The main points of in the letter by Kopylev and Dzierlenga can be summarized as: (1) if the difference of two parameter estimates is not statistically significant, then using either one can be considered a reasonable representation of a parameter for use in a meta-analysis, and (2) that we calculated the estimand in our simulation incorrectly. We address these two points in turn.
Comparing and combining reports from different publication is of interest to many when conducting meta-analyses. However, challenges can arise with reports using transformations of the exposure data. A recent publication, Linakis et al. (BMC Med Res Methodol 24:6, 2024), compared methods for re-expression with the conclusion that the re-expression methods examined are not...
Complex socio-cultural, psychological, geographical, and service-related challenges are faced when recruiting people with dementia for clinical trials. The aim of Phase 1 of the Dementia PersonAlised Care Team (D-PACT) project was to assess the feasibility of recruiting (identifying, approaching and consenting) people with dementia, including those without capacity to consent, to...
Human behavioral responses to changes in risks are often delayed. Methods for estimating these delayed responses either rely on rigid assumptions about the delay distribution (e.g., Erlang distribution), producing a poor fit, or yield period-specific estimates (e.g., estimates from the Autoregressive Distributed Lag (ARDL) model) that are difficult to integrate into simulation...
With the emergence of high-dimensional censored survival data in health and medicine, the use of survival models for risk prediction is increasing. To date, practical techniques exist for splitting data for model training and performance evaluation. While different sampling methods have been compared for their performances, the effect of data splitting ratio and survival specific...
Accurate prediction of survival in oncology can guide targeted interventions. The traditional regression-based Cox proportional hazards (CPH) model has statistical assumptions and may have limited predictive accuracy. With the capability to model large datasets, machine learning (ML) holds the potential to improve the prediction of time-to-event outcomes, such as cancer survival...
To develop a Bayesian method for dynamic borrowing of information from historical clinical trials and real-world data that has the potential to improve the efficiency of clinical trials. We propose a novel statistical metric to quantify heterogeneity among data sources. Based on this metric, a Multi-Source Dynamic Borrowing (MSDB) Bayesian prior framework was proposed, which can...
The increasingly available multi-omics datasets have posed both new opportunities and challenges to the development of quantitative methods for discovering novel mechanisms in biomedical research. One natural approach to analyzing such datasets is mediation analysis originated from the causal inference literature. Mediation analysis can help unravel the mechanisms through which...
Linking mothers to their newborns in health records is crucial for understanding the impact of policies, programs, and medical treatments on intergenerational health outcomes. While previous studies have used shared identifiers for linkage, such data are often unavailable in Medicaid records due to privacy concerns. Existing algorithms are not sufficiently flexible to accommodate...
Clinical research coordinators (CRCs) play a vital role in the management and execution of clinical trials, particularly in oncology and hematology. This survey-based study, conducted by the Italian Group of Data Managers and Clinical Research Coordinators (GIDMcrc), explores the responsibilities, job satisfaction and training needs of CRCs across Italy. 171 professionals from 20...
Surveys are an important source of timely and comprehensive population health data and play a crucial role in public health research and policymaking, as shown during the COVID-19 pandemic. However, the reliability of survey data depends on their accuracy, which is often difficult to assess due to the limited availability of benchmark data. This study evaluates the accuracy of...
Inadequate reporting of interventions in educational research hinders the identification and replication of effective teaching strategies. This study aimed to analyze the reporting of educational interventions in randomized controlled trials (RCTs) focused on educating parents and caregivers of children with developmental disabilities (CDD). This was a meta-research study...
Evaluating the performance of predictive models for survival is essential before they can be trusted for real-world applications and decision making. While good measures such as the C-index are available for model discrimination, the toolbox for model calibration is much more limited in the time-to-event setting. The method of D-calibration was therefore an important contribution...
This study examines the complex issue of specifically quantifying sensitive quantitative variables while ensuring respondents’ privacy and maintaining data integrity. The present study presents two novel optional stratified double response models (OSDRMs) that integrate simultaneously additive and subtractive frantically mechanisms to improve privacy protection while maintaining...
Although single-arm trials (SATs) can serve as a pivotal study design for evaluating drug efficacy in specific scenarios, they are accompanied by inherent limitations and challenges. This paper explores the challenges of validity, reliability, statistical power, and the use of external control in SATs from a statistical perspective. We reflect on the rationale for employing SATs...
Challenges in identifying patients at high risk of asthma have driven the development of clinical prediction models (CPMs) to optimise workflows. However, concerns about the transparency and usability of these models remain. This study systematically reviewed previously developed CPMs for asthma diagnosis, focusing on their reporting, methodology, and applicability. We searched...
Random-effects meta-analysis is widely used for synthesizing the studies of a systematic review assuming a normal distribution for the study-specific effects. However, this assumption might not always be plausible. Alternative options have been suggested but not used in published meta-analyses. We conducted a systematic review to identify articles that proposed alternative meta...
Chronic kidney disease (CKD) is slowly progressive, with clinically-relevant end-points of interest (e.g. kidney failure, dialysis, transplantation, death due to kidney disease) occurring many years after diagnosis, making the design of trials to evaluate treatments that slow the progression of kidney disease challenging. Recent meta-analyses have shown that the 3-year total...
Collaborations involving partnerships between academic researchers and knowledge users can improve the relevance and potential adoption of evidence in health care practices and decision-making. However, descriptions of partnering practice characteristics are often limited to self-report from the lead academic researcher, with no comparison among team members. The primary...
Musculoskeletal disorders (MSDs) are a major health concern for children. Traditional assessment methods, which are based on subjective assessments, may be inaccurate. The main objective of this research is to evaluate Synthetic Minority Over-sampling Technique(SMOTE)-based machine learning algorithms for predicting MSDs in elementary school students with an unbalanced dataset...
Standard random-effects meta-analysis models for rare events exhibit significant limitations, particularly when synthesizing studies with double-zero events. While methodological advances in both frequentist and Bayesian frameworks now offer robust alternatives that bypass continuity corrections, the comparative performance of these approaches—especially between Bayesian and...
The ATR-FTIR spectral data represent a valuable source of information in a wide range of pathologies, including neurological disorders, and can be used for disease discrimination. To this end, the identification of the potential spectral biomarkers among all possible candidates is needed, but the amount of information characterizing the spectral dataset and the presence of...
Conducting a rigorous systematic review of animal studies requires a priori registration of a study protocol. However, it remains unknown how many of these registered studies culminate in publication and how long it takes to complete such a systematic review. Thus, this study had two objectives: (1) to assess the proportion of registered protocols that result in publication, and...
The cut-point selection of biomarkers based on clinical benefit of test results rather than accuracy-based is of interest for decision makers. We adapted the four methods of cut-point selection based on clinical utility of test results including Youden, Product, Union and the absolute difference of total utility with 2 times of AUC. The population-based parametric pairs of...
Recruiting frail older adults is challenging, resulting in underpowered trials, wasted resources, and unexpected costs. Researchers rarely report transparently and comprehensively on recruitment. However, sharing recruitment experiences could improve future efforts. This study tracks recruitment efforts and assesses their impact on recruitment outcomes in the ACTIVE-AGE@home...