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With continuous outcomes, the average causal effect is typically defined using a contrast of expected potential outcomes. However, in the presence of skewed outcome data, the expectation (population mean) may ...
Case-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by design...
Deep learning models have had a lot of success in various fields. However, on structured data they have struggled. Here we apply four state-of-the-art supervised deep learning models using the attention mechan...
Qualitative longitudinal research (QLR) is an emerging methodology used in health research. The method literature states that the change in a phenomenon through time should be the focus of any QLR study, but i...
Clinical practice guidelines (CPGs) are evaluated for quality with the Appraisal of Guidelines for Research and Evaluation (AGREE) tool, and this is increasingly done for different countries and regional group...
Health policy decisions are often informed by estimates of long-term survival based primarily on short-term data. A range of methods are available to include longer-term information, but there has previously b...
This study aimed to assess the methodological quality of the systematic reviews/meta-analyses (SRs/MAs) of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) using A Measurement Tool to Assess systemat...
Automated feature selection methods such as the Least Absolute Shrinkage and Selection Operator (LASSO) have recently gained importance in the prediction of quality-related outcomes as well as the risk-adjustm...
Clinical trials often seek to determine the superiority, equivalence, or non-inferiority of an experimental condition (e.g., a new drug) compared to a control condition (e.g., a placebo or an already existing ...
Factors influencing the health of populations are subjects of interdisciplinary study. However, datasets relevant to public health often lack interdisciplinary breath. It is difficult to combine data on health...
The interrupted time series (ITS) design is widely used to examine the effects of large-scale public health interventions and has the highest level of evidence validity. However, there is a notable gap regardi...
Assessing malignancy risk is important to choose appropriate management of ovarian tumors. We compared six algorithms to estimate the probabilities that an ovarian tumor is benign, borderline malignant, stage ...
For certain conditions, treatments aim to lessen deterioration over time. A trial outcome could be change in a continuous measure, analysed using a random slopes model with a different slope in each treatment ...
Since 1997, research on Gulf War illness (GWI) has predominantly used 3 case definitions—the original Research definition, the CDC definition, and modifications of the Kansas definition—but they have not been ...
In most African countries, confirmed COVID-19 case counts underestimate the number of new SARS-CoV-2 infection cases. We propose a multiplying factor to approximate the number of biologically probable new infe...
Real world evidence is now accepted by authorities charged with assessing the benefits and harms of new therapies. Clinical trials based on real world evidence are much less expensive than randomized clinical ...
This systematic review aimed to identify effective methods to increase adolescents’ response to surveys about mental health and substance use, to improve the quality of survey information.
The design of clinical trials in rare diseases is often complicated by a lack of real-world translational knowledge. Fibrodysplasia ossificans progressiva (FOP) is an ultra-rare genetic disorder characterized ...
Despite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explo...
Planning the design of a new trial comparing two treatments already in a network of trials with an a-priori plan to estimate the effect size using a network meta-analysis increases power or reduces the sample ...
There is growing interest in whether linked administrative data have the potential to aid analyses subject to missing data in cohort studies.
Suboptimal or slow recruitment affects 30–50% of trials. Education and training of trial recruiters has been identified as one strategy for potentially boosting recruitment to randomised controlled trials (her...
Consensus methods are crucial in developing clinical guidelines. Different methods, such as the Delphi and nominal group techniques, are commonly used, but there is a lack of detailed instructions on how to im...
Abstracts provide readers a concise and readily accessible information of the trials. However, poor reporting quality and spin (misrepresentation of research findings) can lead to an overestimation in trial va...
The concept of intersectionality proposes that demographic and social constructs intersect with larger social structures of oppression and privilege to shape experiences. While intersectionality is a widely ac...
Standard outcome sets enable the value-based evaluation of health care delivery. Whereas the attainment of expert opinion has been structured using methods such as the modified-Delphi process, standardized gui...
The original article was published in BMC Medical Research Methodology 2023 23:230
Data loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical diagnosis. A suitable data imputation method can help researchers make b...
Subject-level real-world data (RWD) collected during daily healthcare practices are increasingly used in medical research to assess questions that cannot be addressed in the context of a randomized controlled ...
The use of mixed effect models with a specific functional form such as the Sigmoidal Mixed Model and the Piecewise Mixed Model (or Changepoint Mixed Model) with abrupt or smooth random change allows the interp...
The comorbidity burden has a negative impact on lung-cancer survival. Several comorbidity scores have been described and are currently used. The current challenge is to select the comorbidity score that best r...
Looking for treatment-by-subset interaction on a right-censored outcome based on observational data using propensity-score (PS) modeling is of interest. However, there are still issues regarding its implementa...
A substantial body of clinical research involving individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evaluated the association between in-hospital biomarkers and severe ...
Physician participation in clinical trials is essential for the progress of modern medicine. However, the demand for physician research partners is outpacing physicians’ interest in participating in scientific...
Optimizing prescribing practices is important due to the substantial clinical and financial costs of polypharmacy and an increasingly aging population. Prior research shows the importance of social relationshi...
Technology advancement has allowed more frequent monitoring of biomarkers. The resulting data structure entails more frequent follow-ups compared to traditional longitudinal studies where the number of follow-...
Evidence-based treatment decisions in medicine are made founded on population-level evidence obtained during randomized clinical trials. In an era of personalized medicine, these decisions should be based on t...
To predict the influencing factors of neonatal pneumonia in pregnant women with diabetes mellitus using a Bayesian network model. By examining the intricate network connections between the numerous variables g...
Causal inference helps researchers and policy-makers to evaluate public health interventions. When comparing interventions or public health programs by leveraging observational sensitive individual-level data ...
When estimating the causal effect on survival outcomes in observational studies, it is necessary to adjust confounding factors due to unbalanced covariates between treatment and control groups. There is no stu...
Administrative healthcare claims databases are used in drug safety research but are limited for investigating the impacts of prenatal exposures on neonatal and pediatric outcomes without mother-infant pair ide...
Systematic reviews of randomized controlled trials are the best evidence for informing on intervention effectiveness. Their results, however, can be biased due to omitted evidence in the quantitative analyses....
The COVID-19 pandemic required that our research team change our mail-only (MO) strategy for a research survey to a strategy more manageable by staff working remotely. We used a modified web-push approach (MWP...
Predicting rare events is a challenging task due to limited data and imbalanced datasets. This special issue explores methodological advancements in prediction and modeling for rare events. The research showca...
Plasmode simulations are a type of simulations that use real data to determine the synthetic data-generating equations. Such simulations thus allow evaluating statistical methods under realistic conditions. As...
Near-real time surveillance of excess mortality has been an essential tool during the COVID-19 pandemic. It remains critical for monitoring mortality as the pandemic wanes, to detect fluctuations in the death ...
Data harmonisation is essential in real-world data (RWD) research projects based on hospital information systems databases, as coding systems differ between countries. The Hungarian hospital information system...
The World Health Organization (WHO)’s excess mortality estimates presented in May 2022 stirred controversy, due in part to the high estimate provided for Germany, which was later attributed to the spline model...
Cancer surveillance researchers analyze incidence or mortality rates jointly indexed by age group and calendar period using age-period-cohort models. Many studies consider age- and period-specific rates in two...
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
Speed 2023
Submission to first editorial decision (median days): 18
Submission to acceptance (median days): 210
Usage 2023
Downloads: 4,638,094
Altmetric mentions: 3,126
The following summary describes the peer review process for this journal:
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