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Archive of posts filed under the Public Health category.

Sequential Bayesian Designs for Rapid Learning in COVID-19 Clinical Trials

This from Frank Harrell looks important: This trial will adopt a Bayesian framework. Continuous learning from data and computation of probabilities that are directly applicable to decision making in the face of uncertainty are hallmarks of the Bayesian approach. Bayesian sequential designs are the simplest of flexible designs, and continuous learning capitalizes on their efficiency, […]

Thank you, James Watson. Thank you, Peter Ellis. (Lancet: You should do the right thing and credit them for your retraction. Actually, do one better and invite them to write a joint editorial in your journal.)

So, Lancet issued a retraction of that controversy hydro-oxy-choloro-supercalifragilisticexpialadocious paper. From three of the four authors of the now-retracted paper: After publication of our Lancet Article, several concerns were raised with respect to the veracity of the data and analyses conducted by Surgisphere Corporation and its founder and our co-author, Sapan Desai, in our publication. […]

Can someone build a Bayesian tool that takes into account your symptoms and where you live to estimate your probability of having coronavirus?

Carl Mears writes: I’m married to a doctor who does primary care with a mostly disadvantaged patient base. The problem her patients face is if they get tested for COVID, they are supposed to self quarantine until they get their test results, which currently takes something like a week. Also, their *family* is supposed to […]

This one’s important: Bayesian workflow for disease transmission modeling in Stan

Léo Grinsztajn, Elizaveta Semenova, Charles Margossian, and Julien Riou write: This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the COVID-19 outbreak and doing Bayesian inference. Bayesian modeling provides a principled way to quantify uncertainty and incorporate prior knowledge into the […]

This one’s for the Lancet editorial board: A trolley problem for our times (involving a plate of delicious cookies and a steaming pile of poop)

A trolley problem for our times OK, I couldn’t quite frame this one as a trolley problem—maybe those of you who are more philosophically adept than I am can do this—so I set it up as a cookie problem? Here it is: Suppose someone was to knock on your office door and use some mix […]

“Note sure what the lesson for data analysis quality control is here is here, but interesting to wonder about how that mistake was not caught pre-publication.”

The Journal of the American Medical Association published a correction notice with perhaps the most boring title ever written: Incorrect Data Due to Incorrect Conversion Factor In the Original Investigation entitled “Effect of Intravenous Acetaminophen vs Placebo Combined With Propofol or Dexmedetomidine on Postoperative Delirium Among Older Patients Following Cardiac Surgery: The DEXACET Randomized Clinical […]

“The good news about this episode is that it’s kinda shut up those people who were criticizing that Stanford antibody study because it was an un-peer-reviewed preprint. . . .” and a P.P.P.S. with Paul Alper’s line about the dead horse

People keep emailing me about this recently published paper, but I already said I’m not going to write about it. So I’ll mask the details. Philippe Lemoine writes: So far it seems you haven’t taken a close look at the paper yourself and I’m hoping that you will, because I’m curious to know what you […]

An open letter expressing concerns regarding the statistical analysis and data integrity of a recently published and publicized paper

James Watson prepared this open letter to **, **, **, and **, authors of ** and to ** (editor of **). The letter has approximately 96,032 signatures from approximately 6 continents. And I heard a rumor that they have contacts at the Antarctic Polar Station who are going to sign the thing once they can […]

This is not a post about remdesivir.

Someone pointed me to this post by a doctor named Daniel Hopkins on a site called, expressing skepticism about a new study of remdesivir. I guess some work has been done following up on that trial on 18 monkeys. From the KevinMD post: On April 29th Anthony Fauci announced the National Institute of Allergy […]

Last post on hydroxychloroquine (perhaps)

James “not this guy” Watson writes: The Lancet study has already been consequential, for example, the WHO have decided to remove the hydroxychloroquine arm from their flagship SOLIDARITY trial. Thanks in part to the crowdsourcing of data sleuthing on your blog, I have an updated version of doubts concerning the data reliability/veracity. 1/ Ozzy numbers: […]

This controversial hydroxychloroquine paper: What’s Lancet gonna do about it?

