首页 > 最新文献

BMC Medical Research Methodology最新文献

英文 中文
How to manage missing covariates in randomized controlled trials: a comparison of strategies. 如何管理随机对照试验中缺失的协变量:策略的比较。
IF 3.4 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-11-25 DOI: 10.1186/s12874-025-02708-w
Shiyu Zhang, Yajuan Si, John J Dziak

Background: When analyzing randomized controlled trials (RCTs) data, covariate adjustment is often employed to increase the precision of estimated treatment effects. Missing data in covariates, if not handled properly, can result in biased and inefficient estimates. However, the existing literature on handling missing covariate data is limited, and recommendations vary regarding a valid and efficient approach.

Methods: To help reconcile the seemingly inconsistent recommendations, we address two questions through methodological descriptions and simulated demonstrations. First, how should a multiple imputation (MI) model be specified for RCTs to best preserve the benefit of the randomization design? We consider three different approaches: MI with only baseline variables, "MI overall", and "MI by arm". Second, when and why will simple general strategies, such as grand mean imputation and the missing indicator method, perform as well as or better than MI in estimating treatment effects, and when and why do they fail?

Results: "MI by arm" has the potential to produce unbiased estimates for both the average and subgroup treatment effect (primary and secondary analyses) under the missing at random assumption. Strategies that capitalize on the randomization design, including MI with baseline variables, grand mean imputation, and the missing indicator method, may generate unbiased estimates for the average treatment effect (primary analysis) regardless of the missing data mechanism.

Conclusion: This article clarifies the assumptions and mechanisms by which different missing data strategies accommodate missingness in covariates and reconcile recommendations that sometimes appear contradictory in the literature. Under MAR, "MI by arm" produces unbiased estimates for both the average treatment effect and subgroup treatment effects. Leveraging the randomization design, "baseline-only MI", grand mean imputation, and the missing indicator method produce unbiased estimates for the average treatment effect, but biased subgroup treatment effects, regardless of the missing data mechanism.

背景:在分析随机对照试验(RCTs)数据时,通常采用协变量调整来提高估计治疗效果的精度。协变量中的缺失数据,如果处理不当,可能导致有偏差和低效的估计。然而,关于处理缺失协变量数据的现有文献是有限的,关于有效和有效的方法的建议各不相同。方法:为了帮助调和看似不一致的建议,我们通过方法描述和模拟演示来解决两个问题。首先,如何为随机对照试验指定多重输入(MI)模型以最好地保持随机化设计的好处?我们考虑了三种不同的方法:只有基线变量的心肌梗死,“整体心肌梗死”和“手臂心肌梗死”。其次,在估计治疗效果方面,简单的一般策略,如大均值imputation和缺失指标法,何时以及为什么会表现得与MI一样好或更好,以及何时以及为什么会失败?结果:在随机缺失假设下,“臂间心肌梗死”有可能对平均和亚组治疗效果(主要和次要分析)产生无偏估计。利用随机化设计的策略,包括具有基线变量的MI、大均值imputation和缺失指标法,可以对平均治疗效果(初级分析)产生无偏估计,而不管缺失的数据机制如何。结论:本文阐明了不同的缺失数据策略适应协变量缺失的假设和机制,并调和了文献中有时出现矛盾的建议。在MAR下,“臂间MI”对平均治疗效果和亚组治疗效果产生无偏估计。利用随机化设计,“仅基线MI”,大均值imputation和缺失指标法对平均治疗效果产生无偏估计,但对亚组治疗效果产生偏倚估计,无论缺失数据机制如何。
{"title":"How to manage missing covariates in randomized controlled trials: a comparison of strategies.","authors":"Shiyu Zhang, Yajuan Si, John J Dziak","doi":"10.1186/s12874-025-02708-w","DOIUrl":"10.1186/s12874-025-02708-w","url":null,"abstract":"<p><strong>Background: </strong>When analyzing randomized controlled trials (RCTs) data, covariate adjustment is often employed to increase the precision of estimated treatment effects. Missing data in covariates, if not handled properly, can result in biased and inefficient estimates. However, the existing literature on handling missing covariate data is limited, and recommendations vary regarding a valid and efficient approach.</p><p><strong>Methods: </strong>To help reconcile the seemingly inconsistent recommendations, we address two questions through methodological descriptions and simulated demonstrations. First, how should a multiple imputation (MI) model be specified for RCTs to best preserve the benefit of the randomization design? We consider three different approaches: MI with only baseline variables, \"MI overall\", and \"MI by arm\". Second, when and why will simple general strategies, such as grand mean imputation and the missing indicator method, perform as well as or better than MI in estimating treatment effects, and when and why do they fail?</p><p><strong>Results: </strong>\"MI by arm\" has the potential to produce unbiased estimates for both the average and subgroup treatment effect (primary and secondary analyses) under the missing at random assumption. Strategies that capitalize on the randomization design, including MI with baseline variables, grand mean imputation, and the missing indicator method, may generate unbiased estimates for the average treatment effect (primary analysis) regardless of the missing data mechanism.</p><p><strong>Conclusion: </strong>This article clarifies the assumptions and mechanisms by which different missing data strategies accommodate missingness in covariates and reconcile recommendations that sometimes appear contradictory in the literature. Under MAR, \"MI by arm\" produces unbiased estimates for both the average treatment effect and subgroup treatment effects. Leveraging the randomization design, \"baseline-only MI\", grand mean imputation, and the missing indicator method produce unbiased estimates for the average treatment effect, but biased subgroup treatment effects, regardless of the missing data mechanism.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"264"},"PeriodicalIF":3.4,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12649034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145602079","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
Improving the efficiency of drug resistant tuberculosis treatment trials: a time-to-event alternative marker for bacteriological response and adaptive minimization for randomization. 提高耐药结核病治疗试验的效率:细菌反应的时间-事件替代标记和随机化的适应性最小化。
IF 3.4 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-11-25 DOI: 10.1186/s12874-025-02697-w
Elise De Vos, Annelies Van Rie, Steven Abrams

