Search

Background: The heterogeneity statistic I(2), interpreted as the percentage of variability due to heterogeneity between studies rather than sampling error, depends on precision, that is, the size of the studies included.Methods: Based on a real meta-analysis, we simulate artificially 'inflating' the sample size under the random effects model. For a given inflation factor M = 1, 2, 3,... and for each trial i, we create a M-inflated trial by drawing a treatment effect estimate from the random effects model, using s(i)(2)/M as within-trial sampling variance.Results: As precision increases, while estimates of the heterogeneity variance tau(2) remain unchanged on average, estimates of I(2) increase rapidly to nearly 100%. A similar phenomenon is apparent in a sample of 157 meta-analyses.Conclusion: When deciding whether or not to pool treatment estimates in a meta-analysis, the yard-stick should be the clinical relevance of any heterogeneity present. tau(2), rather than I(2), is the appropriate measure for this purpose. [ABSTRACT FROM AUTHOR]

Background: Simpson's paradox is sometimes referred to in the areas of epidemiology and clinical research. It can also be found in meta-analysis of randomized clinical trials. However, though readers are able to recalculate examples from hypothetical as well as real data, they may have problems to easily figure where it emerges from.Method: First, two kinds of plots are proposed to illustrate the phenomenon graphically, a scatter plot and a line graph. Subsequently, these can be overlaid, resulting in a overlay plot. The plots are applied to the recent large meta-analysis of adverse effects of rosiglitazone on myocardial infarction and to an example from the literature. A large set of meta-analyses is screened for further examples.Results: As noted earlier by others, occurrence of Simpson's paradox in the meta-analytic setting, if present, is associated with imbalance of treatment arm size. This is well illustrated by the proposed plots. The rosiglitazone meta-analysis shows an effect reversion if all trials are pooled. In a sample of 157 meta-analyses, nine showed an effect reversion after pooling, though non-significant in all cases.Conclusion: The plots give insight on how the imbalance of trial arm size works as a confounder, thus producing Simpson's paradox. Readers can see why meta-analytic methods must be used and what is wrong with simple pooling. [ABSTRACT FROM AUTHOR]

Background: Healthcare workers (HCW) are at increased risk of infection with SARS-CoV-2. Vulnerable patient populations in particular must be protected, and clinics should not become transmission hotspots to avoid delaying medical treatments independent of COVID. Because asymptomatic transmission has been described, routine screening of asymptomatic HCW would potentially be able to interrupt chains of infection through early detection.Methods: A systematic search was conducted in the Cochrane COVID-19 Study Register, Web of Science and WHO COVID-19 Global literature on coronavirus with regard to non-incident related testing of healthcare workers using polymerase chain reaction on May 4th 2021. Studies since January 2020 were included. An assessment of risk of bias and representativeness was performed.Results: The search identified 39 studies with heterogeneous designs. Data collection of the included studies took place from January to August 2020. The studies were conducted worldwide and the sample size of the included HCW ranged from 70 to 9449 participants. In total, 1000 of 51,700 (1.9%) asymptomatic HCW were tested positive for SARS-CoV-2 using PCR testing. The proportion of positive test results ranged between 0 and 14.3%. No study reported on HCW-screening related reductions in infected person-days.Discussion and Conclusions: The heterogeneous proportions might be explained by different regional incidences, lock-downs, and pre-analytical pitfalls that reduce the sensitivity of the nasopharyngeal swab. The very high prevalence in some studies indicates that screening HCW for SARS-CoV-2 may be important particularly in geographical regions and pandemic periods with a high-incidence. With low numbers and an increasing rate of vaccinated HCW, a strict cost-benefit consideration must be made, especially in times of low incidences. Since we found no studies that reported on HCW-screening related reductions in infected person-days, re-evaluation should be done when these are available. [ABSTRACT FROM AUTHOR]

Background: The German Research Foundation (DFG) and the Federal Ministry of Education and Research (BMBF) initiated large research programs to foster high quality clinical research in the academic area. These investigator initiated trials (IITs) cover important areas of medical research and often go beyond the scope of industry sponsored trials (ISTs). The purpose of this project was to understand to what extent results of randomized controlled IITs and ISTs have an impact on medical practice, measured by their availability for decisions in healthcare and their implementation in clinical practice. We aimed to determine study characteristics influencing a trial's impact such as type of sponsor and place of conduct. In this article, we describe the rationale and design of this project and present the characteristics of the trials included in our study cohort.Methods: The research impact of the following sub-cohorts was compared: German IITs (funded by DFG and BMBF or by other German non-commercial organizations), international IITs (without German contribution), German ISTs, and international ISTs. Trials included were drawn from the DFG-/BMBF-Websites, the German Clinical Trials Register, and from ClinicalTrials.gov . Research impact was measured as follows: 1) proportion of published trials, 2) time to publication, 3) proportion of publications appropriately indexed in biomedical databases, 4) proportion of openly accessible publications, 5) broadness of publication's target group, 6) citation of publications by systematic reviews or meta-analyses, and 7) appearance of publications or citing systematic reviews or meta-analyses in clinical practice guidelines. We also aimed to identify study characteristics associated with the impact of trials.Results: We included 691 trials: 120 German IITs, 200 International IITs, 171 German ISTs and 200 International ISTs. The median number of participants was 150, 30% were international trials and 70% national trials, 48% drug-trials and 52% non-drug trials. Overall, 72% of the trials had one pre-defined primary endpoint, 28% two or more (max. 36).Conclusions: The results of this project deepen our understanding of the impact of biomedical research on clinical practice and healthcare policy, add important insights for the efficient allocation of scarce research resources and may facilitate providing accountability to the different stakeholders involved. [ABSTRACT FROM AUTHOR]