Peer review is not a form of quality control In the past month there’s been a lot of discussion of the flawed Stanford study of coronavirus prevalence—it’s even hit the news—and one thing came up was that the article under discussion was just a preprint—it wasn’t even peer reviewed! For example, in a NYT op-ed: […]

Be careful when estimating years of life lost: quick-and-dirty estimates of attributable risk are, well, quick and dirty.

Peter Morfeld writes: Global burden of disease (GBD) studies and environmental burden of disease (EBD) studies are supported by hundreds of scientifically well-respected co-authors, are published in high level journals, are cited world wide and have a large impact on health institutions‘ reports and related political discussions. The main metrics used to calculate the impact […]

Hydroxychloroquine update

Following up on our earlier post, James “not the cancer cure guy” Watson writes: I [Watson] wanted to relay a few extra bits of information that have come to light over the weekend. The study only has 4 authors which is weird for a global study in 96,000 patients (and no acknowledgements at the end […]

Doubts about that article claiming that hydroxychloroquine/chloroquine is killing people

James Watson (no, not the one who said that cancer would be cured by 2000, and not this guy either) writes: You may have seen the paper that came out on Friday in the Lancet on hydroxychloroquine/chloroquine in COVID19 hospitalised patients. It’s got quite a lot of media attention already. This is a retrospective study […]

New report on coronavirus trends: “the epidemic is not under control in much of the US . . . factors modulating transmission such as rapid testing, contact tracing and behavioural precautions are crucial to offset the rise of transmission associated with loosening of social distancing . . .”

Juliette Unwin et al. write: We model the epidemics in the US at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the time-varying reproduction number (the average number of secondary infections caused by an infected person), the number of individuals that have been infected and […]

OK, here’s a hierarchical Bayesian analysis for the Santa Clara study (and other prevalence studies in the presence of uncertainty in the specificity and sensitivity of the test)

After writing some Stan programs to analyze that Santa Clara coronavirus antibody study, I thought it could be useful to write up what we did more formally so that future researchers could use these methods more easily. So Bob Carpenter and I wrote an article, Bayesian analysis of tests with unknown specificity and sensitivity: When […] A COVID-19 collaboration platform.

Following up on today’s post on design of studies for coronavirus treatments and vaccines, Z points to this site, which states: In the U.S. only a few COVID-19 randomized clinical trials (RCTs) have been centrally organized, e.g. by NIAID, PCORI and individual PIs. Over 400 such trials have been registered on with dozens being […]

This one’s important: Designing clinical trials for coronavirus treatments and vaccines

I’ve had various thoughts regarding clinical trials for coronavirus treatments and vaccines, and then I came across thoughtful posts by Thomas Lumley and Joseph Delaney on vaccines. So let’s talk, first about treatments, then about vaccines. Clinical trials for treatments The first thing I want to say is that designing clinical trials is not just […]

Hey, I think something’s wrong with this graph! Free copy of Regression and Other Stories to the first commenter who comes up with a plausible innocent explanation of this one.

Paul Alper points us to this column by Dana Milbank discussing the above graph from Georgia’s Department of Public Health: Ok, the comb-style bar graph is, as always, a bad idea, as it multiplexes two dimensions (county and time) on a single x-axis. The graph should be a lineplot, with one line per county, and […]

What a difference a month makes (polynomial extrapolation edition)

Someone pointed me to this post from Cosma Shalizi conveniently using R to reproduce the famous graph endorsed by public policy professor and A/Chairman @WhiteHouseCEA. Here’s the original graph that caused all that annoyance: Here’s Cosma’s reproduction in R (retro-Cosma is using base graphics!), fitting a third-degree polynomial on the logarithms of the death counts: […]