Most UK scientists who publish extremely highly-cited papers do not secure funding from major public and charity funders: A descriptive analysis

The UK is one of the largest funders of health research in the world, but little is known about how health funding is spent. Our study explores whether major UK public and charitable health research funders support the research of UK-based scientists producing the most highly-cited research. To address this question, we searched for UK-based authors of peer-reviewed papers that were published between January 2006 and February 2018 and received over 1000 citations in Scopus. We explored whether these authors have held a grant from the National Institute for Health Research (NIHR), the Medical Research Council (MRC) and the Wellcome Trust and compared the results with UK-based researchers who serve currently on the boards of these bodies. From the 1,370 papers relevant to medical, biomedical, life and health sciences with more than 1000 citations in the period examined, we identified 223 individuals from a UK institution at the time of publication who were either first/last or single authors. Of those, 164 are still in UK academic institutions, while 59 are not currently in UK academia (have left the country, are retired, or work in other sectors). Of the 164 individuals, only 59 (36%; 95% CI: 29-43%) currently hold an active grant from one of the three funders. Only 79 (48%; 95% CI: 41-56%) have held an active grant from any of the three funders between 2006-2017. Conversely, 457 of the 664 board members of MRC, Wellcome Trust, and NIHR (69%; 95% CI: 65-72%) have held an active grant in the same period by any of these funders. Only 7 out of 655 board members (1.1%) were first, last or single authors of an extremely highly-cited paper. There are many reasons why the majority of the most influential UK authors do not hold a grant from the country’s major public and charitable funding bodies. Nevertheless, the results are worrisome and subscribe to similar patterns shown in the US. We discuss possible implications and suggest ways forward

Health outcomes during the 2008 financial crisis in Europe: systematic literature review

OBJECTIVE:  To systematically identify, critically appraise, and synthesise empirical studies about the impact of the 2008 financial crisis in Europe on health outcomes. DESIGN:  Systematic literature review. DATA SOURCES:  Structural searches of key databases, healthcare journals, and organisation based websites. REVIEW METHODS:  Empirical studies reporting on the impact of the financial crisis on health outcomes in Europe, published from January 2008 to December 2015, were included. All selected studies were assessed for risk of bias. Owing to the heterogeneity of studies in terms of study design and analysis and the use of overlapping datasets across studies, studies were analysed thematically per outcome, and the evidence was synthesised on different health outcomes without formal meta-analysis. RESULTS:  41 studies met the inclusion criteria, and focused on suicide, mental health, self rated health, mortality, and other health outcomes. Of those studies, 30 (73%) were deemed to be at high risk of bias, nine (22%) at moderate risk of bias, and only two (5%) at low risk of bias, limiting the conclusions that can be drawn. Although there were differences across countries and groups, there was some indication that suicides increased and mental health deteriorated during the crisis. The crisis did not seem to reverse the trend of decreasing overall mortality. Evidence on self rated health and other indicators was mixed. CONCLUSIONS:  Most published studies on the impact of financial crisis on health in Europe had a substantial risk of bias; therefore, results need to be cautiously interpreted. Overall, the financial crisis in Europe seemed to have had heterogeneous effects on health outcomes, with the evidence being most consistent for suicides and mental health. There is a need for better empirical studies, especially those focused on identifying mechanisms that can mitigate the adverse effects of the crisis

Timing and characteristics of cumulative evidence available on novel therapeutic agents receiving Food and Drug Administration accelerated approval

Context: Therapeutic agents treating serious conditions are eligible for Food and Drug Administration (FDA) accelerated approval. The clinical evidence accrued on agents receiving accelerated approval has not been systematically evaluated. Our objective was to assess the timing and characteristics of available studies. Methods: We first identified clinical studies of novel therapeutic agents receiving accelerated approval. We then (1) categorized those studies as randomized or non-randomized; (2) explored whether or not they evaluated the FDA-approved indications; and (3) documented the available treatment comparisons. We also meta-analyzed the difference in start times between randomized studies that (1) did or did not evaluate approved indications and (2) were or were not designed to evaluate the agent’s effectiveness. Findings: In total, 37 novel therapeutic agents received accelerated approval between 2000 and 2013. Our search identified 7,757 studies including 1,258,315 participants. Only one third of identified studies were randomized controlled trials. Of 1,631 randomized trials with advanced recruitment status, 906 were conducted in therapeutic areas for which agents received initial accelerated approval, 202 were in supplemental indications, and 523 were outside approved indications. Only 411/906 (45.4%) trials were designed to test the effectiveness of agents that received accelerated approval (“evaluation” trials); others used these agents as common background treatment in both arms (“background” trials). There was no detectable lag between average start times of trials conducted within and outside initially approved indications. “Evaluation” trials started on average 1.52 years, (95% CI: 0.87 to 2.17) earlier than “background” trials. Conclusions: Cumulative evidence on agents with accelerated approvals has major limitations. Most clinical studies including these agents are small and non-randomized, and about a third are conducted in unapproved areas, typically concurrently with those conducted in approved areas. Most randomized trials including these therapeutic agents are not designed to evaluate directly their clinical benefits but incorporate them as standard treatment

An umbrella review of systematic reviews on the impact of the COVID-19 pandemic on cancer prevention and management, and patient needs.