Background: Establishing the efficacy of new treatments for rifampicin-resistant tuberculosis (RR-TB) is challenging due to the long-term clinical endpoints of two-year relapse-free survival. This study aimed to evaluate the effect of an alternative indicator of treatment response on sample size requirements and the use of a minimization strategy for randomization.

Methods: Sample size estimates were compared when based on the commonly used endpoint of the proportion of patients achieving stable culture conversion (SCC) at 12 weeks versus a novel but corresponding indicator of treatment response based on a model of changes in mycobacterial load (MBL) over time. The non-linear mixed effects model, calibrated using data from a RR-TB cohort in the same setting, included a longitudinal MBL decline, a probabilistic component for mycobacteria presence in sputum, and a time-to-event model for culture positivity. Data were simulated for a prespecified treatment effect to compare the power of detecting the treatment effect for various sample sizes when using the commonly used endpoint and alternative indicator of treatment response. Additionally, the impact of random patient allocation versus a minimization strategy for randomization on covariate imbalance was assessed.

Results: To achieve 80% power, 410 individuals were needed using the commonly used endpoint versus 110 participants when using the non-linear mixed effects model, corresponding to a 73% reduction in sample size. A small sample size results in high baseline covariate imbalance with random treatment group allocation, with a median relative imbalance of 0.104 for 110 participants versus 0.053 for 410 participants. This imbalance was reduced to 0.036 for 110 participants when an adaptive minimization procedure was implemented.

Conclusion: Using a model of mycobacterial burden changes over time as an alternative indicator of treatment response, combined with a minimization procedure during the randomization process, significantly reduced the sample size which could, if validated, enhance the efficiency of RR-TB clinical trial design.

背景:由于两年无复发生存期这一长期临床终点,确立利福平耐药结核病(RR-TB)新疗法的疗效具有挑战性。本研究旨在评估治疗反应的替代指标对样本量要求的影响,并使用最小化随机化策略。方法:根据12周时达到稳定培养转化(SCC)的患者比例的常用终点与基于分枝杆菌负荷(MBL)随时间变化模型的新的但相应的治疗反应指标,比较样本量估计。非线性混合效应模型使用同一环境下RR-TB队列的数据进行校准,包括MBL的纵向下降,痰中分枝杆菌存在的概率成分,以及培养阳性的时间-事件模型。模拟预先指定的治疗效果的数据,以比较在使用常用终点和治疗反应的替代指标时检测不同样本量的治疗效果的能力。此外,还评估了随机患者分配与最小化随机化策略对协变量失衡的影响。结果:为了达到80%的功效,使用常用终点时需要410人,而使用非线性混合效应模型时需要110人,相应的样本量减少了73%。小样本量导致随机处理组分配的高基线协变量不平衡,110名参与者的中位相对不平衡为0.104,而410名参与者的中位相对不平衡为0.053。当实施自适应最小化程序时,110名参与者的这种不平衡减少到0.036。结论:使用分枝杆菌负担随时间变化的模型作为治疗反应的替代指标,结合随机化过程中的最小化程序,可以显着减少样本量,如果验证,可以提高RR-TB临床试验设计的效率。
{"title":"Improving the efficiency of drug resistant tuberculosis treatment trials: a time-to-event alternative marker for bacteriological response and adaptive minimization for randomization.","authors":"Elise De Vos, Annelies Van Rie, Steven Abrams","doi":"10.1186/s12874-025-02697-w","DOIUrl":"10.1186/s12874-025-02697-w","url":null,"abstract":"<p><strong>Background: </strong>Establishing the efficacy of new treatments for rifampicin-resistant tuberculosis (RR-TB) is challenging due to the long-term clinical endpoints of two-year relapse-free survival. This study aimed to evaluate the effect of an alternative indicator of treatment response on sample size requirements and the use of a minimization strategy for randomization.</p><p><strong>Methods: </strong>Sample size estimates were compared when based on the commonly used endpoint of the proportion of patients achieving stable culture conversion (SCC) at 12 weeks versus a novel but corresponding indicator of treatment response based on a model of changes in mycobacterial load (MBL) over time. The non-linear mixed effects model, calibrated using data from a RR-TB cohort in the same setting, included a longitudinal MBL decline, a probabilistic component for mycobacteria presence in sputum, and a time-to-event model for culture positivity. Data were simulated for a prespecified treatment effect to compare the power of detecting the treatment effect for various sample sizes when using the commonly used endpoint and alternative indicator of treatment response. Additionally, the impact of random patient allocation versus a minimization strategy for randomization on covariate imbalance was assessed.</p><p><strong>Results: </strong>To achieve 80% power, 410 individuals were needed using the commonly used endpoint versus 110 participants when using the non-linear mixed effects model, corresponding to a 73% reduction in sample size. A small sample size results in high baseline covariate imbalance with random treatment group allocation, with a median relative imbalance of 0.104 for 110 participants versus 0.053 for 410 participants. This imbalance was reduced to 0.036 for 110 participants when an adaptive minimization procedure was implemented.</p><p><strong>Conclusion: </strong>Using a model of mycobacterial burden changes over time as an alternative indicator of treatment response, combined with a minimization procedure during the randomization process, significantly reduced the sample size which could, if validated, enhance the efficiency of RR-TB clinical trial design.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"265"},"PeriodicalIF":3.4,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12649012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145602218","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
Use of quasi-experimental studies to evaluate causal effects of public health interventions in Portugal: a scoping review. 使用准实验研究评估葡萄牙公共卫生干预措施的因果影响:范围审查。
IF 3.4 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-11-24 DOI: 10.1186/s12874-025-02701-3
A Leite, I Kislaya, A Machado, P Aguiar, B Nunes, C Matias Dias

