Lightnews — Scholar-powered news

Reposted by Paul Madley-Dowd

Viktor H. Ahlqvist @ahlqvistviktor.bsky.social · 15d

theconversation.com/paracetamol-...

Paracetamol use during pregnancy not linked to autism, our study of 2.5 million children shows

Our research provides strong evidence against the concerning claims made recently by US president Donald Trump.

theconversation.com

1 4

Reposted by Paul Madley-Dowd

Adam Kucharski @adamjkucharski.bsky.social · 26d

When can a 10% improvement be worse than a 5% one? New post on vibe coding and 'volatility tax':

kucharski.substack.com/p/the-hidden...

Vibe coding and 'volatility tax'

The value of predictable improvement

kucharski.substack.com

1 2 11

Paul Madley-Dowd @pmadleydowd.bsky.social · Sep 3

I've just begun my journey into grant writing/internal project applications and it is starting to feel more and more like an exercise in click bait production. Why on earth is it more desirable to be vague and punchy than actual explaining what you want to do using field specific standard language

3

Paul Madley-Dowd @pmadleydowd.bsky.social · Sep 3

@timpmorris.bsky.social @ahlqvistviktor.bsky.social @maartenvsmeden.bsky.social

Paul Madley-Dowd @pmadleydowd.bsky.social · Sep 3

...modelling effects from these simulated patients.

1

Paul Madley-Dowd @pmadleydowd.bsky.social · Sep 3

I can see a (perhaps naïve) similarity to g-computation (I recognise this is just a tool for estimating marginal effects) but even there the analyst is using predictions based on models that used patient data as opposed to using the models to simulate a control patient and then...

1 2

Paul Madley-Dowd @pmadleydowd.bsky.social · Sep 3

Does anyone have any thoughts on digital twins in clinical trials or in-silico trials?

2

Reposted by Paul Madley-Dowd

Margarita Moreno-Betancur @margaritamb.bsky.social · Jul 10

1/ NEW R PACKAGE! For estimating the impact of potential interventions on multiple mediators in countering exposure effects (led by @cttc101.bsky.social)

- Paper👉 tinyurl.com/ye26jsps
- Package👉 tinyurl.com/yuh4kens

Thread shows published examples of how the method can be used! #EpiSky #CausalSky

tinyurl.com

1 12 30

Reposted by Paul Madley-Dowd

Venexia Walker @venexia.bsky.social · Jul 4

🚨 Funded PhD opportunity 🚨 Work with large-scale electronic health record data from #OpenSAFELY to optimise vaccine effectiveness estimation for respiratory viruses.

Apply here 👉 www.findaphd.com/phds/project...

1 3 3

Reposted by Paul Madley-Dowd

Viktor H. Ahlqvist @ahlqvistviktor.bsky.social · May 17

For those of you using sibling analysis, perhaps you'll find this useful 🥳: www.medrxiv.org/content/10.1...

Moving beyond risk ratios in sibling analysis: estimating clinically useful measures from family-based analysis

Objective: Findings from family-based analyses, such as sibling comparisons, are often reported using only odds ratios or hazard ratios. We demonstrate how this can be improved upon by applying the ma...

www.medrxiv.org

1 1 5

Paul Madley-Dowd @pmadleydowd.bsky.social · May 14

Just adding "The meta-analyst decides that the accumulated evidence is in fact a pileup." as an additional favourite

1 2

Paul Madley-Dowd @pmadleydowd.bsky.social · Mar 27

Congratulations Viktor!!

1 1

Reposted by Paul Madley-Dowd

Viktor H. Ahlqvist @ahlqvistviktor.bsky.social · Mar 27

Fantastic news! This is entirely attributable to all my colleagues—everyone from mentors to students—who have made this possible🎉🎉

news.ki.se/safer-medica...

