Statistical Methods in Medical Research最新文献

{"title":"Strategies to boost statistical efficiency in randomized oncology trials with primary time-to-event endpoints.","authors":"Alan D Hutson, Han Yu","doi":"10.1177/09622802251343599","DOIUrl":"https://doi.org/10.1177/09622802251343599","url":null,"abstract":"<p><p>Oncology clinical trials are increasingly expensive, necessitating efforts to streamline phase II and III trials to reduce costs and expedite treatment delivery. Randomization is often impractical in oncology trials due to small sample sizes and limited statistical power, leading to biased inferences. The FDA has recently published guidance documents encouraging the use of prognostic baseline measures to improve the precision of inferences around treatment effects. To address this, we propose an extension of Rosenbaum's exact testing method incorporating a variant of martingale residuals for right censored data. This method can dramatically improve the statistical power of the test comparing treatment arms given time-to-event endpoints as compared to the standard log-rank test. Additionally, the modification of the martingale residual provides a straightforward metric for summarizing treatment effect by quantifying the expected events per treatment arm at each time-point. This approach is illustrated using a phase II clinical trial in small cell lung cancer.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251343599"},"PeriodicalIF":1.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476715","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":"Multiple imputation for systematically missing effect modifiers in individual participant data meta-analysis.","authors":"Robert Thiesmeier, Scott M Hofer, Nicola Orsini","doi":"10.1177/09622802251348800","DOIUrl":"10.1177/09622802251348800","url":null,"abstract":"<p><p>Individual participant data (IPD) meta-analysis of randomised trials is a crucial method for detecting and investigating effect modifications in medical research. However, few studies have explored scenarios involving systematically missing data on discrete effect modifiers (EMs) in IPD meta-analyses with a limited number of trials. This simulation study examines the impact of systematic missing values in IPD meta-analysis using a two-stage imputation method. We simulated IPD meta-analyses of randomised trials with multiple studies that had systematically missing data on the EM. A multivariable Weibull survival model was specified to assess beneficial (Hazard Ratio (HR)<math><mo>=</mo></math>0.8), null (HR<math><mo>=</mo></math>1.0), and harmful (HR<math><mo>=</mo></math>1.2) treatment effects for low, medium, and high levels of an EM, respectively. Bias and coverage were evaluated using Monte-Carlo simulations. The absolute bias for common and heterogeneous effect IPD meta-analyses was less than 0.016 and 0.007, respectively, with coverage close to its nominal value across all EM levels. An uncongenial imputation model resulted in larger bias, even when the proportion of studies with systematically missing data on the EM was small. Overall, the proposed two-stage imputation approach provided unbiased estimates with improved precision. The assumptions and limitations of this approach are discussed.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251348800"},"PeriodicalIF":1.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333871","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":"Design optimization of longitudinal studies using metaheuristics: Application to lithium pharmacokinetics.","authors":"Mitchell Aaron Schepps, Jérémy Seurat, France Mentré, Weng Kee Wong","doi":"10.1177/09622802251350262","DOIUrl":"https://doi.org/10.1177/09622802251350262","url":null,"abstract":"<p><p>Lithium is recommended as a first line treatment for patients with bipolar disorder. However, only certain patients show a good response to the drug, and the variability and tolerability of lithium response are poorly understood. Greater precision in the early identification of individuals who are likely to respond well to lithium is a significant unmet clinical need. We create optimal designs to better understand the pharmacokinetic exposition of lithium for patients with and without a genetic covariate. From a Fisher information matrix based method, we find different optimal designs for estimating various parameters in a complicated pharmacokinetics/pharmacodynamics nonlinear mixed effects model with multiple physician specified constraints. Our approach uses flexible state-of-the-art metaheuristics to find various types of efficient designs, including multiple-objective optimal designs that can balance the competitiveness of the objectives and deliver higher efficiencies for more important objectives. Results from this article will be used as part of a broader study to implement efficient designs to better understand the exposition of sustained-release lithium in patients with bipolar disorder.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251350262"},"PeriodicalIF":1.