Background: Quasi-experimental designs are a valid option to assess causal effects of public health interventions when randomized studies are unfeasible, but not widely used in Portugal. We identified and reviewed characteristics of studies employing quasi-experimental designs to evaluate causal effects of public health interventions in Portugal.

Methods: PubMed, Scopus, Web of Science and CINHAL were searched, alongside grey literature, reference mining and contact of authors of eligible studies. We extracted information on the intervention assessed, study design, outcomes assessed, statistical analysis and reporting guidelines.

Results: We identified 1143 studies; 25 were eligible. Studies assessed interventions in various areas, mainly healthcare services (28.0%), drugs/tobacco consumption policy (20.0%), and COVID-19 related restrictions (20.0%). Studies employed interrupted time series (56.0%) and difference-in-differences designs (44.0%). Analyses utilised regression-based models, namely linear (48.0%), negative binominal (20.0%) and logistic (12.0%). Studies analysed 53 outcomes, with two outcomes per study on average. No reporting guidelines were mentioned.

Conclusions: There is a limited number of studies using quasi-experimental designs to estimate the causal effects of public health interventions in Portugal, mainly interrupted time series and difference-in-differences. Training in this area might promote the adequate use and dissemination of quasi-experimental studies.

背景:当随机研究不可行时,准实验设计是评估公共卫生干预因果效应的有效选择,但在葡萄牙没有广泛使用。我们确定并回顾了采用准实验设计来评估葡萄牙公共卫生干预措施因果效应的研究特征。方法:检索PubMed、Scopus、Web of Science和CINHAL,并对符合条件的研究进行灰色文献、参考文献挖掘和作者联系。我们提取了有关干预评估、研究设计、结果评估、统计分析和报告指南的信息。结果:我们确定了1143项研究;25人符合条件。研究评估了各个领域的干预措施,主要是医疗保健服务(28.0%)、药物/烟草消费政策(20.0%)和与COVID-19相关的限制(20.0%)。研究采用中断时间序列(56.0%)和差中差设计(44.0%)。分析使用基于回归的模型,即线性(48.0%)、负二项(20.0%)和逻辑(12.0%)。研究分析了53个结果,平均每个研究有两个结果。没有提到报告准则。结论:使用准实验设计来估计葡萄牙公共卫生干预措施的因果效应的研究数量有限,主要是时间序列中断和差异中的差异。这方面的培训可以促进准实验研究的充分利用和传播。
{"title":"Use of quasi-experimental studies to evaluate causal effects of public health interventions in Portugal: a scoping review.","authors":"A Leite, I Kislaya, A Machado, P Aguiar, B Nunes, C Matias Dias","doi":"10.1186/s12874-025-02701-3","DOIUrl":"10.1186/s12874-025-02701-3","url":null,"abstract":"<p><strong>Background: </strong>Quasi-experimental designs are a valid option to assess causal effects of public health interventions when randomized studies are unfeasible, but not widely used in Portugal. We identified and reviewed characteristics of studies employing quasi-experimental designs to evaluate causal effects of public health interventions in Portugal.</p><p><strong>Methods: </strong>PubMed, Scopus, Web of Science and CINHAL were searched, alongside grey literature, reference mining and contact of authors of eligible studies. We extracted information on the intervention assessed, study design, outcomes assessed, statistical analysis and reporting guidelines.</p><p><strong>Results: </strong>We identified 1143 studies; 25 were eligible. Studies assessed interventions in various areas, mainly healthcare services (28.0%), drugs/tobacco consumption policy (20.0%), and COVID-19 related restrictions (20.0%). Studies employed interrupted time series (56.0%) and difference-in-differences designs (44.0%). Analyses utilised regression-based models, namely linear (48.0%), negative binominal (20.0%) and logistic (12.0%). Studies analysed 53 outcomes, with two outcomes per study on average. No reporting guidelines were mentioned.</p><p><strong>Conclusions: </strong>There is a limited number of studies using quasi-experimental designs to estimate the causal effects of public health interventions in Portugal, mainly interrupted time series and difference-in-differences. Training in this area might promote the adequate use and dissemination of quasi-experimental studies.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"263"},"PeriodicalIF":3.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12642283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145596021","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
Estimating standard deviation via sample mean extended quantile estimation. 通过样本均值扩展分位数估计估计标准差。
IF 3.4 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-11-24 DOI: 10.1186/s12874-025-02711-1
Mediya Bawakhan Mrakhan, Tamás Kói
{"title":"Estimating standard deviation via sample mean extended quantile estimation.","authors":"Mediya Bawakhan Mrakhan, Tamás Kói","doi":"10.1186/s12874-025-02711-1","DOIUrl":"10.1186/s12874-025-02711-1","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":"266"},"PeriodicalIF":3.4,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12659276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145596000","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
Randomization in the age of platform trials: unexplored challenges and some potential solutions. 平台试验时代的随机化:未探索的挑战和一些潜在的解决方案。
IF 3.4 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-11-22 DOI: 10.1186/s12874-025-02693-0
Olga Kuznetsova, Jennifer Ross, Daniel Bodden, Freda Cooner, Jonathan Chipman, Peter Jacko, Johannes Krisam, Yuqun Abigail Luo, Tobias Mielke, David S Robertson, Yevgen Ryeznik, Sofia S Villar, Wenle Zhao, Oleksandr Sverdlov

While platform trials have several benefits with their adaptive features, randomization challenges become of central relevance to the design and execution of a platform trial. This paper intends to address these challenges and explore some potential solutions. A platform type of clinical trial is a clinical trial design where multiple interventions are investigated simultaneously often against partly or fully shared controls, with new treatment arms added and completed treatment arms removed. Unequal allocation is often used in platform trials to improve statistical efficiency, deliver benefits to trial participants, and control the speed of enrollment in different treatment arms. Changes to the allocation ratio may be required after an interim analysis even when the number of treatment arms remains constant, for example, in a platform trial with response-adaptive randomization. To deliver the design efficiencies promised by the carefully optimized allocation ratio or simply to ensure a pre-determined allocation ratio, randomization methods that keep allocation proportions close to the target allocation ratio throughout randomization are helpful. Other situations commonly occurring in platform trials require special considerations for randomization methods and in some cases new classes of randomization methods. Such specific platform features include the requirement to accommodate differences in eligibility for different treatments, the need to ensure partial blinding with a 2-step randomization when mode of administration for different interventions is conspicuously different and full blinding is unfeasible, the objective to balance through dynamic randomization multiple prognostic factors or the need to accommodate limited drug supplies at the numerous trial centers, among others. The key to a successful execution of a complex randomization in the platform trial is the expert design of the Interactive Response Technology (IRT) system, where the system is built at the master protocol level and existing and potential randomization needs are incorporated from the outset. An additional, often overlooked, challenge when working with unequal allocation ratios and randomization methods to attain these, is the importance of preserving the unconditional allocation ratio at every allocation. Failure to do so might lead to a selection and evaluation bias even in double-blind trials, accidental bias, and reduced power of the re-randomization test.

