Statistical Methods in Medical Research最新文献

{"title":"Response adaptive randomisation in clinical trials: Current practice, gaps and future directions.","authors":"Isabelle Wilson, Steven Julious, Christina Yap, Susan Todd, Munyaradzi Dimairo","doi":"10.1177/09622802251348183","DOIUrl":"https://doi.org/10.1177/09622802251348183","url":null,"abstract":"<p><p><b>Introduction:</b> Adaptive designs (ADs) offer clinical trials flexibility to modify design aspects based on accumulating interim data. Response adaptive randomisation (RAR) adjusts treatment allocation according to interim results, favouring promising treatments. Despite scientific appeal, RAR adoption lags behind other ADs. Understanding methods and applications could provide insights and resources and reveal future research needs. This study examines RAR application, trial results and achieved benefits, reporting gaps, statistical tools and concerns, while highlighting examples of effective practices. <b>Methods:</b> RAR trials with comparative efficacy, effectiveness or safety objectives, classified at least phase I/II, were identified via statistical literature, trial registries, statistical resources and researcher-knowledge. Search spanned until October 2023, including results until February 2024. Analysis was descriptive and narrative. <b>Results:</b> From 652 articles/trials screened, 65 planned RAR trials (11 platform trials) were identified, beginning in 1985 and gradually increasing through to 2023. Most trials were in oncology (25%) and drug-treatments (80%), with 63% led by US teams. Predominantly Phase II (62%) and multi-arm (63%), 85% used Bayesian methods, testing superiority hypotheses (86%). Binary outcomes appeared in 55%, with a median observation of 56 days. Bayesian RAR algorithms were applied in 83%. However, 71% of all trials lacked clear details on statistical implementation. Subgroup-level RAR was seen in 23% of trials. Allocation was restricted in 51%, and 88% was included a burn-in period. Most trials (85%) planned RAR alongside other adaptations. Of trials with results, 92% used RAR, but over 50% inadequately reported allocation changes. A mean 22% reduction in sample size was seen, with none over-allocating to ineffective arms. <b>Conclusion:</b> RAR has shown benefits in conditions like sepsis, COVID-19 and cancer, enhancing effective treatment allocation and saving resources. However, complexity, costs and simulation need limit wider adoption. This review highlights RAR's benefits and suggests enhancing statistical tools to encourage wider adoption in clinical research.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251348183"},"PeriodicalIF":1.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317876","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}

{"title":"Permutation tests for detecting treatment effect heterogeneity in cluster randomized trials.","authors":"Lara Maleyeff, Fan Li, Sebastien Haneuse, Rui Wang","doi":"10.1177/09622802251348999","DOIUrl":"https://doi.org/10.1177/09622802251348999","url":null,"abstract":"<p><p>Cluster randomized trials are widely used in healthcare research for the evaluation of intervention strategies. Beyond estimating the average treatment effect, it is often of interest to assess whether the treatment effect varies across subgroups. While conventional methods based on tests of interaction terms between treatment and covariates can be used to detect treatment effect heterogeneity in cluster randomized trials, they typically rely on parametric assumptions that may not hold in practice. Adapting existing permutation tests from individually randomized trials, however, requires conceptual clarification and modification due to the multiple possible interpretations of treatment effect heterogeneity in the cluster randomized trial context. In this work, we develop variations of permutation tests and clarify key causal definitions in order to assess treatment effect heterogeneity in cluster randomized trials. Our procedure enables investigators to simultaneously test for effect modification across a large number of covariates, while maintaining nominal type I error rates and reasonable power in simulation studies. In the Pain Program for Active Coping and Training (PPACT) study, the proposed methods are able to detect treatment effect heterogeneity that was not identified by conventional methods assessing treatment-covariate interactions.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251348999"},"PeriodicalIF":1.6,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317875","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}

{"title":"Multiply robust causal inference in the presence of an error-prone treatment.","authors":"Shaojie Wei, Qinpeng He, Wei Li, Zhi Geng","doi":"10.1177/09622802251338364","DOIUrl":"https://doi.org/10.1177/09622802251338364","url":null,"abstract":"<p><p>Numerous estimation procedures employed in causal inference often rely on accurately measured data. However, the prevalence of measurement errors in practical studies may yield biased effect estimates. It is common to employ validation samples to rectify such biases in the measurement error literature. This article focuses on the estimation of the average causal effect with a misclassified binary treatment in a primary population of interest. By leveraging a validation sample with covariates, an error-prone version of treatment and a true treatment recorded, we provide identifiability results under certain conditions. Building on identifiability, we explore three classes of estimators, each demonstrating consistency and asymptotic normality within distinct model sets. Furthermore, we propose a multiply robust estimation approach for the treatment effect based on the semiparametric theory framework. The multiply robust estimator retains consistent under any one of the listed model sets and achieves the semiparametric efficiency bound, provided all models are correct. We demonstrate the satisfactory performance of the proposed estimators through simulation studies and a real data analysis.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251338364"},"PeriodicalIF":1.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144286522","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}

