the ultimate reviewer #2 bingo card
key citations 👇
unclear analysis aims…

evidence of absence fallacy…

data dredging…

noisy data fallacy…
only apparent predictive performance…

point estimate is the effect


multivariate vs multivariable models…

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More from @MaartenvSmeden

3 Aug
The BMJ just published an editorial about living systematic reviews worth a read, which is new territory for just about everyone…

ICYI, I have a few thoughts to share
We were fortunate to have produced @bmj_latest first living review…
The aim of our review is (and always was) to give an overview and appraisal of currently available diagnosis and prognosis models related to COVID-19

But this is a fast moving field: from 31 models reviewed in April to 145 models reviewed in our 2nd update published in July
Read 14 tweets
11 Jul
Used to get annoyed by stats consult clients who insisted they needed machine learning for their very large dataset (N of 100s or few 1000s). Now I tell them logistic regression *is* machine learning and everything is great again
And since machine learning is a sub field of AI, logistic regression is also AI. I should have understood this sooner
Logistic regression as statistical model
- prepare data
- estimate model
- evaluate performance
- report

Logistic regression as machine learning
- prepare data
- estimate model
- evaluate performance
- report
Read 4 tweets
9 Jun
Was asked for personal favorite resources for improving methods and statistics skills. I promised to make it a thread, so here it is

I work in medical research, so that is going to be my focus here too. But I’d like to think the resources are relevant to a wider audience

This list should not be taken as a guide to become a statistician, nor is it a must-read list for all academics (obviously)

My personal view is that medical research would benefit from involving trained statisticians earlier and more frequently; not from everyone trying to become one

Here are some good arguments by @statsepi:…

And some more:…

Read 20 tweets
19 May
The definitive guide to COVID-19 prognosis modeling success

1) Do not explain where the data come from (country) or when (study dates) they were obtained. Do not specify inclusion or exclusion criteria
2) Do not define a target group. Talk generically about COVID-19 patients, do not define how they were recruited
3) Do not provide a table with patient characteristics. In particular, do not mention use of medication or co-morbidities
Read 18 tweets
13 Apr
Let's talk about the "risk factors" for COVID-19 for a moment

We talk about risk factors all the time. Not just in the medical scientific literature: you will find risk factors being discussed in the popular media and on social media too

Exhibit A:…

The term "risk factor" is popular in medical research. It has been used in literature since at least the 1950s

BUT definitions for what a risk factor really is or should be varies. As this article argues quite convincingly…

Read 15 tweets
7 Apr
Our NEW systematic review article about diagnosis and prognosis models related to COVID-19 is out now in @bmj_latest…

📢 This review will be regularly updated in the coming months. Watch this space📢

The evidence base for COVID-19 related diagnosis and prognosis models is weak and reporting quality is generally poor: we can and should do better

We will continue our critical appraisals of new models when they appear in the coming months

What we did
Systematically reviewed and critically appraised articles (including preprints) of COVID-19 related diagnosis and prognosis models developed for individual level predictions

Models to forecast the spread of the COVID-19 infection are not part of this review

Read 12 tweets

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