虽然平台试验具有自适应特性,但随机化挑战成为平台试验设计和执行的核心问题。本文旨在解决这些挑战,并探讨一些潜在的解决方案。平台型临床试验是一种临床试验设计,其中同时调查多种干预措施,通常针对部分或完全共享的对照,增加新的治疗组并删除已完成的治疗组。不平等分配常用于平台试验,以提高统计效率,使试验参与者受益,并控制不同治疗组的入组速度。在中期分析后,即使治疗组数量保持不变,也可能需要改变分配比例,例如,在响应自适应随机化的平台试验中。为了实现精心优化的分配比例所承诺的设计效率,或者仅仅是为了确保预先确定的分配比例,在随机化过程中使分配比例接近目标分配比例的随机化方法是有帮助的。在平台试验中常见的其他情况需要特别考虑随机化方法,在某些情况下需要新的随机化方法。这些特定的平台特征包括:需要适应不同治疗的资格差异;当不同干预措施的给药模式明显不同且完全盲化不可行的时候,需要确保采用两步随机化的部分盲化;通过动态随机化平衡多种预后因素的目标;或需要适应众多试验中心有限的药物供应等。在平台试验中成功执行复杂随机化的关键是交互式响应技术(IRT)系统的专家设计,该系统建立在主协议级别,从一开始就纳入了现有和潜在的随机化需求。在使用不平等分配比例和随机化方法来实现这些目标时,另一个经常被忽视的挑战是,在每次分配中保持无条件分配比例的重要性。如果不这样做,即使在双盲试验中也可能导致选择和评估偏倚、意外偏倚和再随机化试验的效力降低。
{"title":"Randomization in the age of platform trials: unexplored challenges and some potential solutions.","authors":"Olga Kuznetsova, Jennifer Ross, Daniel Bodden, Freda Cooner, Jonathan Chipman, Peter Jacko, Johannes Krisam, Yuqun Abigail Luo, Tobias Mielke, David S Robertson, Yevgen Ryeznik, Sofia S Villar, Wenle Zhao, Oleksandr Sverdlov","doi":"10.1186/s12874-025-02693-0","DOIUrl":"10.1186/s12874-025-02693-0","url":null,"abstract":"<p><p>While platform trials have several benefits with their adaptive features, randomization challenges become of central relevance to the design and execution of a platform trial. This paper intends to address these challenges and explore some potential solutions. A platform type of clinical trial is a clinical trial design where multiple interventions are investigated simultaneously often against partly or fully shared controls, with new treatment arms added and completed treatment arms removed. Unequal allocation is often used in platform trials to improve statistical efficiency, deliver benefits to trial participants, and control the speed of enrollment in different treatment arms. Changes to the allocation ratio may be required after an interim analysis even when the number of treatment arms remains constant, for example, in a platform trial with response-adaptive randomization. To deliver the design efficiencies promised by the carefully optimized allocation ratio or simply to ensure a pre-determined allocation ratio, randomization methods that keep allocation proportions close to the target allocation ratio throughout randomization are helpful. Other situations commonly occurring in platform trials require special considerations for randomization methods and in some cases new classes of randomization methods. Such specific platform features include the requirement to accommodate differences in eligibility for different treatments, the need to ensure partial blinding with a 2-step randomization when mode of administration for different interventions is conspicuously different and full blinding is unfeasible, the objective to balance through dynamic randomization multiple prognostic factors or the need to accommodate limited drug supplies at the numerous trial centers, among others. The key to a successful execution of a complex randomization in the platform trial is the expert design of the Interactive Response Technology (IRT) system, where the system is built at the master protocol level and existing and potential randomization needs are incorporated from the outset. An additional, often overlooked, challenge when working with unequal allocation ratios and randomization methods to attain these, is the importance of preserving the unconditional allocation ratio at every allocation. Failure to do so might lead to a selection and evaluation bias even in double-blind trials, accidental bias, and reduced power of the re-randomization test.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":" ","pages":"268"},"PeriodicalIF":3.4,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145582110","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
Alternative tests and measures for between-study inconsistency in meta-analysis. 荟萃分析中研究间不一致性的替代检验和测量方法。
IF 3.4 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-11-20 DOI: 10.1186/s12874-025-02719-7
Zhiyuan Yu, Mengli Xiao, Xing Xing, Lifeng Lin

Meta-analysis is a widely used method for synthesizing results from multiple studies across diverse fields. A central challenge in meta-analysis is assessing between-study inconsistency, which can arise from differences in study populations, methodological heterogeneity, or the presence of outliers. Conventional tools such as the [Formula: see text] and [Formula: see text] statistics could be limited in power, especially when the number of studies is small or when the between-study distribution deviates from normality. To address these limitations, we propose a family of alternative [Formula: see text]-like statistics and a hybrid test that adaptively combines their strengths. We also introduce new measures to quantify inconsistency based on these statistics. Simulation studies demonstrate that the hybrid test performs robustly across a wide range of inconsistency patterns, including heavy-tailed, skewed, and contaminated distributions. We further illustrate the practical utility of our methods using three real-world meta-analyses. These approaches offer more flexible and powerful tools for detecting and quantifying inconsistency in meta-analytic practice.

荟萃分析是一种广泛使用的方法,用于综合不同领域的多项研究结果。荟萃分析的一个核心挑战是评估研究之间的不一致性,这可能源于研究群体的差异、方法的异质性或异常值的存在。传统的统计工具,如[公式:见文]和[公式:见文]的作用可能有限,特别是当研究数量很少或当研究间分布偏离正态时。为了解决这些限制,我们提出了一组替代的[公式:见文本]-像统计和混合测试,自适应地结合他们的优势。我们还介绍了基于这些统计数据的量化不一致性的新方法。仿真研究表明,混合测试在包括重尾分布、偏态分布和污染分布在内的广泛的不一致模式下都具有鲁棒性。我们使用三个真实世界的元分析进一步说明了我们的方法的实际效用。这些方法为元分析实践中的不一致性检测和量化提供了更灵活和强大的工具。
{"title":"Alternative tests and measures for between-study inconsistency in meta-analysis.","authors":"Zhiyuan Yu, Mengli Xiao, Xing Xing, Lifeng Lin","doi":"10.1186/s12874-025-02719-7","DOIUrl":"10.1186/s12874-025-02719-7","url":null,"abstract":"<p><p>Meta-analysis is a widely used method for synthesizing results from multiple studies across diverse fields. A central challenge in meta-analysis is assessing between-study inconsistency, which can arise from differences in study populations, methodological heterogeneity, or the presence of outliers. Conventional tools such as the [Formula: see text] and [Formula: see text] statistics could be limited in power, especially when the number of studies is small or when the between-study distribution deviates from normality. To address these limitations, we propose a family of alternative [Formula: see text]-like statistics and a hybrid test that adaptively combines their strengths. We also introduce new measures to quantify inconsistency based on these statistics. Simulation studies demonstrate that the hybrid test performs robustly across a wide range of inconsistency patterns, including heavy-tailed, skewed, and contaminated distributions. We further illustrate the practical utility of our methods using three real-world meta-analyses. These approaches offer more flexible and powerful tools for detecting and quantifying inconsistency in meta-analytic practice.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"261"},"PeriodicalIF":3.4,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12632047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145562839","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
Random survival forests for the analysis of recurrent events for right-censored data, with or without a terminal event. 随机生存森林,用于分析右删节数据的重复事件,有或没有终止事件。
IF 3.4 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-11-20 DOI: 10.1186/s12874-025-02678-z
Juliette Murris, Olivier Bouaziz, Michal Jakubczak, Sandrine Katsahian, Audrey Lavenu

