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Mashup Score: 5Open science saves lives: lessons from the COVID-19 pandemic - BMC Medical Research Methodology - 10 month(s) ago
In the last decade Open Science principles have been successfully advocated for and are being slowly adopted in different research communities. In response to the COVID-19 pandemic many publishers and researchers have sped up their adoption of Open Science practices, sometimes embracing them fully and sometimes partially or in a sub-optimal manner. In this article, we express concerns about the violation of some of the Open Science principles and its potential impact on the quality of research output. We provide evidence of the misuses of these principles at different stages of the scientific process. We call for a wider adoption of Open Science practices in the hope that this work will encourage a broader endorsement of Open Science principles and serve as a reminder that science should always be a rigorous process, reliable and transparent, especially in the context of a pandemic where research findings are being translated into practice even more rapidly. We provide all data and scr
Categories: General Medicine News, CardiologistsTweet
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Mashup Score: 11Contextual effects: how to, and how not to, quantify them - BMC Medical Research Methodology - 10 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
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Mashup Score: 2
Background Selective reporting of results from only well-performing cut-offs leads to biased estimates of accuracy in primary studies of questionnaire-based screening tools and in meta-analyses that synthesize results. Individual participant data meta-analysis (IPDMA) of sensitivity and specificity at each cut-off via bivariate random-effects models (BREMs) can overcome this problem. However, IPDMA is laborious and depends on the ability to successfully obtain primary datasets, and BREMs ignore the correlation between cut-offs within primary studies. Methods We compared the performance of three recent multiple cut-off models developed by Steinhauser et al., Jones et al., and Hoyer and Kuss, that account for missing cut-offs when meta-analyzing diagnostic accuracy studies with multiple cut-offs, to BREMs fitted at each cut-off. We used data from 22 studies of the accuracy of the Edinburgh Postnatal Depression Scale (EPDS; 4475 participants, 758 major depression cases). We fitted each of
Categories: General Medicine News, CardiologistsTweet-
Multiple cut-off models appear to be the favorable methods for meta-analysis of diagnostic test accuracy data when only published data are available. While collecting IPD is expensive/time consuming, it can facilitate subgroup analyses https://t.co/T1Qph0oncZ https://t.co/EGPp8esLKD
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Mashup Score: 0Adaptive designs in critical care trials: a simulation study - BMC Medical Research Methodology - 1 year(s) ago
Background Adaptive clinical trials are growing in popularity as they are more flexible, efficient and ethical than traditional fixed designs. However, notwithstanding their increased use in assessing treatments for COVID-19, their use in critical care trials remains limited. A better understanding of the relative benefits of various adaptive designs may increase their use and interpretation. Methods Using two large critical care trials (ADRENAL. ClinicalTrials.gov number, NCT01448109. Updated 12-12-2017; NICE-SUGAR. ClinicalTrials.gov number, NCT00220987. Updated 01-29-2009), we assessed the performance of three frequentist and two bayesian adaptive approaches. We retrospectively re-analysed the trials with one, two, four, and nine equally spaced interims. Using the original hypotheses, we conducted 10,000 simulations to derive error rates, probabilities of making an early correct and incorrect decision, expected sample size and treatment effect estimates under the null scenario (no t
Categories: General Medicine News, General Journals & SocietTweet-
Adaptive designs in critical care trials: a simulation study https://t.co/4xs0xNEh3c
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Mashup Score: 0Data visualisation approaches for component network meta-analysis: visualising the data structure - BMC Medical Research Methodology - 1 year(s) ago
Background Health and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other components which differ across trials. Multicomponent interventions can be synthesised using component NMA (CNMA). CNMA is limited by the structure of the available evidence, but it is not always straightforward to visualise such complex evidence networks. The aim of this paper is to develop tools to visualise the structure of complex evidence networks to support CNMA. Methods We performed a citation review of two key CNMA methods papers to identify existing published CNMA analyses and reviewed how they graphically represent intervention complexity and comparisons across trials. Building on identified shortcomings of existing visualisation approaches, we propose three approaches to standardise visualis
Categories: Cardiologists, Latest HeadlinesTweet-
