-
Mashup Score: 1
Background There is common belief among some medical researchers that if a potential surrogate endpoint is highly correlated with a true endpoint, then a positive (or negative) difference in potential surrogate endpoints between randomization groups would imply a positive (or negative) difference in unobserved true endpoints between randomization groups. We investigate this belief when the potential surrogate and unobserved true endpoints are perfectly correlated within each randomization group. Methods We use a graphical approach. The vertical axis is the unobserved true endpoint and the horizontal axis is the potential surrogate endpoint. Perfect correlation within each randomization group implies that, for each randomization group, potential surrogate and true endpoints are related by a straight line. In this scenario the investigator does not know the slopes or intercepts. We consider a plausible example where the slope of the line is higher for the experimental group than for the
Categories: General Medicine News, Hem/OncsTweet
-
Mashup Score: 7
Background Systematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools. Implementation Meta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, reques
Categories: General Medicine News, General HCPsTweet-
An article published in #BMCMedResMethodol presents Meta-Analysis Accelerator: a tool that enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. https://t.co/eEVBZo5f4T
-
-
Mashup Score: 10Contextual effects: how to, and how not to, quantify them - BMC Medical Research Methodology - 2 month(s) ago
The importance of contextual effects and their roles in clinical care controversial. A Cochrane review published in 2010 concluded that placebo interventions lack important clinical effects overall, but that placebo interventions can influence patient-reported outcomes such as pain and nausea. However, systematic reviews published after 2010 estimated greater contextual effects than the Cochrane review, which stems from the inappropriate methods employed to quantify contextual effects. The effects of medical interventions (i.e., the total treatment effect) can be divided into three components: specific, contextual, and non-specific. We propose that the most effective method for quantifying the magnitude of contextual effects is to calculate the difference in outcome measures between a group treated with placebo and a non-treated control group. Here, we show that other methods, such as solely using the placebo control arm or calculation of a ‘proportional contextual effect,’ are limited
Categories: General Medicine News, Hem/OncsTweet-
Contextual effects: how to, and how not to, quantify them https://t.co/2kUSucXSka via @tobi_saueressig et al
-
-
Mashup Score: 8
Background Systematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools. Implementation Meta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, reques
Categories: General Medicine News, General HCPsTweet-
An article published in #BMCMedResMethodol presents Meta-Analysis Accelerator: a tool that enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. https://t.co/eEVBZo5f4T
-
-
Mashup Score: 1
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 specifically designed for the joint modelling of longitudinal and survival data in the context of big data analytics. The jmBIG package offers efficient and scalable implementations of joint modelling algorithms, allowing for integrating large-scale healthcare datasets.By utilizing the capabilities of jmBIG, researchers and analysts can effectively handle the challenges associated with big healthcare data, such as high dimensionality and complex relationships between multiple outcomes.With the support of jmBIG, analysts can seamlessly fit Bayesian joint models, generate predictions, and evaluate the performance of the models. The package incorporates cutting-edge methodologies and harnesses the computational capabilities of parallel computing to accelerate the analysis of large-scale hea
Categories: General Medicine News, General HCPsTweet-
An article published in #BMCMedResMethodol presents jmBIG: a package that offers efficient and scalable implementations of joint modeling algorithms, allowing for integrating large-scale healthcare datasets. https://t.co/pjrV29msZd
-
-
Mashup Score: 12An evaluation of sample size requirements for developing risk prediction models with binary outcomes - BMC Medical Research Methodology - 5 month(s) ago
Background Risk prediction models are routinely used to assist in clinical decision making. A small sample size for model development can compromise model performance when the model is applied to new patients. For binary outcomes, the calibration slope (CS) and the mean absolute prediction error (MAPE) are two key measures on which sample size calculations for the development of risk models have been based. CS quantifies the degree of model overfitting while MAPE assesses the accuracy of individual predictions. Methods Recently, two formulae were proposed to calculate the sample size required, given anticipated features of the development data such as the outcome prevalence and c-statistic, to ensure that the expectation of the CS and MAPE (over repeated samples) in models fitted using MLE will meet prespecified target values. In this article, we use a simulation study to evaluate the performance of these formulae. Results We found that both formulae work reasonably well when the antic