The COVID-19 pandemic led to relocation and reconstruction of health care resources and systems, and to a decrease in healthcare utilization, and this may have affected the treatment, diagnosis, prognosis, and psychosocial well-being of patients with cancer. We aimed to summarize and quantify the evidence on the impact of the COVID-19 pandemic on the full spectrum of cancer care. An umbrella review was undertaken to summarize and quantify the findings from systematic reviews on impact of the COVID-19 pandemic on cancer treatment modification, delays, and cancellations; delays or cancellations in screening and diagnosis; psychosocial well-being, financial distress, and use of telemedicine as well as on other aspects of cancer care. PubMed and WHO COVID-19 Database was searched for relevant systematic reviews with or without meta-analysis published before November 29th, 2022. Abstract, full text screening and data extraction were performed by two independent reviewers. AMSTAR-2 was used for critical appraisal of included systematic reviews. 51 systematic reviews evaluating different aspects of cancer care were included in our analysis. Most reviews were based on observational studies judged to be at medium and high risk of bias. Only 2 of the included reviews had high or moderate scores based on AMSTAR-2. Findings suggest treatment modifications in cancer care during the pandemic versus the pre-pandemic period were based on low level of evidence. Different degrees of delays and cancellations in cancer treatment, screening and diagnosis were observed, with low-and-middle income countries and countries that implemented lockdowns being disproportionally affected. A shift from in-person appointments to telemedicine use was observed, but utility of telemedicine, challenges in implementation and cost-effectiveness in different areas of cancer care were little explored. Evidence was consistent in suggesting psychosocial well-being (e.g., depression, anxiety, and social activities) of patients with cancer deteriorated, and cancer patients experienced financial distress, albeit results were in general not compared to pre-pandemic levels. Impact of cancer care disruption during the pandemic on cancer prognosis was little explored. In conclusion, Substantial but heterogenous impact of COVID-19 pandemic on cancer care has been observed. Evidence gaps exist on this topic, with mid- and long-term impact on cancer care being most uncertain

Moving Beyond Noninformative Priors: Why and How to Choose Weakly Informative Priors in Bayesian Analyses

Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods. However, protocols for incorporating prior information, the defining characteristic of Bayesian philosophy, are nearly nonexistent in the ecological literature. Here, I hope to encourage the use of weakly informative priors in ecology and evolution by providing a ‘consumer\u27s guide’ to weakly informative priors. The first section outlines three reasons why ecologists should abandon noninformative priors: 1) common flat priors are not always noninformative, 2) noninformative priors provide the same result as simpler frequentist methods, and 3) noninformative priors suffer from the same high type I and type M error rates as frequentist methods. The second section provides a guide for implementing informative priors, wherein I detail convenient ‘reference’ prior distributions for common statistical models (i.e. regression, ANOVA, hierarchical models). I then use simulations to visually demonstrate how informative priors influence posterior parameter estimates. With the guidelines provided here, I hope to encourage the use of weakly informative priors for Bayesian analyses in ecology. Ecologists can and should debate the appropriate form of prior information, but should consider weakly informative priors as the new ‘default’ prior for any Bayesian model

Quantifying Selective Reporting and the Proteus Phenomenon for Multiple Datasets with Similar Bias

Meta-analyses play an important role in synthesizing evidence from diverse studies and datasets that address similar questions. A major obstacle for meta-analyses arises from biases in reporting. In particular, it is speculated that findings which do not achieve formal statistical significance are less likely reported than statistically significant findings. Moreover, the patterns of bias can be complex and may also depend on the timing of the research results and their relationship with previously published work. In this paper, we present an approach that is specifically designed to analyze large-scale datasets on published results. Such datasets are currently emerging in diverse research fields, particularly in molecular medicine. We use our approach to investigate a dataset on Alzheimer's disease (AD) that covers 1167 results from case-control studies on 102 genetic markers. We observe that initial studies on a genetic marker tend to be substantially more biased than subsequent replications. The chances for initial, statistically non-significant results to be published are estimated to be about 44% (95% CI, 32% to 63%) relative to statistically significant results, while statistically non-significant replications have almost the same chance to be published as statistically significant replications (84%; 95% CI, 66% to 107%). Early replications tend to be biased against initial findings, an observation previously termed Proteus phenomenon: The chances for non-significant studies going in the same direction as the initial result are estimated to be lower than the chances for non-significant studies opposing the initial result (73%; 95% CI, 55% to 96%). Such dynamic patters in bias are difficult to capture by conventional methods, where typically simple publication bias is assumed to operate. Our approach captures and corrects for complex dynamic patterns of bias, and thereby helps generating conclusions from published results that are more robust against the presence of different coexisting types of selective reporting