Statistical Methods in Medical Research最新文献_第6页

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

Optimising error rates in programmes of pilot and definitive trials using Bayesian statistical decision theory. 利用贝叶斯统计决策理论优化试点和最终试验方案的错误率。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-03-31 DOI: 10.1177/09622802251322987

Duncan T Wilson, Andrew Hall, Julia M Brown, Rebecca Ea Walwyn

{"title":"Optimising error rates in programmes of pilot and definitive trials using Bayesian statistical decision theory.","authors":"Duncan T Wilson, Andrew Hall, Julia M Brown, Rebecca Ea Walwyn","doi":"10.1177/09622802251322987","DOIUrl":"https://doi.org/10.1177/09622802251322987","url":null,"abstract":"Pilot trials are often conducted in advance of definitive trials to assess their feasibility and to inform their design. Although pilot trials typically collect primary endpoint data, preliminary tests of effectiveness have been discouraged given their typically low power. Power could be increased at the cost of a higher type I error rate, but there is little methodological guidance on how to determine the optimal balance between these operating characteristics. We consider a Bayesian decision-theoretic approach to this problem, introducing a utility function and defining an optimal pilot and definitive trial programme as that which maximises expected utility. We base utility on changes in average primary outcome, the cost of sampling, treatment costs, and the decision-maker's attitude to risk. We apply this approach to re-design OK-Diabetes, a pilot trial of a complex intervention with a continuous primary outcome with known standard deviation. We then examine how optimal programme characteristics vary with the parameters of the utility function. We find that the conventional approach of not testing for effectiveness in pilot trials can be considerably sub-optimal.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251322987"},"PeriodicalIF":1.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754028","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

G-formula with multiple imputation for causal inference with incomplete data. 不完全数据下的多重归因g公式。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-03-31 DOI: 10.1177/09622802251316971

Jonathan W Bartlett, Camila Olarte Parra, Emily Granger, Ruth H Keogh, Erik W van Zwet, Rhian M Daniel

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

Sequential design for paired ordinal categorical outcome. 配对有序分类结果的序贯设计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-03-31 DOI: 10.1177/09622802251322990

Baoshan Zhang, Yuan Wu

引用次数: 0

Estimand-based inference in the presence of long-term survivors. 在长期幸存者面前的基于估计的推断。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-03-31 DOI: 10.1177/09622802251327686

Yi-Cheng Tai, Weijing Wang, Martin T Wells

{"title":"Estimand-based inference in the presence of long-term survivors.","authors":"Yi-Cheng Tai, Weijing Wang, Martin T Wells","doi":"10.1177/09622802251327686","DOIUrl":"https://doi.org/10.1177/09622802251327686","url":null,"abstract":"In this article, we develop nonparametric inference methods for comparing survival data across two samples, beneficial for clinical trials of novel cancer therapies where long-term survival is critical. These therapies, including immunotherapies and other advanced treatments, aim to establish durable effects. They often exhibit distinct survival patterns such as crossing or delayed separation and potentially leveling-off at the tails of survival curves, violating the proportional hazards assumption and rendering the hazard ratio inappropriate for measuring treatment effects. Our methodology uses the mixture cure framework to separately analyze cure rates of long-term survivors and the survival functions of susceptible individuals. We evaluated a nonparametric estimator for the susceptible survival function in a one-sample setting. Under sufficient follow-up, it is expressed as a location-scale-shift variant of the Kaplan-Meier estimator. It retains desirable features of the Kaplan-Meier estimator, including inverse-probability-censoring weighting, product-limit estimation, self-consistency, and nonparametric efficiency. Under insufficient follow-up, it can be adapted by incorporating a suitable cure rate estimator. In the two-sample setting, in addition to using the difference in cure rates to measure long-term effects, we propose a graphical estimand to compare relative treatment effects on susceptible subgroups. This process, inspired by Kendall's tau, compares the order of survival times among susceptible individuals. Large-sample properties of the proposed methods are derived for inference and their finite-sample properties are evaluated through simulations. The methodology is applied to analyze digitized data from the CheckMate 067 trial.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251327686"},"PeriodicalIF":1.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753827","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 computationally efficient approach to false discovery rate control and power maximisation via randomisation and mirror statistic. 一种通过随机化和镜像统计实现错误发现率控制和功率最大化的高效计算方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-03-31 DOI: 10.1177/09622802251329768

Marco Molinari, Magne Thoresen

{"title":"A computationally efficient approach to false discovery rate control and power maximisation via randomisation and mirror statistic.","authors":"Marco Molinari, Magne Thoresen","doi":"10.1177/09622802251329768","DOIUrl":"https://doi.org/10.1177/09622802251329768","url":null,"abstract":"Simultaneously performing variable selection and inference in high-dimensional regression models is an open challenge in statistics and machine learning. The increasing availability of vast amounts of variables requires the adoption of specific statistical procedures to accurately select the most important predictors in a high-dimensional space, while controlling the false discovery rate (FDR) associated with the variable selection procedure. In this paper, we propose the joint adoption of the Mirror Statistic approach to FDR control, coupled with outcome randomisation to maximise the statistical power of the variable selection procedure, measured through the true positive rate. Through extensive simulations, we show how our proposed strategy allows us to combine the benefits of the two techniques. The Mirror Statistic is a flexible method to control FDR, which only requires mild model assumptions, but requires two sets of independent regression coefficient estimates, usually obtained after splitting the original dataset. Outcome randomisation is an alternative to data splitting that allows to generate two independent outcomes, which can then be used to estimate the coefficients that go into the construction of the Mirror Statistic. The combination of these two approaches provides increased testing power in a number of scenarios, such as highly correlated covariates and high percentages of active variables. Moreover, it is scalable to very high-dimensional problems, since the algorithm has a low memory footprint and only requires a single run on the full dataset, as opposed to iterative alternatives such as multiple data splitting.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251329768"},"PeriodicalIF":1.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754551","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

Biomarker-driven optimal designs for patient enrollment restriction. 生物标志物驱动的患者入组限制优化设计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-03-31 DOI: 10.1177/09622802251327690

Alessandro Baldi Antognini, Sara Cecconi, Rosamarie Frieri, Maroussa Zagoraiou

{"title":"Biomarker-driven optimal designs for patient enrollment restriction.","authors":"Alessandro Baldi Antognini, Sara Cecconi, Rosamarie Frieri, Maroussa Zagoraiou","doi":"10.1177/09622802251327690","DOIUrl":"https://doi.org/10.1177/09622802251327690","url":null,"abstract":"The rapidly developing field of personalized medicine is giving the opportunity to treat patients with a specific regimen according to their individual demographic, biological, or genomic characteristics, known also as biomarkers. While binary biomarkers simplify subgroup selection, challenges arise in the presence of continuous ones, which are often categorized based on data-driven quantiles. In the context of binary response trials for treatment comparisons, this paper proposes a method for determining the optimal cutoff of a continuous predictive biomarker to discriminate between sensitive and insensitive patients, based on their relative risk. We derived the optimal design to estimate such a cutoff, which requires a set of equality constraints that involve the unknown model parameters and the patients' biomarker values and are not directly attainable. To implement the optimal design, a novel covariate-adjusted response-adaptive randomization is introduced, aimed at sequentially minimizing the Euclidean distance between the current allocation and the optimum. An extensive simulation study shows the performance of the proposed approach in terms of estimation efficiency and variance of the estimated cutoff. Finally, we show the potential severe ethical impact of adopting the data-dependent median to identify the subpopulations.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251327690"},"PeriodicalIF":1.6,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754567","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