BMC Medical Research Methodology最新文献

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.

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.

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.

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.

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.

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.

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.

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.