the potential outcomes for each individual; Step 6: Fit cluster modeling to identify potential effect modifiers; Step 7: Estimate subgroup CATEs. We illustrated the use of this approach using simulated and real data. Our generalized HTE approach successfully identified HTEs and subgroups defined by all effect modifiers using simulated and real data. Our study illustrates that it is feasible to use a step-by-step parametric and transparent data-adaptive approach to detect effect modifiers and identify meaningful HTEs in an observational setting. This approach should be more appealing to epidemiologists interested in explanation.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"648-662"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Interval estimation for the Youden index of a continuous diagnostic test with verification biased data. 带有验证偏差数据的连续诊断测试尤登指数的区间估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251322989

Shirui Wang, Shuangfei Shi, Gengsheng Qin

{"title":"Interval estimation for the Youden index of a continuous diagnostic test with verification biased data.","authors":"Shirui Wang, Shuangfei Shi, Gengsheng Qin","doi":"10.1177/09622802251322989","DOIUrl":"10.1177/09622802251322989","url":null,"abstract":"In medical diagnostic studies, the Youden index plays a crucial role as a comprehensive measurement of the diagnostic test effectiveness, aiding in determining the optimal threshold values by maximizing the sum of sensitivity and specificity. However, in clinical practice, verification of true disease status might be partially missing and estimators based on partially validated subjects are usually biased. While verification bias-corrected estimation methods for the receiver operating characteristic curve have been widely studied, no such results have been specifically developed for the Youden index. In this paper, we propose bias-corrected interval estimation methods for the Youden index of a continuous test under the missing-at-random assumption. Based on four estimators (full imputation (FI), mean score imputation, inverse probability weighting, and the semiparametric efficient (SPE)) introduced by Alonzo and Pepe for handling verification bias, we develop multiple confidence intervals for the Youden index by applying bootstrap resampling and the method of variance estimates recovery (MOVER). Extensive simulation and real data studies show that when the disease model is correctly specified, MOVER-FI intervals yield better coverage probability. We also observe a tradeoff between methods when the verification proportion is low: Bootstrap approaches achieve higher accuracy, while MOVER approaches deliver greater precision. Remarkably, bootstrap-SPE interval exhibit appealing doubly robustness to model misspecification and perform adequately across almost all scenarios considered. Based on our findings, we recommend using the bootstrap-SPE intervals when the true disease model is unknown, and the MOVERws-FI interval if the true disease model can be well approximated.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"796-811"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Distribution-free control charts for mixed-type data based on rank of interpoint distances. 基于点间距离秩的混合型数据无分布控制图。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-02-10 DOI: 10.1177/09622802251316964

Guojun Liu, Jyun-You Chiang, Yajie Bai, Zhengcheng Mou

{"title":"Distribution-free control charts for mixed-type data based on rank of interpoint distances.","authors":"Guojun Liu, Jyun-You Chiang, Yajie Bai, Zhengcheng Mou","doi":"10.1177/09622802251316964","DOIUrl":"10.1177/09622802251316964","url":null,"abstract":"Multivariate control charts have found wide application in healthcare, yet they primarily cater to continuous or categorical variables. However, the emergence of mixed-type data has sparked interest in adapting traditional control charts to handle such complexity. Unfortunately, existing methods often struggle to effectively manage this complexity, particularly in scenarios with limited historical in-control data. In response, this article introduces three distribution-free control charts specifically designed for monitoring mixed-type processes. The proposed approach revolves around computing distances between observations and a specified point, thereby reducing the data to a single dimension. Subsequently, the ranks of these one-dimensional distances are leveraged to develop monitoring statistics. Furthermore, to facilitate dimensionality reduction, a novel distance measure tailored for mixed-type data is introduced. Extensive validation of our proposed method is conducted through comprehensive simulation experiments. Moreover, we demonstrate the practical applicability of the proposed method using an example related to heart disease.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"633-647"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Sample size determinations in four-level longitudinal cluster randomized trials with random slope. 随机斜率的四水平纵向聚类随机试验的样本量确定。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251321996

Priyanka Majumder, Siuli Mukhopadhyay, Bo Wang, Samiran Ghosh

{"title":"Sample size determinations in four-level longitudinal cluster randomized trials with random slope.","authors":"Priyanka Majumder, Siuli Mukhopadhyay, Bo Wang, Samiran Ghosh","doi":"10.1177/09622802251321996","DOIUrl":"10.1177/09622802251321996","url":null,"abstract":"Cluster or group randomized trials (CRTs) are increasingly used for behavioral as well as system-level interventions in many areas e.g. medicine, psychotherapy, policy, and health service research etc. Sample size determination for each level at the design stage is always a key requirement for any intervention trial including CRT. This work addresses this important issue for a four-level longitudinal CRT via detecting the intervention effect over time. A random intercept and random slope mixed effects linear regression model, including a time-by-intervention interaction is used for modeling. Closed-form expression of the power function and sample size for each level are determined to detect the interaction effect. Other than statistical power consideration, several other factors need attention while designing such CRTs. Optimal allocations accounting for subject attrition and cost constraints have been determined here. How sample size determination based on fixed and random slope models affects when between-subject variations in outcome are anticipated to be significant is also studied. The effect of ignoring cluster levels in a four-level CRT, which is often the case in the absence of an appropriate four-level model, is studied in details. Lastly, the proposed model is illustrated via a real-life human immunodeficiency virus prevention study conducted in the Bahamas.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"751-762"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 connection between covariate adjustment and stratified randomization in randomized clinical trials. 随机临床试验中协变量调整与分层随机化之间的联系。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251324764

