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Models, theories, and frameworks (MTFs) provide the foundation for a cumulative science of implementation, reflecting a shared, evolving understanding of various facets of implementation. One under-represented...
Although books and articles guiding the methods of sample size calculation for prevalence studies are available, we aim to guide, assist and report sample size calculation using the present calculators.
Estimations of causal effects from observational data are subject to various sources of bias. One method for adjusting for the residual biases in the estimation of treatment effects is through the use of negat...
Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression...
Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixe...
Randomized test-treatment studies aim to evaluate the clinical utility of diagnostic tests by providing evidence on their impact on patient health. However, the sample size calculation is affected by several f...
When designing a noninferiority (NI) study one of the most important steps is to set the noninferiority (NI) limit. The NI limit is an acceptable loss of efficacy for a new investigative treatment compared to ...
Collecting new data from cross-sectional/survey and cohort observational study designs can be expensive and time-consuming. Nested (hierarchically cocooned within an existing parent study) and/or Multipart (≥ ...
Interrupted time series (ITS) analysis has become a popular design to evaluate the effects of health interventions. However, the most common formulation for ITS, the linear segmented regression, is not always ...
In recent years there is increasing interest in modeling the effect of early longitudinal biomarker data on future time-to-event or other outcomes. Sometimes investigators are also interested in knowing whethe...
We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and deve...
Studying the relationship between unemployment and health raises many methodological challenges. In the current study, the aim was to evaluate the sensitivity of estimates based on different ways of measuring ...
The decision to initiate invasive long-term ventilation for a child with complex medical needs can be extremely challenging. TechChild is a research programme that aims to explore the liminal space between ini...
Linkage of public healthcare data provides powerful resources for studying from a comprehensive view of quality of care than information for a single administrative database. It is believed that positive patie...
Multiple imputation is frequently used to address missing data when conducting statistical analyses. There is a paucity of research into the performance of multiple imputation when the prevalence of missing da...
We aimed to investigate factors related to the 90-day poor prognosis (mRS≥3) in patients with transient ischemic attack (TIA) or minor stroke, construct 90-day poor prognosis prediction models for patients wit...
Group-based trajectory modelling (GBTM) is increasingly used to identify subgroups of individuals with similar patterns. In this paper, we use simulated and real-life data to illustrate that GBTM is susceptibl...
Meta-analysis is a central method for quality evidence generation. In particular, meta-analysis is gaining speedy momentum in the growing world of quantitative information. There are several software applicati...
Meta-analyses of test accuracy studies may provide estimates that are highly improbable in clinical practice. Tailored meta-analysis produces plausible estimates for the accuracy of a test within a specific se...
The quality of communication between healthcare professionals (HCPs) and patients affects health outcomes. Different coding systems have been developed to unravel the interaction. Most schemes consist of prede...
Comparative effectiveness research (CER) using observational databases has been suggested to obtain personalized evidence of treatment effectiveness. Inferential difficulties remain using traditional CER appro...
Methods for developing national recommendations vary widely. The successful adoption of new guidance into routine practice is dependent on buy-in from the clinicians delivering day-to-day patient care and must...
The individual data collected throughout patient follow-up constitute crucial information for assessing the risk of a clinical event, and eventually for adapting a therapeutic strategy. Joint models and landma...
Due to contradictory results in current research, whether age at menopause is increasing or decreasing in Western countries remains an open question, yet worth studying as later ages at menopause are likely to...
Increasingly in network meta-analysis (NMA), there is a need to incorporate non-randomised evidence to estimate relative treatment effects, and in particular in cases with limited randomised evidence, sometime...
The purpose of this study is to describe and assess a remote height and weight protocol that was developed for an ongoing trial conducted during the SARS COV-2 pandemic.
Systematic reviews can apply the Appraisal of Guidelines for Research & Evaluation (AGREE) II tool to critically appraise clinical practice guidelines (CPGs) for treating low back pain (LBP); however, when app...
Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of 30-day mortality in patients with sepsis-associated encephalopathy (SAE).
Seasonality classification is a well-known and important part of time series analysis. Understanding the seasonality of a biological event can contribute to an improved understanding of its causes and help gui...
Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information...
The need to engage adults, age 65 and older, in clinical trials of conditions typical in older populations, (e.g. hypertension, diabetes mellitus, Alzheimer’s disease and related dementia) is exponentially inc...
To optimize colorectal cancer (CRC) screening and surveillance, information regarding the time-dependent risk of advanced adenomas (AA) to develop into CRC is crucial. However, since AA are removed after diagn...
Implementation science in healthcare aims to understand how to get evidence into practice. Once this is achieved in one setting, it becomes increasingly difficult to replicate elsewhere. The problem is often a...
Accurate measurement of trajectories in longitudinal studies, considered the gold standard method for tracking functional growth during adolescence, decline in aging, and change after head injury, is subject t...
A lack of available data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance res...
Normalization process theory (NPT) has been widely used to better understand how new interventions are implemented and embedded. The NoMAD (Normalization Measurement Development questionnaire) is a 23-item NPT...
Sustainable Human Immunodeficiency Virus (HIV) virological suppression is crucial to achieving the Joint United Nations Programme of HIV/AIDS (UNAIDS) 95–95-95 treatment targets to reduce the risk of onward HI...
The concept of standard of care (SoC) treatment is commonly utilized in clinical trials. However, in a setting of an emergent disease, such as COVID-19, where there is no established effective treatment, it is...
The NINDS rt-PA Stroke Study is frequently cited in support of alteplase for acute ischemic stroke within 3 h of symptom onset. Multiple post-hoc reanalyses of this trial have been published to adjust for a ba...
To summarize the up-to-date empirical evidence on trial-level characteristics of randomized controlled trials associated with treatment effect estimates.
Schizophrenia is a chronic, severe, and debilitating mental disorder always considered one of the recurrent psychiatric diseases. This study aimed to use penalized count regression models to determine factors ...
Separation or monotone likelihood may exist in fitting process of the accelerated failure time (AFT) model using maximum likelihood approach when sample size is small and/or rate of censoring is high (rare eve...
In binary logistic regression data are ‘separable’ if there exists a linear combination of explanatory variables which perfectly predicts the observed outcome, leading to non-existence of some of the maximum l...
Machine learning and automation are increasingly used to make the evidence synthesis process faster and more responsive to policymakers’ needs. In systematic reviews of randomized controlled trials (RCTs), ris...
Real-life data consist of exhaustive data which are not subject to selection bias. These data enable to study drug-safety profiles but are underused because of their temporality, necessitating complex models (...
Network analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic health conditions. The effect that co-occurrence measurement has on disease network struct...
There is increasing interest in the development and use of clinical prediction models, but a lack of evidence-supported guidance on the merits of different modelling approaches. This is especially true for tim...
Development of complex interventions for management of chronic conditions has become increasingly common, with guidance now provided. Fidelity (whether the intervention is designed, delivered and received as i...
Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study heterogeneity of two association studies requ...
Recent international health events have led to an increased proliferation of remotely delivered health interventions. Even with the pandemic seemingly coming under control, the experiences of the past year hav...
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