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144326884","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":"Fast leave-one-cluster-out cross-validation using clustered network information criterion.","authors":"Jiaxing Qiu, Douglas E Lake, Pavel Chernyavskiy, Teague R Henry","doi":"10.1177/09622802251345486","DOIUrl":"https://doi.org/10.1177/09622802251345486","url":null,"abstract":"<p><p>For prediction models developed on clustered data that do not account for cluster heterogeneity in model parameterization, it is crucial to use cluster-based validation to assess model generalizability on unseen clusters. This article introduces a clustered estimator of the network information criterion to approximate leave-one-cluster-out deviance for standard prediction models with twice-differentiable log-likelihood functions. The clustered network information criterion serves as a fast alternative to cluster-based cross-validation. Stone proved that the Akaike information criterion is asymptotically equivalent to leave-one-observation-out cross-validation for true parametric models with independent and identically distributed observations. Ripley noted that the network information criterion, derived from Stone's proof, is a better approximation when the model is misspecified. For clustered data, we derived clustered network information criterion by substituting the Fisher information matrix in the network information criterion with a clustering-adjusted estimator. The clustered network information criterion imposes a greater penalty when the data exhibits stronger clustering, thereby allowing the clustered network information criterion to better prevent over-parameterization. In a simulation study and an empirical example, we used standard regression to develop prediction models for clustered data with Gaussian or binomial responses. Compared to the commonly used Akaike information criterion and Bayesian information criterion for standard regression, clustered network information criterion provides a much more accurate approximation to leave-one-cluster-out deviance and results in more accurate model size and variable selection, as determined by cluster-based cross-validation, especially when the data exhibit strong clustering.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251345486"},"PeriodicalIF":1.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144326885","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":"Incorporation of missing indicator with multiple imputation in propensity score analysis with partially observed covariates: A simulation study.","authors":"Sevinc Puren Yucel Karakaya, Ilker Unal","doi":"10.1177/09622802251338365","DOIUrl":"https://doi.org/10.1177/09622802251338365","url":null,"abstract":"<p><p>One of the primary challenges encountered in propensity score (PS) weighting is the presence of observations with missing covariates. In such cases, several potential solutions based on multiple imputation have been proposed. The most prevalent of these is the MI_te method, which combines treatment effect estimates derived from imputed datasets. A limited number of PS studies have incorporated the MI_te method with the missing indicator method; however, these studies only incorporated the missing indicator into the PS model. The aim of this simulation study is to propose two novel methods that incorporate the missing indicator approach with the MI_te. This incorporation either entails including the missing indicator into the outcome model (MIMI_o) or, alternatively, into both the outcome and PS model (MIMI_pso). The construction of the simulation scenarios was predicated on three elements: the mechanism of missing data, the type of treatment effect, and the presence of unmeasured confounding. In the presence of unmeasured confounding, the MIMI_pso method was the most effective method under the MAR mechanism. In the context of the MNAR mechanism, the method that exhibited the lowest bias was MIMI_o for homogeneous treatment effect and MIMI_pso for heterogeneous treatment effect. The MI_te method exhibited the highest levels of bias and variation. In view of the difficulties involved in identifying the mechanism of missing data, the variability in treatment effects across subgroups and the potential for unmeasured confounding variables in practice, researchers are encouraged to utilize the MIMI_pso method.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251338365"},"PeriodicalIF":1.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144326886","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":"Spatiotemporal effects on dengue incidence based on a large cluster randomized study.","authors":"Jerome Johnson, Xiangyu Yu, Suzanne M Dufault, Nicholas P Jewell","doi":"10.1177/09622802251338371","DOIUrl":"10.1177/09622802251338371","url":null,"abstract":"<p><p>A recent large-scale cluster randomized test-negative study assessed the impact of a mosquito-based intervention on the incidence of clinical dengue showing a protective efficacy of 77.1% (95% CI: (65.3%, 84.9%)). While the intervention was randomized at a cluster-level, human and mosquito movement suggest potential violations in assumptions necessary for intention-to-treat analyses to produce accurate estimates of the full intervention effect due to spatial clustering of dengue cases, and/or potential non-independence in the intervention arising from spillover of the intervention (or control) across cluster boundaries. We address these distinct but related effects using two approaches. First, we examine whether a clustering effect exists, that is, whether the presence of a recent dengue case in the sample within a specified distance from a residence raises the risk of dengue. Second, we use cluster reallocation techniques to examine intervention spillover effects. We find strong spatial effects of the presence of dengue cases on the risk of clinical dengue that exhibit both serospecificity and a dose response, more evident in control than intervention clusters at least on an additive scale. Contrarily, there is no evidence of any appreciable local spillover effect from intervention to control clusters, or vice versa, in terms of either the risk of dengue infection or the level of disease clustering.</p>","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251338371"},"PeriodicalIF":1.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333872","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":"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}