Background: Random survival forests (RSF) have emerged as valuable tools in medical research. They have shown their utility in modelling complex relationships between predictors and survival outcomes, overcoming linearity or low dimensionality assumptions. Nevertheless, RSF have not been adapted to right-censored data with recurrent events (RE).

Methods: This work introduces RecForest, an extension of RSF and tailored for RE data, leveraging principles from survival analysis and ensemble learning. RecForest adapts the splitting rule to account for RE, with or without a terminal event, by employing the pseudo-score test or the Wald test derived from the marginal Ghosh-Lin model. The ensemble estimate is constructed by aggregating the expected number of events from each tree. Performance metrics involve a concordance index (C-index) tailored for RE analysis, along with an extension of the mean squared error (MSE). A comprehensive evaluation was conducted on both simulated and open-source data. We compared RecForest against the non-parametric mean cumulative function and the Ghosh-Lin model.

Results: Across the simulations and application, RecForest consistently outperforms, exhibiting C-index values ranging from 0.60 to 0.82 and lowest MSE metrics.

Conclusions: As analysing time-to-recurrence data is critical in medical research, the proposed method represents a valuable addition to the analytical toolbox in this domain. The RecForest implementation is publicly available as an R package on CRAN.

背景:随机生存森林(RSF)已成为医学研究中有价值的工具。它们在模拟预测因子和生存结果之间的复杂关系、克服线性或低维假设方面显示了它们的效用。然而,RSF还没有适应右审查的数据与复发事件(RE)。方法:本工作引入了RecForest,它是RSF的扩展,针对RE数据量身定制,利用了生存分析和集成学习的原理。RecForest通过采用从边际Ghosh-Lin模型导出的伪分数检验或Wald检验,调整分裂规则来考虑RE,无论是否有终端事件。集成估计是通过聚合来自每个树的预期事件数来构建的。性能指标包括为RE分析量身定制的一致性指数(C-index),以及均方误差(MSE)的扩展。对模拟数据和开源数据进行了综合评价。我们将RecForest与非参数平均累积函数和Ghosh-Lin模型进行了比较。结果:在模拟和应用过程中,RecForest始终表现优异,c -指数值在0.60至0.82之间,MSE指标最低。结论:由于分析复发时间数据在医学研究中至关重要,因此所提出的方法是对该领域分析工具箱的有价值的补充。RecForest实现在CRAN上以R包的形式公开提供。
{"title":"Random survival forests for the analysis of recurrent events for right-censored data, with or without a terminal event.","authors":"Juliette Murris, Olivier Bouaziz, Michal Jakubczak, Sandrine Katsahian, Audrey Lavenu","doi":"10.1186/s12874-025-02678-z","DOIUrl":"10.1186/s12874-025-02678-z","url":null,"abstract":"<p><strong>Background: </strong>Random survival forests (RSF) have emerged as valuable tools in medical research. They have shown their utility in modelling complex relationships between predictors and survival outcomes, overcoming linearity or low dimensionality assumptions. Nevertheless, RSF have not been adapted to right-censored data with recurrent events (RE).</p><p><strong>Methods: </strong>This work introduces RecForest, an extension of RSF and tailored for RE data, leveraging principles from survival analysis and ensemble learning. RecForest adapts the splitting rule to account for RE, with or without a terminal event, by employing the pseudo-score test or the Wald test derived from the marginal Ghosh-Lin model. The ensemble estimate is constructed by aggregating the expected number of events from each tree. Performance metrics involve a concordance index (C-index) tailored for RE analysis, along with an extension of the mean squared error (MSE). A comprehensive evaluation was conducted on both simulated and open-source data. We compared RecForest against the non-parametric mean cumulative function and the Ghosh-Lin model.</p><p><strong>Results: </strong>Across the simulations and application, RecForest consistently outperforms, exhibiting C-index values ranging from 0.60 to 0.82 and lowest MSE metrics.</p><p><strong>Conclusions: </strong>As analysing time-to-recurrence data is critical in medical research, the proposed method represents a valuable addition to the analytical toolbox in this domain. The RecForest implementation is publicly available as an R package on CRAN.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"262"},"PeriodicalIF":3.4,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12636200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145562852","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
Covariate selection strategies and estimands - a review of current practice of risk factor analysis from a causal perspective. 协变量选择策略和估计-从因果角度回顾当前风险因素分析的实践。
IF 3.4 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-11-19 DOI: 10.1186/s12874-025-02704-0
Ragna Reinhammar, Ingeborg Waernbaum
{"title":"Covariate selection strategies and estimands - a review of current practice of risk factor analysis from a causal perspective.","authors":"Ragna Reinhammar, Ingeborg Waernbaum","doi":"10.1186/s12874-025-02704-0","DOIUrl":"10.1186/s12874-025-02704-0","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"260"},"PeriodicalIF":3.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12629056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145548125","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
Could master protocols be adapted for effectiveness-implementation hybrid studies? 主方案能否适用于有效性-实施混合研究?
IF 3.4 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-11-18 DOI: 10.1186/s12874-025-02684-1
Justin J Chapman, Taren Massey-Swindle, Urska Arnautovska, Ingrid J Hickman, Amanda J Wheeler, Dan Siskind, Jeroen Deenik, Robert S Ware, James A Roberts, Yong Yi Lee, Alyssa Milton, Wolfgang Marx, Stephen J Wood, Zoe Rutherford, Catherine Kaylor-Hughes, Mike Trott, Ravi Iyer