As component network meta-analyses becomes more widely used for the evaluation of multicomponent interventions, novel specific visualisations will be important to aid understanding of the complex data structure and facilitate interpretation of results https://t.co/owzQsR22q1 https://t.co/Cfu9dFGSIa
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Mashup Score: 0Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise - BMC Medical Research Methodology - 1 year(s) ago
Background Intensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference formulations or ICU random effects (RE), which have not been previously compared. Methods From calendar year 2016 data of an adult ICU registry-database (Australia & New Zealand Intensive Care Society (ANZICS) CORE), LOS predictive models were established using linear (LMM) and generalised linear (GLMM) mixed models. Model fixed effects quality-metric formulations were estimated as RALOSR for LMM (geometric mean derived from log(ICU LOS)) and GLMM (day) and observed minus expected ICU LOS (OMELOS from GLMM). Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). Rob
Categories: General Medicine Journals and Societies, Latest HeadlinesTweet-
Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise https://t.co/RA8hG4YkF4
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Mashup Score: 0
Background Stringent requirements exist regarding the transparency of the study selection process and the reliability of results. A 2-step selection process is generally recommended; this is conducted by 2 reviewers independently of each other (conventional double-screening). However, the approach is resource intensive, which can be a problem, as systematic reviews generally need to be completed within a defined period with a limited budget. The aim of the following methodological systematic review was to analyse the evidence available on whether single screening is equivalent to double screening in the screening process conducted in systematic reviews. Methods We searched Medline, PubMed and the Cochrane Methodology Register (last search 10/2018). We also used supplementary search techniques and sources (“similar articles” function in PubMed, conference abstracts and reference lists). We included all evaluations comparing single with double screening. Data were summarized in a structu
Categories: Hem/Oncs, Latest HeadlinesTweet-
Single screening versus conventional double screening for study selection in systematic reviews: a methodological systematic review https://t.co/6ySFXH0i8Y via @PieperDawid et al
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Mashup Score: 2
Background Pragmatic clinical trials (PCTs) are designed to reflect how an investigational treatment would be applied in clinical practice. As such, unlike their explanatory counterparts, they measure therapeutic effectiveness and are capable of generating high-quality real-world evidence. However, the conduct of PCTs remains extremely rare. The scarcity of such studies has contributed to the emergence of the efficacy-effectiveness gap and has led to calls for launching more of them, including in the field of oncology. This analysis aimed to identify self-labelled pragmatic trials of antineoplastic interventions and to evaluate whether their use of this label was justified. Methods We searched PubMed® and Embase® for publications corresponding with studies that investigated antitumor therapies and that were tagged as pragmatic in their titles, abstracts and/or index terms. Subsequently, we consulted all available source documents for the included trials and extracted relevant informati
Categories: Hem/Onc News and Journals, Latest HeadlinesTweet-
Proud to share EORTC fellow @robbesaesen's article discussing the learnings from his recent #publication on the use of #PragmaticTrials in #oncology. Publication available here: https://t.co/doJfV97vyV #ClinicalTrials #CancerResearch https://t.co/X3H8VvvIhA
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Mashup Score: 10
Background Having an appropriate sample size is important when developing a clinical prediction model. We aimed to review how sample size is considered in studies developing a prediction model for a binary outcome. Methods We searched PubMed for studies published between 01/07/2020 and 30/07/2020 and reviewed the sample size calculations used to develop the prediction models. Using the available information, we calculated the minimum sample size that would be needed to estimate overall risk and minimise overfitting in each study and summarised the difference between the calculated and used sample size. Results A total of 119 studies were included, of which nine studies provided sample size justification (8%). The recommended minimum sample size could be calculated for 94 studies: 73% (95% CI: 63–82%) used sample sizes lower than required to estimate overall risk and minimise overfitting including 26% studies that used sample sizes lower than required to estimate overall risk only. A si
Categories: Healthcare Professionals, Latest HeadlinesTweet-
Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review https://t.co/U0piAtyJyT via @pauladhiman et al @Argenscore @ovidiogarciav @pomyers @FaisalBakaeen @mmamas1973 https://t.co/XujeMwUAsU
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Outline of the publication process with its potential issues and solutions proposed by Open Science https://t.co/aW74dIxQZK https://t.co/bhFMq1GSZa