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Machine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possibl...
Identifying mechanisms to maintain CBPR studies during an infectious disease pandemic is vital. The current paper describes the changes in methods and processes conducted within a CBPR mixed-methods study to a...
Many clinical practice guidelines are based on randomised controlled trials conducted in secondary or tertiary care setting and general practitioners frequently question their relevance for primary care patien...
Precision medicine is an emerging field that involves the selection of treatments based on patients’ individual prognostic data. It is formalized through the identification of individualized treatment rules (I...
Due to economical and ethical reasons, the two-stage designs have been widely used for Phase 2 single-arm trials in oncology because the designs allow us to stop the trial early if the proposed treatment is li...
Availability of linked biomedical and social science data has risen dramatically in past decades, facilitating holistic and systems-based analyses. Among these, Bayesian networks have great potential to tackle...
Prognostic information for patients with hypertension is largely based on population averages. The purpose of this study was to compare the performance of four machine learning approaches for personalized pred...
The role of immunological responses to exposed bacteria on disease incidence is increasingly under investigation. With many bacterial species, and many potential antibody reactions to a particular species, the...
Research about the decision to participate in a clinical study has tended to be limited to single indications and has focused on narrow sets of study and participant characteristics. This study applied stated ...
Within evidence-based practice (EBP), systematic reviews (SR) are considered the highest level of evidence in that they summarize the best available research and describe the progress in a determined field. Du...
To improve the efficiency of clinical trials, leveraging external data on control and/or treatment effects, which is almost always available, appears to be a promising approach.
Within implementation science studies, contextual analysis is increasingly recognized as foundational to interventions' successful and sustainable implementation. However, inconsistencies between methodologica...
Meta-analyses are used to summarise the results of several studies on a specific research question. Standard methods for meta-analyses, namely inverse variance random effects models, have unfavourable properti...
This paper provides the methodologies of a new summary curve that measures the dynamic outcome following allogenic hematopoietic cell transplantation. This new summary curve computes the probabilities that a p...
Subconcussive blast exposure during military training has been the subject of both anecdotal concerns and reports in the medical literature, but prior studies have often been small and have used inconsistent m...
Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and...
The original article was published in BMC Medical Research Methodology 2022 22:305
When conducting a randomised controlled trial, there exist many different methods to allocate participants, and a vast array of evidence-based opinions on which methods are the most effective at doing this, le...
This meta-epidemiological study aimed to assess methodological quality of a sample of contemporary non-randomised clinical studies of clinical interventions.
Numerous observational studies have revealed an increased risk of death and complications with transfusion, but this observation has not been confirmed in randomized controlled trials (RCTs). The “transfusion ...
Many dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. External...
Search filters are standardised sets of search terms, with validated performance, that are designed to retrieve studies with specific characteristics. A cost–utility analysis (CUA) is the preferred type of eco...
Reliable information on the use of health services is important for health care planning, monitoring and policy. It is critical to assess the validity of the sources used for this purpose, including register a...
Altering cover letter information to reduce non-response bias in trauma research could inadvertently leave survey participants unprepared for potentially upsetting questions. In an unsolicited, mailed survey, ...
Case study methodology is widely used in health research, but has had a marginal role in evaluative studies, given it is often assumed that case studies offer little for making causal inferences. We undertook ...
Diagnostic evidence of the accuracy of a test for identifying a target condition of interest can be estimated using systematic approaches following standardized methodologies. Statistical methods for the meta-...
Globorisk is a novel risk prediction model for predicting cardiovascular disease (CVD). Globorisk is a country-specific risk prediction model that determines CVD risk for all countries. This model has two vers...
The Correction to this article has been published in BMC Medical Research Methodology 2022 22:315
The U.S. Ending the HIV epidemic (EHE) plan aims to reduce annual HIV incidence by 90% by 2030, by first focusing interventions on 57 regions (EHE jurisdictions) that contributed to more than 50% of annual HIV...
Health state utility values (HSUVs) are an essential input parameter to cost-utility analysis (CUA). Systematic literature reviews (SLRs) provide summarized information for selecting utility values from an inc...
Process evaluations aim to understand how complex interventions bring about outcomes by examining intervention mechanisms, implementation, and context. While much attention has been paid to the methodology of ...
Mediation analysis aims at estimating to what extent the effect of an exposure on an outcome is explained by a set of mediators on the causal pathway between the exposure and the outcome. The total effect of t...
This study illustrates the use of logistic regression and machine learning methods, specifically random forest models, in health services research by analyzing outcomes for a cohort of patients with concomitan...
A critical step in trial design is determining the sample size and sample allocation to ensure the proposed study has sufficient power to test the hypothesis of interest: superiority, equivalence, or non-infer...
Psychometrically sound resilience outcome measures are essential to establish how health and care services or interventions can enhance the resilience of people living with dementia (PLWD) and their carers. Th...
The occurrence and timing of mycobacterial culture conversion is used as a proxy for tuberculosis treatment response. When researchers serially sample sputum during tuberculosis studies, contamination or misse...
Anal human papillomavirus (HPV) disproportionately affects men who have sex with men (MSM), particularly those who are older and those living with HIV. After experiencing difficulty recruiting older MSM into a...
The association structure linking the longitudinal and survival sub-models is of fundamental importance in the joint modeling framework and the choice of this structure should be made based on the clinical bac...
Due to identifiability problems, statistical inference about treatment-by-period interactions has not been discussed for stepped wedge designs in the literature thus far. Unidirectional switch designs (USDs) g...
Numerous nutrition-related policy options and strategies have been proposed to tackle hypertension and other risk factors of non-communicable diseases (NCDs). In this study, we developed a comparative analysis...
To report our recommended methodology for extracting and then confirming research uncertainties – areas where research has failed to answer a research question – derived from previously published literature du...
Two common ways of assessing the clinical relevance of treatment outcomes are the minimal important difference (MID) and the patient acceptable symptom state (PASS). The former represents the smallest change i...
There are situations when we need to model multiple time-scales in survival analysis. A usual approach in this setting would involve fitting Cox or Poisson models to a time-split dataset. However, this leads t...
A short instrument would enhance the viability of a study. Therefore, we aimed to shorten the specific module (SPD-10) of the Quality of Life Instrument for Chronic Diseases - Chronic Renal Failure (QLICD-CRF)...
Investigations of participant retention in longitudinal health and medical research, document strategies that work best but overlook social marketing’s capacity to influence participant retention. After apply...
The increased adoption of the internet, social media, wearable devices, e-health services, and other technology-driven services in medicine and healthcare has led to the rapid generation of various types of di...
The Correction to this article has been published in BMC Medical Research Methodology 2023 23:109
Risk prediction models are useful tools in clinical decision-making which help with risk stratification and resource allocations and may lead to a better health care for patients. AutoScore is a machine learni...
Meta-analyses of analgesic medicines for low back pain often rescale measures of pain intensity to use mean difference (MD) instead of standardised mean difference for pooled estimates. Although this improves ...
Cox proportional hazards regression models and machine learning models are widely used for predicting the risk of dementia. Existing comparisons of these models have mostly been based on empirical datasets and...
Semi-continuous data characterized by an excessive proportion of zeros and right-skewed continuous positive values appear frequently in medical research. One example would be the pharmaceutical expenditure (PE...
The original article was published in BMC Medical Research Methodology 2022 22:217
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