Skip to content
Archive of posts filed under the Public Health category.

New research in tuberculosis mapping and control

Mapping and control. Or, as we would say, descriptive and causal inference. Jon Zelner informs os about two ongoing research projects: 1. TB Hotspot Mapping: Over the summer, I [Zelner] put together a really simple R package to do non-parametric disease mapping using the distance-based mapping approach developed by Caroline Jeffery and Al Ozonoff at […]

How is ethics like logistic regression?

Ethics decisions, like statistical inferences, are informative only if they’re not too easy or too hard. For the full story, read the whole thing.

Bayesian models, causal inference, and time-varying exposures

Mollie Wood writes: I am a doctoral student in clinical and population health research. My dissertation research is on prenatal medication exposure and neurodevelopmental outcomes in children, and I’ve encountered a difficult problem that I hope you might be able to advise me on. I am working on a problem in which my main exposure […]

“The Saturated Fat Studies: Set Up to Fail”

Russ Lyons points me to this recent magazine article by Martijn Katan and a research article, “Diet and Serum Cholesterol: Do zero correlations negate the relationship?” by David Jacobs, Joseph Anderson, and Henry Blackburn, and this video by Michael Greger. This is interesting stuff, especially as the ultimate truth is still very unknown. It’s good […]

“Academics should be made accountable for exaggerations in press releases about their own work”

Fernando Martel Garcia points me to this news article by Ben Goldacre: For anyone with medical training, mainstream media coverage of science can be an uncomfortable read. It is common to find correlational findings misrepresented as denoting causation, for example, or findings in animal studies confidently exaggerated to make claims about treatment for humans. But […]

Statistical Significance – Significant Problem?

John Carlin, who’s collaborated on some of my recent work on Type S and Type M errors, prepared this presentation for a clinical audience. It might be of interest to some of you. The ideas and some of the examples should be familiar to regular readers of this blog, but it could be useful to […]

Bayesian survival analysis with horseshoe priors—in Stan!

Tomi Peltola, Aki Havulinna, Veikko Salomaa, and Aki Vehtari write: This paper describes an application of Bayesian linear survival regression . . . We compare the Gaussian, Laplace and horseshoe shrinkage priors, and find that the last has the best predictive performance and shrinks strong predictors less than the others. . . . And here’s […]

How a clever analysis of health survey data became transformed into bogus feel-good medical advice

Jonathan Falk sends a message with the heading, “Garden of forking paths, p value abuse, questionable causality, you name it,” this link to an article in JAMA Internal Medicine, and the following remarks: Unfortunately, I can only see the first page of this article, but it seems to contain all the usual suspects. (a) Forking […]

“Epidemiology and Biostatistics: competitive or complementary?”

Mohammad Mansournia writes: I have a 20 minute lecture on “Epidemiology and Biostatistics: competitive or complementary?” at Tehran University of Medical Sciences in the next month. I should mention the difference between an epidemiologist and a biostatistician and their competitive or complementary roles in public health. I am wondering if you have any thoughts on […]

A New Year puzzle from Macartan Humphreys

Macartan writes: There is a lot of worry about publication and analysis bias in social science research. It seems results are much more likely to be published if they are statistically significant than if not which can lead to very misleading inferences. There is some hope that this problem can be partly addressed through analytic […]