Methodology research is an intense process mentally & emotionally - this rarely gets mentioned so thought I would share that it’s something I struggle with personally

It’s all consuming as your brain keeps trying to solve problems & generate solutions

Exciting & exhausting
1/n eg, working on statistical theory, devising formulae, innovating a new idea, realising an idea is futile, devising and undertaking simulations etc

It’s an emotional rollercoaster

You also don’t always know what the final destination is or indeed if you will find a solution
2/n

Here are 10 of the 'best' reviewer comments I have received over the years (all real, sadly!):

1. Richard Riley is quite good as far as he goes

2. The style of presenting the main ideas is so long winded that I wanted to die several times over whilst reading it 3. Prof Riley's response made my concerns much worse

4. The committee concluded that Dr Riley's methods are not truly innovative

5. Equation (8) is a very clever reformation (I didn’t realise he was that clever)”

** Writing an academic text book - a thread **

I've written/edited a couple of textbooks in the last 5 years, & gained experience about the process & what worked well.

So 👇 I share 10 learning points to help others considering their own book project.
#AcademicTwitter 1. Make sure there's a gap in the market
- fundamentally, there must be a need for the book, so make sure you know what is (not) already covered by existing books. Ask yourself, what would your book add new or different? And, do you have enough content to warrant a new book?

** NEW PAPER in @JClinEpi **

"Penalisation and shrinkage methods produced unreliable clinical prediction models especially when sample size was small"

- with @GSCollins @Kym_Snell @LucindaAArcher @Beckylwhittle @glen_martin1 @MatthewSperrin

sciencedirect.com/science/articl…

1/6 Summary points:

• When developing a clinical prediction model, penalisation and shrinkage techniques are recommended to address overfitting
• Some methodology articles suggest penalisation methods are a ‘carte blanche’, and resolve any issues to do with overfitting
2/6

NEW PAPER with @GSCollins & @BenVanCalster

A note on estimating the Cox-Snell R^2 from a reported C-statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome

Open access with R & Stata code onlinelibrary.wiley.com/doi/epdf/10.10… @Wiley_Stats This paper builds on our previous proposals about how to calculate the minimum sample size for developing a multivariable prediction model with a continuous outcome (onlinelibrary.wiley.com/doi/abs/10.100…)or with a binary or time-to-event outcome (onlinelibrary.wiley.com/doi/full/10.10…)

Each week I'm going to recommend an article/book that I've found useful but is generally under-appreciated :

#3 "Undue reliance on I-squared in assessing heterogeneity may mislead" by Gerta Rücker and colleagues
bmcmedresmethodol.biomedcentral.com/articles/10.11… I often use this paper in my peer-reviews, to push back against authors using I-squared to either (i) decide whether to use a random-effects meta-analysis or (ii) quantify the amount of between-study heterogeneity. (Sadly I see these errors a lot)

NEW: Minimum sample size for developing a multivariable prediction model PART II: binary & time‐to‐event outcomes. Big thanks to @GSCollins @f2harrell @Kym_Snell @CarlMoons @DanielleBurke88 @joie_ensor Calculate minimum EPV required based on three criteria onlinelibrary.wiley.com/doi/full/10.10… Our aim: push researchers to develop robust prediction models Thus the sample size should be large enough to minimise overfitting, whilst precisely estimating key parameters (e.g. overall risk). Complements work by @MaartenvSmeden that suggests the 10 EPV rule is, well, dead.