Zhiwei Zhang

{"title":"A connection between covariate adjustment and stratified randomization in randomized clinical trials.","authors":"Zhiwei Zhang","doi":"10.1177/09622802251324764","DOIUrl":"10.1177/09622802251324764","url":null,"abstract":"The statistical efficiency of randomized clinical trials can be improved by incorporating information from baseline covariates (i.e. pre-treatment patient characteristics). This can be done in the design stage using stratified (permutated block) randomization or in the analysis stage through covariate adjustment. This article makes a connection between covariate adjustment and stratified randomization in a general framework where all regular, asymptotically linear estimators are identified as augmented estimators. From a geometric perspective, covariate adjustment can be viewed as an attempt to approximate the optimal augmentation function, and stratified randomization improves a given approximation by moving it closer to the optimal augmentation function. The efficiency benefit of stratified randomization is asymptotically equivalent to attaching an optimal augmentation term based on the stratification factor. In designing a trial with stratified randomization, it is not essential to include all important covariates in the stratification, because their prognostic information can be incorporated through covariate adjustment. Under stratified randomization, adjusting for the stratification factor only in data analysis is not expected to improve efficiency, and the key to efficient estimation is incorporating prognostic information from all important covariates. These observations are confirmed in a simulation study and illustrated using real clinical trial data.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"829-844"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Novel empirical likelihood method for the cumulative hazard ratio under stratified Cox models. 分层Cox模型下累积风险比的新经验似然方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-05-13 DOI: 10.1177/09622802251327688

Dazhi Zhao, Yichuan Zhao

引用次数: 0

Semi-parametric testing for ordinal treatment effects in time-to-event data via dynamic Dirichlet process mixtures of the inverse-Gaussian distribution. 通过反高斯分布的动态狄利克雷过程混合物对时间-事件数据中顺序处理效果的半参数检验。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251322986

Jonathan A Race, Amy S Ruppert, Yvonne Efebera, Michael L Pennell

{"title":"Semi-parametric testing for ordinal treatment effects in time-to-event data via dynamic Dirichlet process mixtures of the inverse-Gaussian distribution.","authors":"Jonathan A Race, Amy S Ruppert, Yvonne Efebera, Michael L Pennell","doi":"10.1177/09622802251322986","DOIUrl":"10.1177/09622802251322986","url":null,"abstract":"Time-to-event data often violate the proportional hazards assumption under which the log-rank test is optimal. Such violations are especially common in the sphere of biological and medical data where heterogeneity due to unmeasured covariates or time varying effects are common. A variety of parametric survival models have been proposed in the literature which make more appropriate assumptions on the hazard function, at least for certain applications. One such model is derived from the first hitting time paradigm which assumes that a subject's event time is determined by a latent stochastic process reaching a threshold value. Several random effects specifications of the first hitting time model have also been proposed which allow for better modeling of data with unmeasured covariates. We propose a Bayesian model which loosens assumptions on the mixing distribution inherent in the random effects first hitting time models currently in use and we do so in a manner which is ideally suited for testing for effects of ordinal treatment variables. We demonstrate via a simulation study that the proposed model has better power than log-rank based methods in detecting ordinal treatment effects in the presence of nonproportional hazards. Additionally, we show that the proposed model is almost as powerful as log-rank based methods when the proportional hazards assumption holds. We also apply the proposed methodology to two biomedical data sets: a toxicity study in rodents and an observational study of cancer patients.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"763-782"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12132803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

The joint quantile regression modeling of mixed ordinal and continuous responses with its application to an obesity risk data. 混合有序和连续响应的联合分位数回归模型及其在肥胖风险数据中的应用。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251316974

Hong-Xia Zhang, Yu-Zhu Tian, Yue Wang, Mao-Zai Tian

{"title":"The joint quantile regression modeling of mixed ordinal and continuous responses with its application to an obesity risk data.","authors":"Hong-Xia Zhang, Yu-Zhu Tian, Yue Wang, Mao-Zai Tian","doi":"10.1177/09622802251316974","DOIUrl":"10.1177/09622802251316974","url":null,"abstract":"In clinical medical health research, individual measurements sometimes appear as a mixture of ordinal and continuous responses. There are some statistical correlations between response indicators. Regarding the joint modeling of mixed responses, the effect of a set of explanatory variables on the conditional mean of mixed responses is usually studied based on a mean regression model. However, mean regression results tend to underperform for data with non-normal errors and outliers. Quantile regression (QR) offers not only robust estimates but also the ability to analyze the impact of explanatory variables on various quantiles of the response variable. In this paper, we propose a joint QR modeling approach for mixed ordinal and continuous responses and apply it to the analysis of a set of obesity risk data. Firstly, we construct the joint QR model for mixed ordinal and continuous responses based on multivariate asymmetric Laplace distribution and a latent variable model. Secondly, we perform parameter estimation of the model using a Markov chain Monte Carlo algorithm. Finally, Monte Carlo simulation and a set of obesity risk data analysis are used to verify the validity of the proposed model and method.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"663-682"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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