Background: Master protocols leverage a common trial infrastructure for launching multiple sub-studies. Translational research aims to progress scientific discoveries toward public health impact, which depends on establishing an intervention's efficacy, effectiveness in real-world conditions, and successful strategies for implementation. While master protocols have been designed to improve the efficiency of clinical trials as sub-studies addressing a particular disease, their application with effectiveness-implementation hybrid studies is yet to be explored. The aim of this study was to develop recommendations for adapting mater protocol methods for effectiveness-implementation research.

Methods: A method of consultation with translational research networks was undertaken between January and December 2024. Consideration was given to the requirements for service providers to engage in translational research, and how master protocols could support effectiveness-implementation hybrid sub-studies. The underlying rationale for potential adaptations is provided with reference to implementation frameworks, discussion of advantages and disadvantages, and summary recommendations.

Results: Recommendations are proposed on establishing common trial infrastructure, aims and hypotheses, data collection, control groups, adaptive elements, and eligibility criteria. By leveraging cross-sectoral partnerships, co-producing research and dissemination, and incorporating adaptive elements, master protocols may offer a promising approach for accelerating progress along the translational research pipeline.

Conclusions: The adaptation of master protocols for hybrid sub-studies could enable evidence-based interventions to be more effectively implemented in routine care settings. The feasibility of master protocols for effectiveness-implementation research is yet to be tested, and further development in this area is needed to trial the proposed methodology.

背景:主协议利用一个通用的试验基础设施来启动多个子研究。转化研究旨在推动科学发现对公共卫生产生影响,这取决于确定干预措施的功效、在现实条件下的有效性以及成功的实施战略。虽然设计主方案是为了提高临床试验作为针对特定疾病的子研究的效率,但它们在有效性-实施混合研究中的应用仍有待探索。本研究的目的是制定建议,以适应物质协议方法的有效性实施研究。方法:于2024年1月至12月,采用与转化研究网络咨询的方法。考虑了服务提供者参与转化研究的要求,以及主协议如何支持有效性-实施混合子研究。通过参考实施框架、优缺点讨论和总结建议,提供了潜在调整的基本原理。结果:提出了建立共同试验基础设施、目标和假设、数据收集、对照组、适应性因素和资格标准的建议。通过利用跨部门伙伴关系,共同开展研究和传播,并纳入适应性因素,主协议可能为加快转化研究管道的进展提供一种有希望的方法。结论:混合子研究的主方案改编可以使循证干预更有效地在常规护理环境中实施。有效性执行研究的总协议的可行性还有待检验,需要在这一领域进一步发展,以试验拟议的方法。
{"title":"Could master protocols be adapted for effectiveness-implementation hybrid studies?","authors":"Justin J Chapman, Taren Massey-Swindle, Urska Arnautovska, Ingrid J Hickman, Amanda J Wheeler, Dan Siskind, Jeroen Deenik, Robert S Ware, James A Roberts, Yong Yi Lee, Alyssa Milton, Wolfgang Marx, Stephen J Wood, Zoe Rutherford, Catherine Kaylor-Hughes, Mike Trott, Ravi Iyer","doi":"10.1186/s12874-025-02684-1","DOIUrl":"10.1186/s12874-025-02684-1","url":null,"abstract":"<p><strong>Background: </strong>Master protocols leverage a common trial infrastructure for launching multiple sub-studies. Translational research aims to progress scientific discoveries toward public health impact, which depends on establishing an intervention's efficacy, effectiveness in real-world conditions, and successful strategies for implementation. While master protocols have been designed to improve the efficiency of clinical trials as sub-studies addressing a particular disease, their application with effectiveness-implementation hybrid studies is yet to be explored. The aim of this study was to develop recommendations for adapting mater protocol methods for effectiveness-implementation research.</p><p><strong>Methods: </strong>A method of consultation with translational research networks was undertaken between January and December 2024. Consideration was given to the requirements for service providers to engage in translational research, and how master protocols could support effectiveness-implementation hybrid sub-studies. The underlying rationale for potential adaptations is provided with reference to implementation frameworks, discussion of advantages and disadvantages, and summary recommendations.</p><p><strong>Results: </strong>Recommendations are proposed on establishing common trial infrastructure, aims and hypotheses, data collection, control groups, adaptive elements, and eligibility criteria. By leveraging cross-sectoral partnerships, co-producing research and dissemination, and incorporating adaptive elements, master protocols may offer a promising approach for accelerating progress along the translational research pipeline.</p><p><strong>Conclusions: </strong>The adaptation of master protocols for hybrid sub-studies could enable evidence-based interventions to be more effectively implemented in routine care settings. The feasibility of master protocols for effectiveness-implementation research is yet to be tested, and further development in this area is needed to trial the proposed methodology.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"258"},"PeriodicalIF":3.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12625322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145548046","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
Understanding the heterogeneity in healthcare expenditure in India. 了解印度医疗保健支出的异质性。
IF 3.4 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2025-11-18 DOI: 10.1186/s12874-025-02695-y
Srikanth Reddy Umenthala, Udaya Shankar Mishra, K S James

