This is a confession building on a conversation with David Sackett in 2004 when I shared with him some personal adventures in evidence-based medicine (EBM), the movement that he had spearheaded. The ...narrative is expanded with what ensued in the subsequent 12 years. EBM has become far more recognized and adopted in many places, but not everywhere, for example, it never acquired much influence in the USA. As EBM became more influential, it was also hijacked to serve agendas different from what it originally aimed for. Influential randomized trials are largely done by and for the benefit of the industry. Meta-analyses and guidelines have become a factory, mostly also serving vested interests. National and federal research funds are funneled almost exclusively to research with little relevance to health outcomes. We have supported the growth of principal investigators who excel primarily as managers absorbing more money. Diagnosis and prognosis research and efforts to individualize treatment have fueled recurrent spurious promises. Risk factor epidemiology has excelled in salami-sliced data-dredged articles with gift authorship and has become adept to dictating policy from spurious evidence. Under market pressure, clinical medicine has been transformed to finance-based medicine. In many places, medicine and health care are wasting societal resources and becoming a threat to human well-being. Science denialism and quacks are also flourishing and leading more people astray in their life choices, including health. EBM still remains an unmet goal, worthy to be attained.
Currently, there is a growing interest in ensuring the transparency and reproducibility of the published scientific literature. According to a previous evaluation of 441 biomedical journals articles ...published in 2000-2014, the biomedical literature largely lacked transparency in important dimensions. Here, we surveyed a random sample of 149 biomedical articles published between 2015 and 2017 and determined the proportion reporting sources of public and/or private funding and conflicts of interests, sharing protocols and raw data, and undergoing rigorous independent replication and reproducibility checks. We also investigated what can be learned about reproducibility and transparency indicators from open access data provided on PubMed. The majority of the 149 studies disclosed some information regarding funding (103, 69.1% 95% confidence interval, 61.0% to 76.3%) or conflicts of interest (97, 65.1% 56.8% to 72.6%). Among the 104 articles with empirical data in which protocols or data sharing would be pertinent, 19 (18.3% 11.6% to 27.3%) discussed publicly available data; only one (1.0% 0.1% to 6.0%) included a link to a full study protocol. Among the 97 articles in which replication in studies with different data would be pertinent, there were five replication efforts (5.2% 1.9% to 12.2%). Although clinical trial identification numbers and funding details were often provided on PubMed, only two of the articles without a full text article in PubMed Central that discussed publicly available data at the full text level also contained information related to data sharing on PubMed; none had a conflicts of interest statement on PubMed. Our evaluation suggests that although there have been improvements over the last few years in certain key indicators of reproducibility and transparency, opportunities exist to improve reproducible research practices across the biomedical literature and to make features related to reproducibility more readily visible in PubMed.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objective To assess differences in estimated treatment effects for mortality between observational studies with routinely collected health data (RCD; that are published before trials are available) ...and subsequent evidence from randomized controlled trials on the same clinical question.Design Meta-epidemiological survey.Data sources PubMed searched up to November 2014.Methods Eligible RCD studies were published up to 2010 that used propensity scores to address confounding bias and reported comparative effects of interventions for mortality. The analysis included only RCD studies conducted before any trial was published on the same topic. The direction of treatment effects, confidence intervals, and effect sizes (odds ratios) were compared between RCD studies and randomized controlled trials. The relative odds ratio (that is, the summary odds ratio of trial(s) divided by the RCD study estimate) and the summary relative odds ratio were calculated across all pairs of RCD studies and trials. A summary relative odds ratio greater than one indicates that RCD studies gave more favorable mortality results.Results The evaluation included 16 eligible RCD studies, and 36 subsequent published randomized controlled trials investigating the same clinical questions (with 17 275 patients and 835 deaths). Trials were published a median of three years after the corresponding RCD study. For five (31%) of the 16 clinical questions, the direction of treatment effects differed between RCD studies and trials. Confidence intervals in nine (56%) RCD studies did not include the RCT effect estimate. Overall, RCD studies showed significantly more favorable mortality estimates by 31% than subsequent trials (summary relative odds ratio 1.31 (95% confidence interval 1.03 to 1.65; I2=0%)).Conclusions Studies of routinely collected health data could give different answers from subsequent randomized controlled trials on the same clinical questions, and may substantially overestimate treatment effects. Caution is needed to prevent misguided clinical decision making.
Background
Estimates of community spread and infection fatality rate (IFR) of COVID‐19 have varied across studies. Efforts to synthesize the evidence reach seemingly discrepant conclusions.
Methods
...Systematic evaluations of seroprevalence studies that had no restrictions based on country and which estimated either total number of people infected and/or aggregate IFRs were identified. Information was extracted and compared on eligibility criteria, searches, amount of evidence included, corrections/adjustments of seroprevalence and death counts, quantitative syntheses and handling of heterogeneity, main estimates and global representativeness.
Results
Six systematic evaluations were eligible. Each combined data from 10 to 338 studies (9‐50 countries), because of different eligibility criteria. Two evaluations had some overt flaws in data, violations of stated eligibility criteria and biased eligibility criteria (eg excluding studies with few deaths) that consistently inflated IFR estimates. Perusal of quantitative synthesis methods also exhibited several challenges and biases. Global representativeness was low with 78%‐100% of the evidence coming from Europe or the Americas; the two most problematic evaluations considered only one study from other continents. Allowing for these caveats, four evaluations largely agreed in their main final estimates for global spread of the pandemic and the other two evaluations would also agree after correcting overt flaws and biases.
