Statistical Methods in Medical Research最新文献_第10页

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

How to add baskets to an ongoing basket trial with information borrowing. 如何通过信息借阅将篮子添加到正在进行的篮子试验中。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251316961

Libby Daniells, Pavel Mozgunov, Helen Barnett, Alun Bedding, Thomas Jaki

{"title":"How to add baskets to an ongoing basket trial with information borrowing.","authors":"Libby Daniells, Pavel Mozgunov, Helen Barnett, Alun Bedding, Thomas Jaki","doi":"10.1177/09622802251316961","DOIUrl":"10.1177/09622802251316961","url":null,"abstract":"Basket trials test a single therapeutic treatment on several patient populations under one master protocol. A desirable adaptive design feature is the ability to incorporate new baskets to an ongoing trial. Limited basket sample sizes can result in reduced power and precision of treatment effect estimates, which could be amplified in added baskets due to the shorter recruitment time. While various Bayesian information borrowing techniques have been introduced to tackle the issue of small sample sizes, the impact of including new baskets into the borrowing model has yet to be investigated. We explore approaches for adding baskets to an ongoing trial under information borrowing. Basket trials have pre-defined efficacy criteria to determine whether the treatment is effective for patients in each basket. The efficacy criteria are often calibrated a-priori in order to control the basket-wise type I error rate to a nominal level. Traditionally, this is done under a null scenario in which the treatment is ineffective in all baskets, however, we show that calibrating under this scenario alone will not guarantee error control under alternative scenarios. We propose a novel calibration approach that is more robust to false decision making. Simulation studies are conducted to assess the performance of the approaches for adding a basket, which is monitored through type I error rate control and power. The results display a substantial improvement in power for a new basket, however, this comes with potential inflation of error rates. We show that this can be reduced under the proposed calibration procedure.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"717-734"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670959","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

On estimation of overall treatment effects in multiregional clinical trials under a discrete random effects model. 离散随机效应模型下多区域临床试验总体治疗效果的估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251319120

Shu-Han Wan, Hwa-Chi Liang, Hsiao-Hui Tsou, Hong-Dar Wu, Suojin Wang

{"title":"On estimation of overall treatment effects in multiregional clinical trials under a discrete random effects model.","authors":"Shu-Han Wan, Hwa-Chi Liang, Hsiao-Hui Tsou, Hong-Dar Wu, Suojin Wang","doi":"10.1177/09622802251319120","DOIUrl":"10.1177/09622802251319120","url":null,"abstract":"Multiregional clinical trials (MRCTs) have become a standard strategy for pharmaceutical product development worldwide. The heterogeneity of regional treatment effects is anticipated in an MRCT. For a two-group comparative study in an MRCT, patient assignments, including regional weights and treatment allocation ratios, are predetermined under the same protocol. In practice, the observed patient assignments at the final analysis stage are often not equal to the predetermined patient assignments, which may impact the accuracy of estimating the overall treatment effect and may lead to a biased estimator. In this study, we use a discrete random effects model (DREM) to account for the heterogeneous treatment effect across regions in an MRCT and propose a bias-adjusted estimator of the overall treatment effect through a naïve estimator conditioned on ancillary statistics based on the observed patient assignments at the final analysis stage in the trial. We also perform power analysis for the overall treatment effect and determine the overall sample size for the bias-adjusted estimator with the DREM. Results of simulation studies are given to illustrate applications of the proposed approach. Finally, we provide an example to demonstrate the implementation of the proposed approach.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"735-750"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670983","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

On the achievability of efficiency bounds for covariate-adjusted response-adaptive randomization. 协变量调整响应自适应随机化效率界的可达性。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-03-31 DOI: 10.1177/09622802251327689

