Greg Atkinson
@gregatki.bsky.social
410 followers 680 following 88 posts
Honorary Visiting Professor at LJMU. Exercise & Nutrition Science, Circadian Rhythms and Jet lag, Research Methods & Statistics, Bike Racing, BBC6-played singer-songwriter. https://scholar.google.co.uk/citations?user=8Gog69EAAAAJ&hl=en
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Reposted by Greg Atkinson
mrjamesob.bsky.social
Happiness, humour, truth and, most crucially, courage are the qualities needed to repel the racist tide.
Because its architects offer whining, victimhood, lies and, most crucially, cowardice.
liamthorp.bsky.social
Very good stuff from Sadiq Khan at the Labour Irish society event. He said there are two reasons he loves coming to Labour Conference.

“One is the reception at the Labour Party Irish Society reception.

“The second is to get away from all the Sharia Law in London”
Reposted by Greg Atkinson
jamessteeleii.bsky.social
#SportScience, especially in elite sports, has an issue that can make effects seem more impressive than they really are... selection bias on the sample (explanation🧵 and a simulation pictured to illustrate below).

1/12
Histogram figure illustrating selection bias in estimating intervention effects. The central blue histogram shows the full population distribution of pre-scores (mean = 100, SD = 10). A green histogram (top left) shows a random sample of 1000 participants with similar mean (≈100) and SD (≈10), producing a raw intervention effect of ~8.7 and standardised mean difference (SMD) of ~0.86. A red histogram (top right) shows a conditionally sampled group (n = 1000, pre-score ≥ 110), with higher mean (≈115) and smaller SD (≈4.4). This sample yields a similar raw effect (~9) but an inflated SMD (~2.0) compared to the population. A corrected SMD brings it back closer to the true effect (~0.9). The figure illustrates how restricting samples to higher-performing individuals (e.g., “elite” athletes) compresses variance and artificially inflates standardised effect sizes, despite unbiased raw effects.
Reposted by Greg Atkinson
who.int
WHO @who.int · 15d
WHO statement on autism-related issues

The World Health Organization (WHO) emphasizes that there is currently no conclusive scientific evidence confirming a possible link between #autism and use of acetaminophen (also known as paracetamol) during pregnancy

Full statement bit.ly/47YsgwI
WHO statement on autism-related issues

Follow @WHO for the latest updates
gregatki.bsky.social
Good luck media fact checkers. You have my sympathy.
Reposted by Greg Atkinson
hk-ijsnem.bsky.social
🚨LEAD FEATURE ARTICLE🚨 November 2025✨

We are very excited to announce our lead feature article for the November 2025 by Lolli et al titled ‘Understanding Treatment Response Heterogeneity Using Crossover Randomized Controlled Trials: A Primer for Exercise and Nutrition Scientists’.
Reposted by Greg Atkinson
statsepi.bsky.social
Restating a prediction I made on twitter that university rankings will be a thing of the past 10 years from now, and we'll look back on the university heads that first led us away from them with a great respect and appreciation....1/
Reposted by Greg Atkinson
sfrost.bsky.social
Almost 900 fewer people have been injured on Welsh roads since the default speed limit was lowered from 30 to 20mph two years ago

Casualties on 20 to 30mph roads between July and September 2024 were the lowest for the three month-period since records began in 1979

👏 Evidence-led policy
Nearly 900 fewer people injured since 20mph introduction in Wales
Figures show a 25% reduction in the number of injuries on Wales' roads in the past 18 months.
www.bbc.co.uk
Reposted by Greg Atkinson
lucymerrell.bsky.social
So happy to see this one out! Thank you so much for your help @gregatki.bsky.social, your insight and analysis of the data were invaluable (as always!). Look forward to working with you again.
Reposted by Greg Atkinson
Reposted by Greg Atkinson
marionkcampbell.bsky.social
It is first important to stress that pilot and feasibility studies are *not* about detecting intervention effects. They are about exploring key uncertainties ahead of the main trial 2/6
Reposted by Greg Atkinson
marionkcampbell.bsky.social
I seem to have been discussing pilot and feasibility studies a lot this week. One question often asked is how big should my pilot/feasibility study be? 1/6
#MethodologyMonday #124
Reposted by Greg Atkinson
hk-ijsnem.bsky.social
✨ NOVEMBER 2025 ISSUE LINE-UP✨

How are we already on the last issue of 2025?! This year has flown by but lucky for you, the IJSNEM content keeps on delivering. Stay tuned for papers to feature 🔔
Reposted by Greg Atkinson
Reposted by Greg Atkinson
statsepi.bsky.social
One of the most common misunderstandings about the use and value of placebos in clinical RCTs, often made by both methodological experts and experienced trialists:

(from @stephensenn.bsky.social in academic.oup.com/jrsssa/advan...)
A further problem is illustrated by the authors equating no treatment with placebo. For example, describing their second assumption, they state, ‘Implicit in this notation is that there is a single version of ‘no treatment’ that is consistently defined across all subjects in the RCT and external controls’ (p4). However, most clinical trials are add-on trials (Senn, 2002), even if not specifically identified as such. In placebo-controlled trials, one starts with standard of care and then subjects are either allocated to receive in addition the active treatment or a placebo to it. The statistical analysis plan for SUNFISH (F Hoffman-La-Roche Ltd, 2020) has a protocol summary as Appendix 1, which has this to say, ‘In addition to the study drug treatment, patients may continue to receive concomitant drug medication…’ (p186). This is very standard for clinical trials. It highlights that a key assumption in borrowing control data in this way is that there has been no evolution in the standard of care in the period since the trial.
Reposted by Greg Atkinson
sashagusevposts.bsky.social
I wrote about gene-gene interactions (epistasis) and the implications for heritability, trait definitions, natural selection, and therapeutic interventions. Biology is clearly full of causal interactions, so why don't we see them in the data? A 🧵:
Beneath the surface of the sum
When genetic interactions matter and when they don't
open.substack.com
gregatki.bsky.social
Thanks for taking time to read the paper. We've been very worried about its length!
1) yes - general agreement although no formal comparison of underlying maths.
2) Our main aim was to cover more robust ways of detecting HTE than typically used. Shrinkage is prob. most useful for clinical relevance.
gregatki.bsky.social
It was a rewarding labour of love to work on this paper with a fantastic team of authors, “Understanding Treatment Response Heterogeneity Using Crossover Randomized Controlled Trials: A Primer for Exercise and Nutrition Scientists”. @hk-ijsnem.bsky.social journals.humankinetics.com/view/journal...
journals.humankinetics.com
gregatki.bsky.social
6. Consideration 1 renders multiplicity likely. Consideration 2 renders it unlikely. IMHO, it’s good to explore the presence of additive/multiplicative effects rather than blanket assumption of the latter & use of %. Figure 2 and related text here is of interest: www-users.york.ac.uk/~mb55/msc/tr...
Cross-over trials
www-users.york.ac.uk
gregatki.bsky.social
5/6. And 2, a possibly competing (to 1 above) expectation that treatment effects would be least for the athletes who are already world-class. Assuming multiplicity (and using % for effect size) naturally assumes that these superb athletes show the biggest (not smallest) absolute effects.