Conclusions
All systematic evaluations of seroprevalence data converge that SARS‐CoV‐2 infection is widely spread globally. Acknowledging residual uncertainties, the available evidence suggests average global IFR of ~0.15% and ~1.5‐2.0 billion infections by February 2021 with substantial differences in IFR and in infection spread across continents, countries and locations.
The article discusses the key aspects of the recently released 2017 American College of Cardiology/American Heart Association (ACC/AHA) guidelines on the diagnosis and treatment of hypertension. Some ...of the main benefits of the new guidelines are highlighted.
In this Viewpoint, John Ioannidis argues against abandoning the notion and language of statistical significance, which has been proposed as a means to diminish oversimplistic interpretations of ...clinical research. A significance filter in some form is essential for distinguishing signal from noise, he writes, and emphasizes that predefined study design choices, prespecified statistical analyses, transparent and documented deviations from either, and improvement in researchers' statistical numeracy can minimize overly subjective interpretations of whatever significance measure is used.
Newly discovered true (non-null) associations often have inflated effects compared with the true effect sizes. I discuss here the main reasons for this inflation. First, theoretical considerations ...prove that when true discovery is claimed based on crossing a threshold of statistical significance and the discovery study is underpowered, the observed effects are expected to be inflated. This has been demonstrated in various fields ranging from early stopped clinical trials to genome-wide associations. Second, flexible analyses coupled with selective reporting may inflate the published discovered effects. The vibration ratio (the ratio of the largest vs. smallest effect on the same association approached with different analytic choices) can be very large. Third, effects may be inflated at the stage of interpretation due to diverse conflicts of interest. Discovered effects are not always inflated, and under some circumstances may be deflated—for example, in the setting of late discovery of associations in sequentially accumulated overpowered evidence, in some types of misclassification from measurement error, and in conflicts causing reverse biases. Finally, I discuss potential approaches to this problem. These include being cautious about newly discovered effect sizes, considering some rational down-adjustment, using analytical methods that correct for the anticipated inflation, ignoring the magnitude of the effect (if not necessary), conducting large studies in the discovery phase, using strict protocols for analyses, pursuing complete and transparent reporting of all results, placing emphasis on replication, and being fair with interpretation of results.
CONTEXT Controversy and uncertainty ensue when the results of clinical research
on the effectiveness of interventions are subsequently contradicted. Controversies
are most prominent when high-impact ...research is involved. OBJECTIVES To understand how frequently highly cited studies are contradicted or
find effects that are stronger than in other similar studies and to discern
whether specific characteristics are associated with such refutation over
time. DESIGN All original clinical research studies published in 3 major general
clinical journals or high-impact-factor specialty journals in 1990-2003 and
cited more than 1000 times in the literature were examined. MAIN OUTCOME MEASURE The results of highly cited articles were compared against subsequent
studies of comparable or larger sample size and similar or better controlled
designs. The same analysis was also performed comparatively for matched studies
that were not so highly cited. RESULTS Of 49 highly cited original clinical research studies, 45 claimed that
the intervention was effective. Of these, 7 (16%) were contradicted by subsequent
studies, 7 others (16%) had found effects that were stronger than those of
subsequent studies, 20 (44%) were replicated, and 11 (24%) remained largely
unchallenged. Five of 6 highly-cited nonrandomized studies had been contradicted
or had found stronger effects vs 9 of 39 randomized controlled trials (P = .008). Among randomized trials, studies with
contradicted or stronger effects were smaller (P = .009)
than replicated or unchallenged studies although there was no statistically
significant difference in their early or overall citation impact. Matched
control studies did not have a significantly different share of refuted results
than highly cited studies, but they included more studies with “negative”
results. CONCLUSIONS Contradiction and initially stronger effects are not unusual in highly
cited research of clinical interventions and their outcomes. The extent to
which high citations may provoke contradictions and vice versa needs more
study. Controversies are most common with highly cited nonrandomized studies,
but even the most highly cited randomized trials may be challenged and refuted
over time, especially small ones.
The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, ...well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Background and Aims
The most restrictive nonpharmaceutical interventions (NPIs) for controlling the spread of COVID‐19 are mandatory stay‐at‐home and business closures. Given the consequences of ...these policies, it is important to assess their effects. We evaluate the effects on epidemic case growth of more restrictive NPIs (mrNPIs), above and beyond those of less‐restrictive NPIs (lrNPIs).
Methods
We first estimate COVID‐19 case growth in relation to any NPI implementation in subnational regions of 10 countries: England, France, Germany, Iran, Italy, Netherlands, Spain, South Korea, Sweden and the United States. Using first‐difference models with fixed effects, we isolate the effects of mrNPIs by subtracting the combined effects of lrNPIs and epidemic dynamics from all NPIs. We use case growth in Sweden and South Korea, 2 countries that did not implement mandatory stay‐at‐home and business closures, as comparison countries for the other 8 countries (16 total comparisons).
Results
Implementing any NPIs was associated with significant reductions in case growth in 9 out of 10 study countries, including South Korea and Sweden that implemented only lrNPIs (Spain had a nonsignificant effect). After subtracting the epidemic and lrNPI effects, we find no clear, significant beneficial effect of mrNPIs on case growth in any country. In France, for example, the effect of mrNPIs was +7% (95% CI: −5%‐19%) when compared with Sweden and + 13% (−12%‐38%) when compared with South Korea (positive means pro‐contagion). The 95% confidence intervals excluded 30% declines in all 16 comparisons and 15% declines in 11/16 comparisons.
Conclusions
While small benefits cannot be excluded, we do not find significant benefits on case growth of more restrictive NPIs. Similar reductions in case growth may be achievable with less‐restrictive interventions.
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