{"title":"Flexible regression methods for estimating optimal individualized treatment regimes with scalar and functional covariates.","authors":"Kaidi Kong, Li Guan, Zhongzhan Zhang","doi":"10.1177/09622802251340259","DOIUrl":"https://doi.org/10.1177/09622802251340259","url":null,"abstract":"<p><p>In personalized medicine study, how to estimate the optimal individualized treatment regime based on available individual information is a fundamental problem. In recent years, functional data analysis has appeared extensively in medical research, while the optimal individualized treatment regime based on the combination of scalar covariates and functional covariates have rarely been studied and the only few studies are mostly conducted in the context of randomized trials. In this article, we propose a flexible regression-based approach in which the outcome variable is real-valued and the covariates contain multiple scalar covariates and a functional covariate. Our approach is applicable to both randomized trials and observational studies, and the convergence rates of the proposed optimal individualized treatment regime estimators are presented for both situations. Sufficient simulation studies and a real data analysis are conducted to justified the validity of our proposed method.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251340259"},"PeriodicalIF":1.6,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144249607","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}

{"title":"Assessing accuracy for multi-class classification when subclasses are involved.","authors":"Nan Nan, Lili Tian","doi":"10.1177/09622802251343600","DOIUrl":"https://doi.org/10.1177/09622802251343600","url":null,"abstract":"<p><p>Classifications that involve subclasses are common in many applied fields. \"Compound multi-class classification\" refers to the settings which involve three or more main classes and at least one of the main classes has multiple subclasses. In this paper, we propose an accuracy metric proper for \"compound <math><mi>M</mi></math>-class classification,\" namely \"hypervolume under compound <math><mrow><mi>R</mi><mi>O</mi><mi>C</mi></mrow></math> manifold <math><mo>(</mo><mi>H</mi><mi>U</mi><msub><mi>M</mi><mrow><mi>C</mi><mo>,</mo><mi>M</mi></mrow></msub><mo>)</mo></math>.\" The proposed <math><mi>H</mi><mi>U</mi><msub><mi>M</mi><mrow><mi>C</mi><mo>,</mo><mi>M</mi></mrow></msub></math> evaluates the overall accuracy of a biomarker measured on continuous scale correctly identifying <math><mi>M</mi></math> main classes without requiring specification of an ordering in terms of marker values for subclasses relative to each other within each main class. The probabilistic interpretation of <math><mi>H</mi><mi>U</mi><msub><mi>M</mi><mrow><mi>C</mi><mo>,</mo><mi>M</mi></mrow></msub></math> is analytically derived. A network-based computing algorithm which enables efficient computation of the empirical estimate of <math><mi>H</mi><mi>U</mi><msub><mi>M</mi><mrow><mi>C</mi><mo>,</mo><mi>M</mi></mrow></msub></math> is developed. Non-parametric bootstrap percentile confidence intervals of <math><mi>H</mi><mi>U</mi><msub><mi>M</mi><mrow><mi>C</mi><mo>,</mo><mi>M</mi></mrow></msub></math> are assessed through extensive simulation studies. Lastly, a real data example is included to illustrate the usage of our proposed method.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251343600"},"PeriodicalIF":1.6,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144226783","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}

{"title":"Partial areas under the curve of the cumulative distribution function as a new composite estimand for randomized clinical trials.","authors":"Masataka Taguri, Kenichi Hayashi","doi":"10.1177/09622802251314195","DOIUrl":"10.1177/09622802251314195","url":null,"abstract":"<p><p>Clinical trials often face the challenge of post-randomization events, such as the initiation of rescue therapy or the premature discontinuation of randomized treatment. Such events, called \"intercurrent events\" (ICEs) in ICH E9(R1), may influence the estimation and interpretation of treatment effects. According to ICH E9(R1), there are five strategies for handling ICEs. This study focuses on the composite strategy, which incorporates ICEs in the outcome of interest and defines the treatment effects using composite endpoints that combine the measured continuous variables and ICEs. An advantage of this strategy is that it avoids the occurrence of missing data because they are defined as part of the outcome of interest. In this study, we propose a new composite estimand: the difference in the partial areas under the curves (pAUCs) of the cumulative distribution function. While the pAUC is closely related to the trimmed mean approach proposed by Permutt and Li, it offers the advantage of allowing pre-specification of the cutoff value for a \"good\" response based on clinical considerations. This ensures that the pAUC can be calculated irrespective of the proportion of ICEs. We describe the causal interpretation of our method and its relationship with two other strategies (treatment policy and hypothetical strategies) using a potential outcome framework. We present simulation results in which our method performs reasonably well compared to several existing approaches in terms of type I error, power, and the proportion of undefined test statistics.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1097-1113"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062239","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}

