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In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully ...
In the context of clinical research, there is an increasing need for new study designs that help to incorporate already available data. With the help of historical controls, the existing information can be uti...
Systematic reviews and data synthesis of randomised clinical trials play a crucial role in clinical practice, research, and health policy. Trial sequential analysis can be used in systematic reviews to control...
The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investi...
Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common ch...
Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confoundin...
Many existing healthcare ranking systems are notably intricate. The standards for peer review and evaluation often differ across specialties, leading to contradictory results among various ranking systems. The...
Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learn...
Distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymou...
Accurate prediction of subject recruitment, which is critical to the success of a study, remains an ongoing challenge. Previous prediction models often rely on parametric assumptions which are not always met o...
Comprehending the research dataset is crucial for obtaining reliable and valid outcomes. Health analysts must have a deep comprehension of the data being analyzed. This comprehension allows them to suggest pra...
Real-world evidence is receiving considerable attention as a way to evaluate the efficacy and safety of medical products for substance use disorders (SUDs). However, the feasibility of using real-world data (R...
According to long-term follow-up data of malignant tumor patients, assessing treatment effects requires careful consideration of competing risks. The commonly used cause-specific hazard ratio (CHR) and sub-dis...
To evaluate transparency practices in randomized controlled trials (RCTs) in dentistry.
Digital mental health interventions (DMHIs) overcome traditional barriers enabling wider access to mental health support and allowing individuals to manage their treatment. How individuals engage with DMHIs im...
While there is an interest in defining longitudinal change in people with chronic illness like Parkinson’s disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researcher...
Spillover of effect, whether positive or negative, from intervention to control group patients invalidates the Stable Unit Treatment Variable Assumption (SUTVA). SUTVA is critical to valid causal inference fro...
Synthetic Electronic Health Records (EHRs) are becoming increasingly popular as a privacy enhancing technology. However, for longitudinal EHRs specifically, little research has been done into how to properly e...
There is a growing awareness of the need to adequately integrate sex and gender into health-related research. Although it is widely known that the entangled dimensions sex/gender are not comprehensively consid...
Randomised, cluster-based study designs in schools are commonly used to evaluate children’s physical activity interventions. Sample size estimation relies on accurate estimation of the intra-cluster correlatio...
Statistical regression models are used for predicting outcomes based on the values of some predictor variables or for describing the association of an outcome with predictors. With a data set at hand, a regres...
Appropriately defining and using the minimal important change (MIC) and the minimal clinically important difference (MCID) are crucial for determining whether the results are clinically significant. The aim of...
Prediction models are often externally validated with data from a single study or cohort. However, the interpretation of performance estimates obtained with single-study external validation is not as straightf...
Childhood stunting is a major indicator of child malnutrition and a focus area of Global Nutrition Targets for 2025 and Sustainable Development Goals. Risk factors for childhood stunting are well studied and w...
Simulation is an important tool for assessing the performance of statistical methods for the analysis of data and for the planning of studies. While methods are available for the simulation of correlated binar...
In order to facilitate the tracing of infectious diseases in a small area and to effectively carry out disease control and epidemiological investigations, this research proposes a novel spatiotemporal model to...
We have introduced the R package jmBIG to facilitate the analysis of large healthcare datasets and the development of predictive models. This package provides a comprehensive set of tools and functions specifi...
Dimension reduction methods do not always reduce their underlying indicators to a single composite score. Furthermore, such methods are usually based on optimality criteria that require discarding some informa...
Wolbachia symbiosis in Aedes aegypti is an emerging biocontrol measure against dengue. However, assessing its real-world efficacy is challenging due to the non-randomised, field-based nature of most intervention ...
Although aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) ca...
Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of ...
Propensity score matching is vital in epidemiological studies using observational data, yet its estimates relies on correct model-specification. This study assesses supervised deep learning models and unsuperv...
Pocock-Simon’s minimisation method has been widely used to balance treatment assignments across prognostic factors in randomised controlled trials (RCTs). Previous studies focusing on the survival outcomes hav...
Standard systematic review can be labor-intensive and time-consuming meaning that it can be difficult to provide timely evidence when there is an urgent public health emergency such as a pandemic. The Clinical...
The research on sodium-glucose cotransporter 2 (SGLT2) inhibitors has been increasing rapidly in the last decade, as well as indications for their use. This study aimed to analyze the methodological characteri...
A platform trial approach allows adding arms to on-going trials to speed up intervention discovery programs. A control arm remains open for recruitment in a platform trial while intervention arms may be added ...
Systematic reviews and meta-analyses are essential tools in contemporary evidence-based medicine, synthesizing evidence from various sources to better inform clinical decision-making. However, the conclusions ...
Lack of diversity in participants throughout the research process limits the generalizability of findings and may contribute to health disparities. There are unique challenges to recruitment of families to ped...
POINCARE-2 trial aimed to assess the effectiveness of a strategy designed to tackle fluid overload through daily weighing and subsequent administration of treatments in critically ill patients. Even in highly ...
The story completion method provides a different way of doing qualitative research. We note the emergent popularity of this method in health-related research, while much remains to be negotiated in terms of be...
In randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups ba...
Network analysis, commonly used to describe the patterns of multimorbidity, uses the strength of association between conditions as weight to classify conditions into communities and calculate centrality statis...
The original article was published in BMC Medical Research Methodology 2021 21:273
There is increasing interest in the capacity of adaptive designs to improve the efficiency of clinical trials. However, relatively little work has investigated how economic considerations – including the costs...
New therapeutics in oncology have presented challenges to existing paradigms and trial designs in all phases of drug development. As a motivating example, we considered an ongoing phase II trial planned to eva...
Engaging researchers as research subjects is key to informing the development of effective and relevant research practices. It is important to understand how best to engage researchers as research subjects.
When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results. This is because there is often substantial flexibility in ...
The test-negative design (TND) is an observational study design to evaluate vaccine effectiveness (VE) that enrolls individuals receiving diagnostic testing for a target disease as part of routine care. VE is ...
Effectiveness in health care is a specific characteristic of each intervention and outcome evaluated. Especially with regard to surgical interventions, organization, structure and processes play a key role in ...
Throughout the Covid-19 pandemic, researchers have made use of electronic health records to research this disease in a rapidly evolving environment of questions and discoveries. These studies are prone to coll...
Citation Impact 2023
Journal Impact Factor: 3.9
5-year Journal Impact Factor: 6.5
Source Normalized Impact per Paper (SNIP): 1.701
SCImago Journal Rank (SJR): 1.632
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Submission to first editorial decision (median days): 18
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