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Archive of posts filed under the Public Health category.

Postdoc in precision medicine at Johns Hopkins using Bayesian methods

Aki Nishimura writes: My colleague Scott Zeger and I have a postdoc position for our precision medicine initiative at Johns Hopkins and we are looking for expertise in Bayesian methods, statistical computation, or software development. Expertise in Stan would be a plus!

Rapid prepublication peer review

The following came in the email last week from Gordon Shotwell: You posted about an earlier pilot trial of calcifidiol, so I wanted to send you this larger study. The randomization is a bit funky and if you were interested it would be great to hear what sorts of inferences we can make about this […]

COVID and Vitamin D…and some other things too.

This post is by Phil Price, not Andrew. Way back in November I started writing a post about my Vitamin D experience. My doctor says I need more, in spite of the fact that I spend lots of time outdoors in the sun. I looked into the research and concluded that nobody really knows how […]

“Our underpowered trial provides no indication that X has a positive or negative effect on Y”

It’s rare to see researchers say flat-out that an experimental result leaves them uncertain. There seems to be such a temptation to either declare victory with statistical significance (setting the significance level to 0.1 if necessary to clear the bar) or to claim that weak and noisy results are “suggestive” or, conversely, to declare non-significance […]

Further comments on “Assessing mandatory stay‐at‐home and business closure effects on the spread of COVID‐19”

A few weeks ago we discussed a recent paper from Bendavid et al., “Assessing mandatory stay‐at‐home and business closure effects on the spread of COVID‐19.” Lonni Besançon writes: Together with Gideon Meyerowitz-Katz we found that the study was fairly limited and did not properly highlight its limitations nor did it discuss several conflicting results from […]

How to track covid using hospital data collection and MRP

Len Covello, Yajuan Si, and I write: The current way we track the prevalence of coronavirus infections is deeply flawed. Ideally, health officials would test random samples of citizens in each community in a systematic way. But throughout the pandemic, the United States has lacked the political will or funding to pursue it. Instead, testing […]

How to incorporate new data into our understanding? Sturgis rally example.

A colleague writes: This is a very provocative claim about the Sturgis rally—can you do a stats “fact check”? I’m curious if this has been subjected to statistical scrutiny. I replied that I’m curious why he said this study is provocative: It makes sense that when people get together and connect nodes in the social […]

The “story time” is to lull us in with a randomized controlled experiment and as we fall asleep, feed us less reliable conclusions that come from an embedded observational study.

Kaiser Fung explains. This comes up a lot, and his formulation in the above title is a good way of putting it. He also has this discussion of the AstraZeneca-Oxford vaccine trial results which makes me want to just do a damn Bayesian analysis of it already. I’ll have to find someone with the right […]

One more cartoon like this, and this blog will be obsolete.

This post is by Phil. This SMBC cartoon seems to wrap up about half of the content of this blog.  Of course I’m exaggerating. There will still be room for book reviews and cat photos.

About that claim that “SARS-CoV-2 is not a natural zoonosis but instead is laboratory derived”

A couple people pointed me to this article, “A Bayesian analysis concludes beyond a reasonable doubt that SARS-CoV-2 is not a natural zoonosis but instead is laboratory derived.” It is hard for me to assess this document, as the key issues involve the biology of the virus, and I don’t know anything about genetics. There […]

Mortality data 2015-2020 around the world

Ariel Karlinsky writes: Me and a colleague (Dmitry Kobak) just published (for now just on medRxiv, publication upcoming soon we hope) a large (the largest to date!) dataset of weekly, monthly or quarterly deaths in 79 countries from 2015 to 2021 – Documenting large increases in mortality in most of them, in tandem with COVID […]

Scott Atlas, Team Stanford, and their friends

A recent comment thread revealed the existence of an organization called Panda: “Pandemics ~ Data & Analytics.” Its scientific advisory board includes Scott Atlas, the former U.S. government advisor described on the website as a “world renowned physician.” He’s now at Stanford’s Hoover Institution. Atlas most recently appeared on Fox News to say, “It is […]

Three coronavirus quickies

1. Charles Horton writes: The existing research into ivermectin doesn’t generally strike me as very strong, with much of it showing up in non-peer-reviewed journals, or having other major flaws (i.e., the Hill study presents itself as a meta-analysis but of the 18 trials it collects, it only claims that two are high-quality—then analyzes all […]

What about that new paper estimating the effects of lockdowns etc?

A couple people pointed me to this article, “Assessing Mandatory Stay‐at‐Home and Business Closure Effects on the Spread of COVID‐19,” which reports: The most restrictive non‐pharmaceutical interventions (NPIs) for controlling the spread of COVID‐19 are mandatory stay‐at‐home and business closures. . . . We evaluate the effects on epidemic case growth of more restrictive NPIs […]

Infer well arsenic dynamic from filed kits

(This post is by Yuling, not Andrew) Rajib Mozumder, Benjamin Bostick, Brian Mailloux, Charles Harvey, Andrew, Alexander van Geen, and I arxiv a new paper “Making the most of imprecise measurements: Changing patterns of arsenic concentrations in shallow wells of Bangladesh from laboratory and field data”. Its abstract reads: Millions of people in Bangladesh drink […]

Webinar: Functional uniform priors for dose-response models

This post is by Eric. This Wednesday, at 12 pm ET, Kristian Brock is stopping by to talk to us about functional uniform priors for dose-response models. You can register here. Abstract Dose-response modeling frequently employs non-linear regression. Functional uniform priors are distributions that can be derived for parameters that convey approximate uniformity over the […]

You’re a data scientist at a local hospital and you’ve been asked to present to the physicians on communicating statistical information to patients. What should you say?

Someone who wishes to remain anonymous writes: I just read your post reflecting on crappy talks . . . I’m reaching out because I’m a data scientist at a local hospital in the US and I’ve been asked to present to our physicians about communicating statistical information to patients (e.g., how to interpret the results […]

Routine hospital-based SARS-CoV-2 testing outperforms state-based data in predicting clinical burden.

Len Covello, Yajuan Si, Siquan Wang, and I write: Throughout the COVID-19 pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community […]

“They adjusted for three hundred confounders.”

Alexey Guzey points to this post by Scott Alexander and this research article by Elisabetta Patorno, Robert Glynn, Raisa Levin, Moa Lee, and Krista Huybrechts, and writes: I [Guzey] am extremely skeptical of anything that relies on adjusting for confounders and have no idea what to think about this. My intuition would be that because […]

Megan Higgs (statistician) and Anna Dreber (economist) on how to judge the success of a replication

The discussion started with this comment from Megan Higgs regarding a recent science replication initiative: I [Higgs] was immediately curious about their criteria for declaring a study replicated. In a quick skim of the info in the google form, here it is: In the survey of beliefs, you will be asked for (a) the probability […]

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