Jiahui Xin, Wei Ma

{"title":"On the achievability of efficiency bounds for covariate-adjusted response-adaptive randomization.","authors":"Jiahui Xin, Wei Ma","doi":"10.1177/09622802251327689","DOIUrl":"https://doi.org/10.1177/09622802251327689","url":null,"abstract":"In the context of precision medicine, covariate-adjusted response-adaptive (CARA) randomization has garnered much attention from both academia and industry due to its benefits in providing ethical and tailored treatment assignments based on patients' profiles while still preserving favorable statistical properties. Recent years have seen substantial progress in inference for various adaptive experimental designs. In particular, research has focused on two important perspectives: how to obtain robust inference in the presence of model misspecification, and what the smallest variance, i.e., the efficiency bound, an estimator can achieve. Notably, Armstrong (2022) derived the asymptotic efficiency bound for any randomization procedure that assigns treatments depending on covariates and accrued responses, thus including CARA, among others. However, to the best of our knowledge, no existing literature has addressed whether and how this bound can be achieved under CARA. In this paper, by connecting two strands of adaptive randomization literature, namely robust inference and efficiency bound, we provide a definitive answer in an important practical scenario where only discrete covariates are observed and used for stratification. We consider a special type of CARA, i.e., a stratified version of doubly-adaptive biased coin design and prove that the stratified difference-in-means estimator achieves Armstrong (2022)'s efficiency bound, with possible ethical constraints on treatment assignments. Our work provides new insights and demonstrates the potential for more research on CARA designs that maximize efficiency while adhering to ethical considerations. Future studies could explore achieving the asymptotic efficiency bound for CARA with continuous covariates, which remains an open question.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251327689"},"PeriodicalIF":1.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754020","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

Long-term Dagum-power variance function frailty regression model: Application in health studies. 长期Dagum-power方差函数衰弱回归模型：在健康研究中的应用。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-03-01 Epub Date: 2025-02-12 DOI: 10.1177/09622802241304113

Agatha Sacramento Rodrigues, Patrick Borges

{"title":"Long-term Dagum-power variance function frailty regression model: Application in health studies.","authors":"Agatha Sacramento Rodrigues, Patrick Borges","doi":"10.1177/09622802241304113","DOIUrl":"10.1177/09622802241304113","url":null,"abstract":"Survival models with cure fractions, known as long-term survival models, are widely used in epidemiology to account for both immune and susceptible patients regarding a failure event. In such studies, it is also necessary to estimate unobservable heterogeneity caused by unmeasured prognostic factors. Moreover, the hazard function may exhibit a non-monotonic shape, specifically, an unimodal hazard function. In this article, we propose a long-term survival model based on a defective version of the Dagum distribution, incorporating a power variance function frailty term to account for unobservable heterogeneity. This model accommodates survival data with cure fractions and non-monotonic hazard functions. The distribution is reparameterized in terms of the cure fraction, with covariates linked via a logit link, allowing for direct interpretation of covariate effects on the cure fraction-an uncommon feature in defective approaches. We present maximum likelihood estimation for model parameters, assess performance through Monte Carlo simulations, and illustrate the model's applicability using two health-related datasets: severe COVID-19 in pregnant and postpartum women and patients with malignant skin neoplasms.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"407-439"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400106","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

Weighting methods for truncation by death in cluster-randomized trials. 聚类随机试验中死亡截断的加权方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-03-01 Epub Date: 2025-01-31 DOI: 10.1177/09622802241309348

Dane Isenberg, Michael O Harhay, Nandita Mitra, Fan Li

{"title":"Weighting methods for truncation by death in cluster-randomized trials.","authors":"Dane Isenberg, Michael O Harhay, Nandita Mitra, Fan Li","doi":"10.1177/09622802241309348","DOIUrl":"10.1177/09622802241309348","url":null,"abstract":"Patient-centered outcomes, such as quality of life and length of hospital stay, are the focus in a wide array of clinical studies. However, participants in randomized trials for elderly or critically and severely ill patient populations may have truncated or undefined non-mortality outcomes if they do not survive through the measurement time point. To address truncation by death, the survivor average causal effect has been proposed as a causally interpretable subgroup treatment effect defined under the principal stratification framework. However, the majority of methods for estimating the survivor average causal effect have been developed in the context of individually randomized trials. Only limited discussions have been centered around cluster-randomized trials, where methods typically involve strong distributional assumptions for outcome modeling. In this article, we propose two weighting methods to estimate the survivor average causal effect in cluster-randomized trials that obviate the need for potentially complicated outcome distribution modeling. We establish the requisite assumptions that address latent clustering effects to enable point identification of the survivor average causal effect, and we provide computationally efficient asymptotic variance estimators for each weighting estimator. In simulations, we evaluate our weighting estimators, demonstrating their finite-sample operating characteristics and robustness to certain departures from the identification assumptions. We illustrate our methods using data from a cluster-randomized trial to assess the impact of a sedation protocol on mechanical ventilation among children with acute respiratory failure.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"473-489"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068032","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