Health expenditure is indicative of the financial burden of health care and serves as a yardstick of health system performance. However, health expenditure may be shaped by multiple factors such as prevalence of morbidity, income inequality and above all, unobserved heterogeneity such as disease severity. This study uses finite mixture models (FMM) to analyze health expenditure distribution based on a National Sample Survey (NSS) which is a nationally representative dataset. This exercise identifies three different class of health care users, acknowledging the heterogeneity within the expenditure distribution. The classes demonstrate variations in spending behavior and associated characteristics. It is observed that health spending is influenced by disease severity, age, gender, education, social group, and economic status. Notably, health expenditure for similar diseases varies significantly across three classes, with the highest expenditure observed in the third latent class. It also reaffirms the gender disparities in health spending irrespective of the class. Additionally, socio-economic status consistently affects health expenditure across classes. These findings underscore the importance of recognizing unobserved heterogeneity in health expenditure for the design of effective healthcare policies. In conclusion, there is a need to recognize the unobserved heterogeneity in health expenditure data and such a recognition that distinct classes within may have greater significance in designing better health care policies. Beyond health expenditure, this analytical framework can be adopted to other medical and public health research to identify the latent classes, thus offering a broader methodological value.

卫生支出表明卫生保健的财政负担,并作为卫生系统绩效的衡量标准。然而,保健支出可能受到多种因素的影响,如发病率、收入不平等,尤其是疾病严重程度等未观察到的异质性。本研究以具有全国代表性的全国抽样调查数据为基础,采用有限混合模型(FMM)分析卫生支出分布。这项工作确定了三种不同类别的卫生保健使用者,承认支出分布中的异质性。这些类别显示了消费行为和相关特征的变化。研究发现,卫生支出受疾病严重程度、年龄、性别、教育程度、社会群体和经济地位的影响。值得注意的是,类似疾病的卫生支出在三个类别之间差异很大,第三个潜在类别的支出最高。它还重申,无论阶级如何,保健支出方面存在性别差异。此外,社会经济地位始终影响各阶层的保健支出。这些发现强调了认识到卫生支出中未观察到的异质性对于设计有效的卫生保健政策的重要性。总之,有必要认识到卫生支出数据中未观察到的异质性,并认识到内部的不同阶层可能在设计更好的卫生保健政策方面具有更大的意义。除卫生支出外,该分析框架还可用于其他医疗和公共卫生研究,以确定潜在类别,从而提供更广泛的方法价值。
{"title":"Understanding the heterogeneity in healthcare expenditure in India.","authors":"Srikanth Reddy Umenthala, Udaya Shankar Mishra, K S James","doi":"10.1186/s12874-025-02695-y","DOIUrl":"10.1186/s12874-025-02695-y","url":null,"abstract":"<p><p>Health expenditure is indicative of the financial burden of health care and serves as a yardstick of health system performance. However, health expenditure may be shaped by multiple factors such as prevalence of morbidity, income inequality and above all, unobserved heterogeneity such as disease severity. This study uses finite mixture models (FMM) to analyze health expenditure distribution based on a National Sample Survey (NSS) which is a nationally representative dataset. This exercise identifies three different class of health care users, acknowledging the heterogeneity within the expenditure distribution. The classes demonstrate variations in spending behavior and associated characteristics. It is observed that health spending is influenced by disease severity, age, gender, education, social group, and economic status. Notably, health expenditure for similar diseases varies significantly across three classes, with the highest expenditure observed in the third latent class. It also reaffirms the gender disparities in health spending irrespective of the class. Additionally, socio-economic status consistently affects health expenditure across classes. These findings underscore the importance of recognizing unobserved heterogeneity in health expenditure for the design of effective healthcare policies. In conclusion, there is a need to recognize the unobserved heterogeneity in health expenditure data and such a recognition that distinct classes within may have greater significance in designing better health care policies. Beyond health expenditure, this analytical framework can be adopted to other medical and public health research to identify the latent classes, thus offering a broader methodological value.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"259"},"PeriodicalIF":3.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12625602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145548154","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
期刊
BMC Medical Research Methodology
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1