Pub Date : 2025-11-24DOI: 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}
Pub Date : 2025-11-24DOI: 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}
Pub Date : 2025-11-22DOI: 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.
{"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}
Pub Date : 2025-11-20DOI: 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.
{"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}
Pub Date : 2025-11-20DOI: 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.
{"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}
Pub Date : 2025-11-19DOI: 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}
Pub Date : 2025-11-18DOI: 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.
{"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}
Pub Date : 2025-11-18DOI: 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.
{"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}
Pub Date : 2025-11-14DOI: 10.1186/s12874-025-02705-z
Arezoo Abasi, Haleh Ayatollahi, Seyed Abbas Motevalian
Cohort studies are a core aspect of clinical research which helps to gather a large volume of data over time. As digital technologies evolve, managing these data has become increasingly complex. Therefore, the use of cohort data management systems (CDMS) has been suggested to enhance data accuracy, confidentiality, and consistency. However, the functional and non-functional requirements of these systems have not been adequately emphasized in literature. This study aimed to identify the key functional and non-functional requirements of these systems. This was a scoping review conducted in 2025, and articles were searched in PubMed, Scopus, Web of Science, ProQuest, IEEE Xplore, and the Cochrane Library databases as well as Google Scholar. Initially, 843 articles were retrieved, and finally, 45 articles published between 1st January 2005 and 31st June 2025 were selected. Nine functional and eight non-functional requirements were identified for CDMS. These systems are essential for facilitating cohort studies through data management, data processing and analysis. Advanced tools like AI, visual dashboards, and automation have improved CDMS functionalities. The most important non-functional requirements included flexibility, security and usability. CDMS must support comprehensive data operations, secure access, user engagement, and interoperability while ensuring scalability, privacy, and regulatory compliance. Requirements such as maintainability, although less emphasized, are essential for the long-term development and optimization of data management systems. Future research should focus on emerging technologies like blockchain and Internet of Things (IoT) to enhance the security, integrity, and performance of CDMS.
队列研究是临床研究的一个核心方面,它有助于收集大量的数据随着时间的推移。随着数字技术的发展,管理这些数据变得越来越复杂。因此,建议使用队列数据管理系统(CDMS)来提高数据的准确性、保密性和一致性。然而,这些系统的功能性和非功能性需求在文献中没有得到充分的强调。本研究旨在确定这些系统的关键功能和非功能需求。这是在2025年进行的一项范围综述,文章在PubMed、Scopus、Web of Science、ProQuest、IEEE Xplore、Cochrane Library数据库以及谷歌Scholar中进行了检索。最初,检索了843篇文章,最终选择了2005年1月1日至2025年6月31日期间发表的45篇文章。确定了CDMS的9个功能性需求和8个非功能性需求。这些系统对于通过数据管理、数据处理和分析促进队列研究至关重要。人工智能、可视化仪表板和自动化等高级工具改进了CDMS的功能。最重要的非功能需求包括灵活性、安全性和可用性。CDMS必须支持全面的数据操作、安全访问、用户参与和互操作性,同时确保可扩展性、隐私性和法规遵从性。诸如可维护性之类的需求虽然较少被强调,但对于数据管理系统的长期开发和优化是必不可少的。未来的研究应集中在区块链和物联网(IoT)等新兴技术上,以提高CDMS的安全性、完整性和性能。
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Pub Date : 2025-11-14DOI: 10.1186/s12874-025-02714-y
Kim May Lee, Richard Emsley
Background: Group sequential designs are increasingly employed to allow trials to stop early with statistical rigor. While existing work focuses on intention-to-treat effect on clinical endpoints, the properties of mediation analysis (commonly conducted in psychological trials to understand a causal mechanism) remain unknown under group sequential designs.
Methods: Considering a group sequential design with one interim analysis for early stopping for efficacy, we conduct a simulation study to evaluate existing analysis techniques when the treatment effect on a continuous outcome is partially or fully mediated by a continuous intermediate variable measuring a casual mechanism. We study the probability of rejecting the null hypotheses on the total effect (i.e., intention-to-treat effect), direct effect and indirect effect, respectively. We examine the bias of maximum likelihood estimator for these effects. We investigate if the penalized (and conditional) maximum likelihood estimator has smaller bias than the maximum likelihood estimator when a trial stopped (did not stop) early.
Results: The presence of an intermediate variable reduces the power of a group sequential design when sample size calculation ignores the causal mechanism, though type I error control remains unaffected. The maximum likelihood estimator is unbiased only for the mediator-outcome path, impacting the properties of mediation analysis since existing methods typically rely on it to estimate the pathways. The penalized maximum likelihood estimator for other pathways has similar bias to the stage-one maximum likelihood estimator, while the conditional maximum likelihood estimator shows negligible or smaller bias than the usual maximum likelihood estimator for estimating the total and the direct effects only.
Conclusions: Mediation analysis needs additional consideration in group sequential designs. As with fixed trial designs, the sample size calculation of group sequential designs should account for the total variability underlying a causal mechanism when the treatment effect is hypothesized to be mediated by an intermediate variable, or risk the overall power to detect an intention-to-treat (total) effect being lower than the nominal value. We suggest reporting several estimators and acknowledging that they may be biased for some mediation pathways. More research is needed to develop methods for the analysis of indirect effect under group sequential designs.
{"title":"Inference in group sequential designs with causal mechanisms: implications for power and mediation analysis.","authors":"Kim May Lee, Richard Emsley","doi":"10.1186/s12874-025-02714-y","DOIUrl":"10.1186/s12874-025-02714-y","url":null,"abstract":"<p><strong>Background: </strong>Group sequential designs are increasingly employed to allow trials to stop early with statistical rigor. While existing work focuses on intention-to-treat effect on clinical endpoints, the properties of mediation analysis (commonly conducted in psychological trials to understand a causal mechanism) remain unknown under group sequential designs.</p><p><strong>Methods: </strong>Considering a group sequential design with one interim analysis for early stopping for efficacy, we conduct a simulation study to evaluate existing analysis techniques when the treatment effect on a continuous outcome is partially or fully mediated by a continuous intermediate variable measuring a casual mechanism. We study the probability of rejecting the null hypotheses on the total effect (i.e., intention-to-treat effect), direct effect and indirect effect, respectively. We examine the bias of maximum likelihood estimator for these effects. We investigate if the penalized (and conditional) maximum likelihood estimator has smaller bias than the maximum likelihood estimator when a trial stopped (did not stop) early.</p><p><strong>Results: </strong>The presence of an intermediate variable reduces the power of a group sequential design when sample size calculation ignores the causal mechanism, though type I error control remains unaffected. The maximum likelihood estimator is unbiased only for the mediator-outcome path, impacting the properties of mediation analysis since existing methods typically rely on it to estimate the pathways. The penalized maximum likelihood estimator for other pathways has similar bias to the stage-one maximum likelihood estimator, while the conditional maximum likelihood estimator shows negligible or smaller bias than the usual maximum likelihood estimator for estimating the total and the direct effects only.</p><p><strong>Conclusions: </strong>Mediation analysis needs additional consideration in group sequential designs. As with fixed trial designs, the sample size calculation of group sequential designs should account for the total variability underlying a causal mechanism when the treatment effect is hypothesized to be mediated by an intermediate variable, or risk the overall power to detect an intention-to-treat (total) effect being lower than the nominal value. We suggest reporting several estimators and acknowledging that they may be biased for some mediation pathways. More research is needed to develop methods for the analysis of indirect effect under group sequential designs.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"257"},"PeriodicalIF":3.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12619153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145522777","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}