on Astra and clots

A tribune this morning in The Guardian by David Spiegelhalter on having no evidence that the Oxford/AstraZeneca vaccine causes blood clots.

“It’s a common human tendency to attribute a causal effect between different events, even when there isn’t one present: we wash the car and the next day a bird relieves itself all over the bonnet. Typical.”

David sets the 30 throboembolic events among the 5 million people vaccinated with AstraZeneca in perpective of the expected 100 deep vein thromboses a week within such a population. Which coincides with the UK’s Medicines and Healthcare Products Regulatory Agency statement that the blood clots are in par with the expected numbers in the vaccinated population. (The part of the tribune about the yellow card reports, based on 10 million vaccinated people, reiterates the remark but may prove confusing to some!) As for hoping for a rational approach to the issue,  … we would need a different type of vaccine, far from being available! As demonstrated by the decision to temporarily stop vaccinating with this vaccine, causing sure additional deaths in the coming weeks.

“Will we ever be able to resist the urge to find causal relationships between different events? One way of doing this would be promoting the scientific method and ensuring everyone understands this basic principle. Testing a hypothesis helps us see which hunches or assumptions are correct and which aren’t. In this way, randomised trials have proved the effectiveness of some Covid treatments and saved vast numbers of lives, while also showing us that some overblown claims about treatments for Covid-19, such as hydroxychloroquine and convalescent plasma, were incorrect.”

Bayesian basics in Le Monde

[The Art of] Regression and other stories

CoI: Andrew sent me this new book [scheduled for 23 July on amazon] of his with Jennifer Hill and Aki Vehtari. Which I read in my garden over a few sunny morns. And as Andrew and Aki are good friends on mine, this review is definitely subjective and biased! Hence to take with a spoonful of salt.

The “other stories’ in the title is a very nice touch. And a clever idea. As the construction of regression models comes as a story to tell, from gathering and checking the data, to choosing the model specifications, to analysing the output and setting the safety lines on its interpretation and usages. I added “The Art of” in my own title as the exercise sounds very much like an art and very little like a technical or even less mathematical practice. Even though the call to the resident stat_glm R function is ubiquitous.

The style itself is very story-like, very far from a mathematical statistics book as, e.g., C.R. Rao’s Linear Statistical Inference and Its Applications. Or his earlier Linear Models which I got while drafted in the Navy. While this makes the “Stories” part most relevant, I also wonder how I could teach from this book to my own undergrad students without acquiring first (myself) the massive expertise represented by the opinions and advice on what is correct and what is not in constructing and analysing linear and generalised linear models. In the sense that I would find justifying or explaining opinionated sentences an amathematical challenge. On the other hand, it would make for a great remote course material, leading the students through the many chapters and letting them experiment with the code provided therein, creating new datasets and checking modelling assumptions. The debate between Bayesian and likelihood solutions is quite muted, with a recommendation for weakly informative priors superseded by the call for exploring the impact of one’s assumption. (Although the horseshoe prior makes an appearance, p.209!) The chapter on math and probability is somewhat superfluous as I hardly fathom a reader entering this book without a certain amount of math and stats background. (While the book warns about over-trusting bootstrap outcomes, I find the description in the Simulation chapter a wee bit too vague.) The final chapters about causal inference are quite impressive in their coverage but clearly require a significant amount of investment from the reader to truly ingest these 110 pages.

“One thing that can be confusing in statistics is that similar analyses can be performed in different ways.” (p.121)

Unsurprisingly, the authors warn the reader about simplistic and unquestioning usages of linear models and software, with a particularly strong warning about significance. (Remember Abandon Statistical Significance?!) And keep (rightly) arguing about the importance of fake data comparisons (although this can be overly confident at times). Great Chapter 11 on assumptions, diagnostics and model evaluation. And terrific Appendix B on 10 pieces of advice for improving one’s regression model. Although there are two or three pages on the topic, at the very end, I would have also appreciated a more balanced and constructive coverage of machine learning as it remains a form of regression, which can be evaluated by simulation of fake data and assessed by X validation, hence quite within the range of the book.

The document reads quite well, even pleasantly once one is over the shock at the limited amount of math formulas!, my only grumble being a terrible handwritten graph for building copters(Figure 1.9) and the numerous and sometimes gigantic square root symbols throughout the book. At a more meaningful level, it may feel as somewhat US centric, at least given the large fraction of examples dedicated to US elections. (Even though restating the precise predictions made by decent models on the eve of the 2016 election is worthwhile.) The Oscar for the best section title goes to “Cockroaches and the zero-inflated negative binomial model” (p.248)! But overall this is a very modern, stats centred, engaging and careful book on the most common tool of statistical modelling! More stories to come maybe?!

the paper where you are a node

Sophie Donnet pointed out to me this arXived paper by Tianxi Li, Elizaveta Levina, and Ji Zhu, on a network resampling strategy for X validation, where I appear as a datapoint rather than as a [direct] citation! Which reminded me of the “where you are the hero” gamebooks with which my kids briefly played, before computer games took over. The model selection method is illustrated on a dataset made of X citations [reduced to 706 authors]  in all papers published between 2003 and 2012 in the Annals of Statistics, Biometrika, JASA, and JRSS Series B. With the outcome being the determination of a number of communities, 20, which the authors labelled as they wanted, based on 10 authors with the largest number of citations in the category. As it happens, I appear in the list, within the “mixed (causality + theory + Bayesian)” category (!), along with Jamie Robbins, Paul Fearnhead, Gilles Blanchard, Zhiqiang Tan, Stijn Vansteelandt, Nancy Reid, Jae Kwang Kim, Tyler VanderWeele, and Scott Sisson, which is somewhat mind-boggling in that I am pretty sure I never quoted six of these authors [although I find it hilarious that Jamie appears in the category, given that we almost got into a car crash together, at one of the Valencià meetings!].

statistics in Nature [a tale of the two Steves]

In the 29 November issue of Nature, Stephen Senn (formerly at Glasgow) wrote an article about the pitfalls of personalized medicine, for the statistics behind the reasoning are flawed.

“What I take issue with is the de facto assumption that the differential response to a drug is consistent for each individual, predictable and based on some stable property, such as a yet-to-be-discovered genetic variant.”S. Senn

One (striking) reason being that the studies rest on a sort of low-level determinism that does not account for many sources of variability. Over-confidence in causality results. Stephen argues that improvement lies in insisting on repeated experiments on the same subjects (with an increased challenge in modelling since this requires longitudinal models with dependent observations). And to “drop the use of dichotomies”, favouring instead continuous modeling of measurements.

And in the 6 December issue, Steven Goodman calls (in the World view tribune) for probability statements to be attached as confidence indices to scientific claims. That he takes great pain to distinguish from p-values and links with Bayesian analysis. (Bayesian analysis that Stephen regularly objects to.) While I applaud the call, I am quite pessimistic about the follow-up it will generate, the primary reply being that posterior probabilities can be manipulated as well as p-values. And that Bayesian probabilities are not “real” probabilities (dixit Don Fraser or Deborah Mayo).