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Generalized propensity score (GPS) methods have become popular for estimating causal relationships between a continuous treatment and an outcome in observational studies with rich covariate information. The pr...
The potential value of large scale datasets is constrained by the ubiquitous problem of missing data, arising in either a structured or unstructured fashion. When imputation methods are proposed for large scal...
Chronic back pain (CBP) is a leading cause of disability worldwide and is commonly managed with pharmacological, non-pharmacological, and procedural interventions. However, adverse event (AE) reporting for the...
Multimorbidity is characterized by the accrual of two or more long-term conditions (LTCs) in an individual. This state of health is increasingly prevalent and poses public health challenges. Adapting approache...
Increasing transparency in clinical research is crucial to avoid misleading conclusions. Registering clinical trials prior to participant enrolment is mandatory, and the publication of trial protocols could fu...
The National Institute for Health and Care Excellence (England’s health technology assessment body) recommend the use of the average treatment effect (ATE) as an estimand for economic evaluations. However ther...
The original article was published in BMC Medical Research Methodology 2024 24:323
Effective interventions to reduce drowning incidents require accurate and reliable data for scientific analysis. However, the lack of high-quality evidence and the variability in drowning terminology, definiti...
SIREN is a healthcare worker cohort study aiming to determine COVID-19 incidence, duration of immunity and vaccine effectiveness across 135 NHS organisations in four UK nations. Conducting an intensive prospec...
Population-adjusted indirect comparison using parametric Simulated Treatment Comparison (STC) has had limited application to survival outcomes in unanchored settings. Matching-Adjusted Indirect Comparison (MAI...
Propensity Score Matching (PSM) stands as a widely embraced method in comparative effectiveness research. PSM crafts matched datasets, mimicking some attributes of randomized designs, from observational data. ...
Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited...
A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliab...
To effectively monitor long-term outcomes among cancer patients, it is critical to accurately assess patients’ dynamic prognosis, which often involves utilizing multiple data sources (e.g., tumor registries, t...
For the follow-up of participants in randomised trials, data linkage is thought a more cost-efficient method for assessing outcomes. However, researchers often encounter technical and budgetary challenges. Dat...
This study set out to identify the factors and combinations of factors associated with the individual’s premature death, using data from the Finnish Longitudinal Study on Ageing Municipal Employees (FLAME) whi...
The purpose of this article is to design and introduce the SQUARE-IT approach to help scientists and clinicians in research to align important research problems with the objectives, the appropriate clinical re...
The generation of metainferences is a core and significant feature of mixed methods research. In recent years, there has been some discussion in the literature about criteria for appraising the quality of meta...
The US National Health and Nutrition Examination Survey (NHANES) dataset does not include a specific question or laboratory test to confirm a history of cancer diagnosis. However, if straightforward variables ...
Cluster randomized trials, which often enroll a small number of clusters, can benefit from constrained randomization, selecting a final randomization scheme from a set of known, balanced randomizations. Previo...
Logistic regression is a useful statistical technique commonly used in many fields like healthcare, marketing, or finance to generate insights from binary outcomes (e.g., sick vs. not sick). However, when appl...
Vast volumes of routinely collected data (RCD) about patients are collated by health professionals. Leveraging this data – a form of real-world data - can be valuable for quality improvement and contributing t...
Since 2015, the Complex Reviews Synthesis Unit (CRSU) has developed a suite of web-based applications (apps) that conduct complex evidence synthesis meta-analyses through point-and-click interfaces. This has b...
Platform trials are innovative clinical trials governed by a master protocol that allows for the evaluation of multiple investigational treatments that enter and leave the trial over time. Interest in platform...
Metrics and instruments can provide guidance for clinical researchers to assess their potential research projects at an early stage before significant investment. Furthermore, metrics can also provide structur...
The process of searching for and selecting clinical evidence for systematic reviews (SRs) or clinical guidelines is essential for researchers in Traditional Chinese medicine (TCM). However, this process is oft...
Focus groups (FGs) are an established method in health research to capture a full range of different perspectives on a particular research question. The extent to which they are effective depends, not least, o...
The integration of real-world evidence (RWE) from real-world data (RWD) in clinical research is crucial for bridging the gap between clinical trial results and real-world outcomes. Analyzing routinely collecte...
Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis. Kernels are used to handle even more complicated and...
Peripartum depression is a common but potentially debilitating pregnancy complication. Mobile applications can be used to collect data throughout the pregnancy and postpartum period to improve understanding of...
We aimed to develop and validate an algorithm for identifying women with polycystic ovary syndrome (PCOS) in the French national health data system.
To assess whether the outcome generation true model could be identified from other candidate models for clinical practice with current conventional model performance measures considering various simulation sce...
The majority of phase 3 clinical trials are implemented in multiple sites or centres, which inevitably leads to a correlation between observations from the same site or centre. This correlation must be careful...
Spinal pain affects up to 30% of school-age children and can interfere with various aspects of daily life, such as school attendance, physical function, and social life. Current assessment tools often rely on ...
In this work, we implement a data-driven approach using an aggregation of several analytical methods to study the characteristics of COVID-19 daily infection and death time series and identify correlations and...
Health services research often relies on secondary data, necessitating quality checks for completeness, validity, and potential errors before use. Various methods address implausible data, including data elimi...
Graft loss is a major health concern for kidney transplant (KTx) recipients. It is of clinical interest to develop a prognostic model for both graft function, quantified by estimated glomerular filtration rate...
The prediction of coronavirus disease in 2019 (COVID-19) in broader regions has been widely researched, but for specific areas such as urban areas the predictive models were rarely studied. It may be inaccurat...
The Correction to this article has been published in BMC Medical Research Methodology 2025 25:29
Autoimmune disorders have primary manifestations such as joint pain and bowel inflammation but can also have secondary manifestations such as non-infectious uveitis (NIU). A regulatory health authority raised ...
The aim of this study is to develop a method we call “cost mining” to unravel cost variation and identify cost drivers by modelling integrated patient pathways from primary care to the palliative care setting....
Missing observations within the univariate time series are common in real-life and cause analytical problems in the flow of the analysis. Imputation of missing values is an inevitable step in every incomplete ...
The connection between participants and their research team can affect how safe, informed, and respected a participant feels, and their willingness to complete a research project. Communication between researc...
Undetected atrial fibrillation (AF) poses a significant risk of stroke and cardiovascular mortality. However, diagnosing AF in real-time can be challenging as the arrhythmia is often not captured instantly. To...
The choice of a single primary outcome in randomised trials can be difficult, especially in mental health where interventions may be complex and target several outcomes simultaneously. We carried out a systema...
Propensity score matching has become a popular method for estimating causal treatment effects in non-randomized studies. However, for time-to-event outcomes, the estimation of hazard ratios based on propensity...
The aim of the study was to investigate the development of evidence-based monitoring strategies in a population with progressive or recurrent disease. A simulation study of monitoring strategies using a new bi...
Treatment switching in randomized clinical trials introduces challenges in performing causal inference. Intention To Treat (ITT) analyses often fail to fully capture the causal effect of treatment in the prese...
Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical informatio...
In cohort studies with time-to-event outcomes, covariates of interest often have values that change over time. The classical Cox regression model can handle time-dependent covariates but assumes linear effects...
A recent systematic review revealed issues in regard to performing and reporting agreement and reliability studies for ordinal scales, especially in the presence of more than two observers. This paper therefor...
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