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 […]

**Bayesian Statistics**category.

## “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 […]

## Flaxman et al. respond to criticisms of their estimates of effects of anti-coronavirus policies

As youall know, as the coronavirus has taken its path through the world, epidemiologists and social scientists have tracked rates of exposure and mortality, studied the statistical properties of the transmission of the virus, and estimated effects of behaviors and policies that have been tried to limit the spread of the disease. All this is […]

## How many infectious people are likely to show up at an event?

Stephen Kissler and Yonatan Grad launched a Shiny app, Effective SARS-CoV-2 test sensitivity, to help you answer the question, How many infectious people are likely to show up to an event, given a screening test administered n days prior to the event? Here’s a screenshot. The app is based on some modeling they did with […]

## The likelihood principle in model check and model evaluation

(This post is by Yuling) The likelihood principle is often phrased as an axiom in Bayesian statistics. It applies when we are (only) interested in estimating an unknown parameter $latex \theta$, and there are two data generating experiments both involving $latex \theta$, each having observable outcomes $latex y_1$ and $latex y_2$ and likelihoods $latex p_1(y_1 […]

## “Inferring the effectiveness of government interventions against COVID-19”

John Salvatier points us to this article by Jan Brauner et al. that states: We gathered chronological data on the implementation of NPIs [non-pharmaceutical interventions, i.e. policy or behavioral interventions] for several European, and other, countries between January and the end of May 2020. We estimate the effectiveness of NPIs, ranging from limiting gathering sizes, […]

## Literally a textbook problem: if you get a positive COVID test, how likely is it that it’s a false positive?

This post is by Phil Price, not Andrew. This will be obvious to most readers of this blog, who have seen this before and probably thought about it within the past few months, but the blog gets lots of readers and this might be new to some of you. A friend of mine just tested […]

## Discussion of uncertainties in the coronavirus mask study leads us to think about some issues . . .

1. Communicating of uncertainty A member of the C19 Discussion List, which is a group of frontline doctors fighting Covid-19, asked me what I thought of this opinion article, “Covid-19: controversial trial may actually show that masks protect the wearer,” published last month by James Brophy in the British Medical Journal. Brophy writes: Paradoxically, the […]

## The Shrinkage Trilogy: How to be Bayesian when analyzing simple experiments

There are lots of examples of Bayesian inference for hierarchical models or in other complicated situations with lots of parameters or with clear prior information. But what about the very common situation of simple experiments, where you have an estimate and standard error but no clear prior distribution? That comes up a lot! In such […]

## Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond

Charles Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal write: Gaussian latent variable models are a key class of Bayesian hierarchical models with applications in many fields. Performing Bayesian inference on such models can be challenging as Markov chain Monte Carlo algorithms struggle with the geometry of the resulting posterior distribution and can be prohibitively slow. […]

## “We’ve got to look at the analyses, the real granular data. It’s always tough when you’re looking at a press release to figure out what’s going on.”

Chris Arderne writes: Surprised to see you hadn’t yet discussed the Oxford/AstraZeneca 60%/90% story on the blog. They accidentally changed the dose for some patients without an hypothesis, saw that it worked out better and are now (sort of) claiming 90% as a result… Sounds like your kind of investigation? I hadn’t heard about this […]

## 2 PhD student positions on Bayesian workflow! With Paul Bürkner!

Paul Bürkner writes: The newly established work group for Bayesian Statistics of Dr. Paul-Christian Bürkner at the Cluster of Excellence SimTech, University of Stuttgart (Germany), is looking for 2 PhD students to work on Bayesian workflow and Stan-related topics. The positions are fully funded for at least 3 years and people with a Master’s degree […]

## Mister P for the 2020 presidential election in Belarus

An anonymous group of authors writes: Political situation Belarus is often called the “last dictatorship” in Europe. Rightly so, Aliaskandr Lukashenka has served as the country’s president since 1994. In the 26 years of his rule, Lukashenka has consolidated and extended his power, which is today absolute. Rigging referendums has been an effective means of […]

## Nonparametric Bayes webinar

This post is by Eric. A few months ago we started running monthly webinars focusing on Bayes and uncertainty. Next week, we will be hosting Arman Oganisian, a 5th-year biostatistics PhD candidate at the University of Pennsylvania and Associate Fellow at the Leonard Davis Institute for Health Economics. His research focuses on developing Bayesian nonparametric […]

## You don’t need a retina specialist to know which way the wind blows

Jayakrishna Ambati writes: I am a retina specialist and vision scientist at the University of Virginia. I am writing to you with a question on Bayesian statistics. I am performing a meta analysis of 5 clinical studies. In addition to a random effects meta analysis model, I am running Bayesian meta analysis models using half […]

## How to describe Pfizer’s beta(0.7, 1) prior on vaccine effect?

Now it’s time for some statistical semantics. Specifically, how do we describe the prior that Pfizer is using for their COVID-19 study? Here’s a link to the report. A PHASE 1/2/3, PLACEBO-CONTROLLED, RANDOMIZED, OBSERVER-BLIND, DOSE-FINDING STUDY TO EVALUATE THE SAFETY, TOLERABILITY, IMMUNOGENICITY, AND EFFICACY OF SARS-COV-2 RNA VACCINE CANDIDATES AGAINST COVID-19 IN HEALTHY INDIVIDUALS Way […]

## Bayesian Workflow

Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Paul-Christian Bürkner, Lauren Kennedy, Jonah Gabry, Martin Modrák, and I write: The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilistic programming languages make it easier to specify and […]

## What would would mean to really take seriously the idea that our forecast probabilities were too far from 50%?

Here’s something I’ve been chewing on that I’m still working through. Suppose our forecast in a certain state is that candidate X will win 0.52 of the two-party vote, with a forecast standard deviation of 0.02. Suppose also that the forecast has a normal distribution. (We’ve talked about the possible advantages of long-tailed forecasts, but […]

## Here’s why rot13 text looks so cool.

To avoid spoilers, I posted some text in rot13: V yvxrq gung ovg arne gur ortvaavat jurer Qnavry Penvt gnyxrq nobhg tbvat gb gur raq bs gur envaobj jurer gurer vf gehgu, naq gura jnvgvat sbe gur riragf bs gur fgbel gb trg gurer. Guvf frrzf gb zr gb qrfpevor n ybg bs jung erfrnepu […]

## Don’t kid yourself. The polls messed up—and that would be the case even if we’d forecasted Biden losing Florida and only barely winning the electoral college

To continue our post-voting, pre-vote-counting assessment (see also here and here), I want to separate two issues which can get conflated: