Randomized controlled studies have advantages, but there are other valuable sources of data.
As Thomas Frieden, who directed the Centers for Disease Control and Prevention under Mr. Obama, wrote in a 2017 New England Journal of Medicine article: “Elevating RCTs at the expense of other potentially highly valuable sources of data is counterproductive.” Such limitations affect their use for “urgent health issues, such as infectious disease outbreaks.” He added: “No study design is flawless, and conflicting findings can emerge from all types of studies.”
Two randomized trials of Gilead’s antiviral drug remdesivir show how such studies can produce inconclusive results. A randomized trial in China, published in the Lancet in May, enrolled 237 patients. The study found no significant clinical benefit over a placebo, but most of the patients were severely ill when treated. Patients who had symptoms for 10 or fewer days, however, were 25% less likely to die. Similarly, a randomized National Institutes of Health trial with 1,063 patients found the drug reduced average recovery time by four days and the risk of death by 30%, but the survival benefit was statistically insignificant.
Some experts have dismissed the antimalarial hydroxychloroquine, or HCQ, even though more than a dozen observational studies have found it beneficial. A retrospective observational study of Covid-infected nursing-home residents in France, for instance, found those treated with HCQ and azithromycin were 40% less likely to die.
But a few randomized controlled trials found no benefit. A Spanish randomized trial of HCQ for prophylaxis found it didn’t reduce risk of illness among a large group of people exposed in nursing homes, households and health-care settings. Yet two-thirds of the subjects “reported routine use of masks at the time of exposure,” so they were probably less likely to be infected. Nursing-home residents, who may be less likely to wear masks, were 50% less likely to become sick if they took HCQ. But this finding was statistically insignificant, because the trial included only 293 residents.
This is a nice, well-sourced piece in The American Thinker
In contrast to the Covid19 attributed deaths, the number for deaths from all causes is a hard number. The deaths from all causes number exposes the current mass panic as an historical aberration and confirms the evidence that the mass panic has been engineered by politicians and a biased medical establishment.The Covid-19 Pandemic is Ending
The 11 year weekly deaths from all causes graph (here and below,) shows that the 2020 flu season was about normal until it spiked for eight weeks in April and May due to Covid19 (CDC data here and here). The April high of 78,000 was significantly higher than the previous multi-year high of 67,000 in 2018, but just as in previous years, the temporary spike rapidly declined toward baseline.
The 11 year baseline increased from about 45,000 in 2009 to about 51,000 in 2019, generally as a result of increasing population. After this year’s April spike to 78,000, the current weekly all causes deaths number is down to 55,000, about 4,000 higher than the projected baseline. We are at week 32 of the year, and flu season is about to kick in. What does this mean regarding the Covid19 epidemic?
I’ve been pounding on the need for fast, frequent testing but it’s clear from some of the comments to The Beginning of the End that I have failed to convey some fundamental points. A seemingly sophisticated objection is to note that given background prevalence rates even a fairly specific test will result in a high fraction of false positives among those who test positive. (This is the standard Bayesian doctor puzzle .) It’s nice to see people doing the Bayes calculation but some of them are then drawing the wrong conclusion.
DUH: More Evidence That Shutdowns Are Useless.