Categories: General Medicine News, CardiologistsTweet-
RT @Richard_D_Riley: Useful work by Pavlou et al. evaluating our sample size work https://t.co/VNqERVKQEr Suggest larger n needed in situa…
-
-
Mashup Score: 4
Background Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. Results This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and st
Categories: General Medicine News, General HCPsTweet-
An article published in #BMCMedResMethodol presents SpatialWavePredict: a toolbox to characterize and forecast epidemic trajectories of infectious disease outbreaks including SARS, Ebola, and COVID-19 pandemic in the U.S. https://t.co/VQyB4hzewh
-
-
Mashup Score: 10
Background Scoping reviews are a relatively new approach to evidence synthesis and currently there exists little guidance regarding the decision to choose between a systematic review or scoping review approach when synthesising evidence. The purpose of this article is to clearly describe the differences in indications between scoping reviews and systematic reviews and to provide guidance for when a scoping review is (and is not) appropriate. Results Researchers may conduct scoping reviews instead of systematic reviews where the purpose of the review is to identify knowledge gaps, scope a body of literature, clarify concepts or to investigate research conduct. While useful in their own right, scoping reviews may also be helpful precursors to systematic reviews and can be used to confirm the relevance of inclusion criteria and potential questions. Conclusions Scoping reviews are a useful tool in the ever increasing arsenal of evidence synthesis approaches. Although conducted for differen
Categories: General Medicine News, CardiologistsTweet-
Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. https://t.co/HpEJhhKBZD
-
-
Mashup Score: 0Reporting of interventional clinical trial results in an academic center: a survey of completed studies - BMC Medical Research Methodology - 8 month(s) ago
Background The dissemination of clinical trial results is an important scientific and ethical endeavour. This survey of completed interventional studies in a French academic center describes their reporting status. Methods We explored all interventional studies sponsored by Rennes University Hospital identified on the French Open Science Monitor which tracks trials registered on EUCTR or clinicaltrials.gov, and provides an automatic assessment of the reporting of results. For each study, we ascertained the actual reporting of results using systematic searches on the hospital internal database, bibliographic databases (Google Scholar, PubMed), and by contacting all principal investigators (PIs). We describe several features (including total budget and numbers of trial participants) of the studies that did not report any results. Results The French Open Science Monitor identified 93 interventional studies, among which 10 (11%) reported results. In contrast, our survey identified 36 studi
Categories: General Medicine News, General HCPsTweet-
Reporting of interventional clinical trial results in an academic center: a survey of completed studies https://t.co/Nl6zd98B99 via @NaudetFlorian et al
-
-
Mashup Score: 8Methodological insights into ChatGPT’s screening performance in systematic reviews - BMC Medical Research Methodology - 9 month(s) ago
Background The screening process for systematic reviews and meta-analyses in medical research is a labor-intensive and time-consuming task. While machine learning and deep learning have been applied to facilitate this process, these methods often require training data and user annotation. This study aims to assess the efficacy of ChatGPT, a large language model based on the Generative Pretrained Transformers (GPT) architecture, in automating the screening process for systematic reviews in radiology without the need for training data. Methods A prospective simulation study was conducted between May 2nd and 24th, 2023, comparing ChatGPT’s performance in screening abstracts against that of general physicians (GPs). A total of 1198 abstracts across three subfields of radiology were evaluated. Metrics such as sensitivity, specificity, positive and negative predictive values (PPV and NPV), workload saving, and others were employed. Statistical analyses included the Kappa coefficient for inte
Categories: General Medicine News, Hem/OncsTweet-
Methodological insights into #ChatGPT’s screening performance in systematic reviews https://t.co/UO6wuUoAmo https://t.co/z6ezuHMsG0
-
RT @kaulcsmc: @djc795 @mvaduganathan @JAMANetworkOpen @drjohnm @AndrewFoy82 https://t.co/NJ6UdtuSuT.