{"title":"Analysis of recurrent events in cluster randomised trials: The PLEASANT trial case study.","authors":"Kelly Grant, Steven A Julious","doi":"10.1177/09622802251316972","DOIUrl":"10.1177/09622802251316972","url":null,"abstract":"<p><p>Recurrent events for many clinical conditions, such as asthma, can indicate poor health outcomes. Recurrent events data are often analysed using statistical methods such as Cox regression or negative binomial regression, suffering event or time information loss. This article re-analyses the preventing and lessening exacerbations of asthma in school-age children associated with a new term (PLEASANT) trial data as a case study, investigating the utility, extending recurrent events survival analysis methods to cluster randomised trials. A conditional frailty model is used, with the frailty term at the general practitioner practice level, accounting for clustering. A rare events bias adjustment is applied if few participants had recurrent events and truncation of small event risk sets is explored, to improve model accuracy. Global and event-specific estimates are presented, alongside a mean cumulative function plot to aid interpretation. The conditional frailty model global results are similar to PLEASANT results, but with greater precision (include time, recurrent events, within-participant dependence, and rare events adjustment). Event-specific results suggest an increasing risk reduction in medical appointments for the intervention group, in September-December 2013, as medical contacts increase over time. The conditional frailty model is recommended when recurrent events are a study outcome for clinical trials, including cluster randomised trials, to help explain changes in event risk over time, assisting clinical interpretation.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1079-1096"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080727","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}

{"title":"Permutation-based global rank test with adaptive weights for multiple primary endpoints.","authors":"Satoshi Yoshida, Yusuke Yamaguchi, Kazushi Maruo, Masahiko Gosho","doi":"10.1177/09622802251334886","DOIUrl":"10.1177/09622802251334886","url":null,"abstract":"<p><p>Multiple efficacy endpoints are investigated in clinical trials, and selecting the appropriate primary endpoints is key to the study's success. The global test is an analysis approach that can handle multiple endpoints without multiplicity adjustment. This test, which aggregates the statistics from multiple primary endpoints into a single statistic using weights for the statistical comparison, has been gaining increasing attention. A key consideration in the global test is determination of the weights. In this study, we propose a novel global rank test in which the weights for each endpoint are estimated based on the current study data to maximize the test statistic, and the permutation test is applied to control the type I error rate. Simulation studies conducted to compare the proposed test with other global tests show that the proposed test can control the type I error rate at the nominal level, regardless of the number of primary endpoints and correlations between endpoints. Additionally, the proposed test offers higher statistical powers when the efficacy is considerably different between endpoints or when endpoints are moderately correlated, such as when the correlation coefficient is greater than or equal to 0.5.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1254-1266"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080760","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}

{"title":"Rank-based estimators of global treatment effects for cluster randomized trials with multiple endpoints on different scales.","authors":"Emma Davies Smith, Vipul Jairath, Guangyong Zou","doi":"10.1177/09622802251338387","DOIUrl":"10.1177/09622802251338387","url":null,"abstract":"<p><p>Cluster randomized trials commonly employ multiple endpoints. When a single summary of treatment effects across endpoints is of primary interest, global methods represent a common analysis strategy. However, specification of the required joint distribution is non-trivial, particularly when the endpoints have different scales. We develop rank-based interval estimators for a global treatment effect referred to here as the \"global win probability, or the mean of multiple Wilcoxon Mann-Whitney probabilities, and interpreted as the probability that a treatment individual responds better than a control individual on average. Using endpoint-specific ranks among the combined sample and within each arm, each individual-level observation is converted to a \"win fraction\" which quantifies the proportion of wins experienced over every observation in the comparison arm. An individual's multiple observations are then replaced with a single \"global win fraction\" by averaging win fractions across endpoints. A linear mixed model is applied directly to the global win fractions to obtain point, variance, and interval estimates adjusted for clustering. Simulation demonstrates our approach performs well concerning confidence interval coverage and type I error, and methods are easily implemented using standard software. A case study using public data is provided with corresponding R and SAS code.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1267-1289"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080761","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}

{"title":"G-formula with multiple imputation for causal inference with incomplete data.","authors":"Jonathan W Bartlett, Camila Olarte Parra, Emily Granger, Ruth H Keogh, Erik W van Zwet, Rhian M Daniel","doi":"10.1177/09622802251316971","DOIUrl":"10.1177/09622802251316971","url":null,"abstract":"<p><p>G-formula is a popular approach for estimating the effects of time-varying treatments or exposures from longitudinal data. G-formula is typically implemented using Monte-Carlo simulation, with non-parametric bootstrapping used for inference. In longitudinal data settings missing data are a common issue, which are often handled using multiple imputation, but it is unclear how G-formula and multiple imputation should be combined. We show how G-formula can be implemented using Bayesian multiple imputation methods for synthetic data, and that by doing so, we can impute missing data and simulate the counterfactuals of interest within a single coherent approach. We describe how this can be achieved using standard multiple imputation software and explore its performance using a simulation study and an application from cystic fibrosis.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1130-1143"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753917","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}