Safer medications for pregnant women and children

Viktor H. Ahlqvist, postdoc at the Institute of Environmental Medicine (IMM), receives 3,000,000 SEK in a Postdoctoral Grant from the Swedish Society for Medical Research (SSMF) for the project “Advan...

news.ki.se

3 1 8

Reposted by Paul Madley-Dowd

Lorenzo Fabbri @epilorenzofabbri.com · Mar 24

It’s my understanding that with the parametric g-formula you use the outcome model to predict the outcome for each subject, independently of whether they are censored. And you take its mean considering ALL N subjects. If the pot. outcome has some NA, I’d still sum and divide by N.
Stupid question:

1 1

Paul Madley-Dowd @pmadleydowd.bsky.social · Mar 19

I'm even less convinced by these certificates now

bsky.app/profile/bkle...

Brian K. Lee @bklee.bsky.social · Mar 19

So I got this email from BJOG saying a paper we wrote was among the top 10 most cited articles of the year....the paper has 1 citation as of today 😂

1 2

Reposted by Paul Madley-Dowd

Suzie Cro @suziecro.bsky.social · Mar 18

New publication led by @proflouisemarston.bsky.social using multiple imputation to target a hypothetical estimand in a pandemic restriction-free world for a trial in
schizophrenia - demonstrating the potential impact of the pandemic on the trial results

Journal of Clinical Epidemiology @jclinepi.bsky.social · Mar 13

Exploring the effect of COVID-19 restrictions on the Social Functioning Scale in a clinical trial of Antipsychotic Reduction: using multiple imputation to target a hypothetical estimand - Journal of Clinical Epidemiology www.jclinepi.com/article/S089...

Exploring the effect of COVID-19 restrictions on the Social Functioning Scale in a clinical trial of Antipsychotic Reduction: using multiple imputation to target a hypothetical estimand

Many trials are affected by unforeseen events after recruitment has commenced. The aim of this study is to explore a hypothetical strategy for dealing with an intercurrent event that occurred during t...

www.jclinepi.com

1 6 8

Paul Madley-Dowd @pmadleydowd.bsky.social · Feb 19

There's already been a very interesting preprint commentary on our paper by Maya Mathur and Ilya Shpitser which I highly recommend people take a look at :

osf.io/preprints/os...

OSF

osf.io

Paul Madley-Dowd @pmadleydowd.bsky.social · Feb 19

Conclusions:
- Use auxiliary variables that are completely observed, or have smaller amounts of missing data
- Explore the missing data mechanisms of incomplete auxiliary variables
- Aim to use auxiliary variables that are independent of their own missingness.

1

Paul Madley-Dowd @pmadleydowd.bsky.social · Feb 19

"Bias was larger when the auxiliary had a stronger correlation with the outcome...In terms of absolute bias in the MI estimate, this equates to around ... 17% of the true effect size." We would tend to treat such an auxiliary as preferable, but we need to show caution when it has missing data in it

1

Paul Madley-Dowd @pmadleydowd.bsky.social · Feb 19

The most striking finding to me was that when there was no bias in CRA (and we are using MI to reduce SEs only), including an auxiliary variable with an open path to its own missing data can introduce substantial quantities of bias.

Subsection from Figure 2 of the paper. The image shows a plot with relative bias on the Y axis, and the proportion of missing data in the auxiliary variable on the Z axis. Four coloured lines are on the plot representing different correlations between the outcome and the auxiliary variable. This plot, plot H, displays results for an example where the missingness mechanism for the outcome leads to an unbiased estimate of an exposure outcome

1

Paul Madley-Dowd @pmadleydowd.bsky.social · Feb 19

We looked at different missing data mechanisms for both an outcome and an auxiliary variable.

Where the outcome missingness mechanism led to a biased complete records analysis, increasing proportions of missing data reduced the ability of auxiliary variables to remove bias.

1

Paul Madley-Dowd @pmadleydowd.bsky.social · Feb 19

But what happens when those auxiliary variables have missing data in them?

We didn't know what consequence including incomplete auxiliary variables has on bias of exposure-outcome estimates made using regression models - so we did some simulating.

1

Paul Madley-Dowd @pmadleydowd.bsky.social · Feb 19

Final version published so time to talk about it: doi.org/10.1093/aje/...

When using multiple imputation to account for missing data we often use auxiliary variables (variables included in the imputation model but not the analysis model) to 1) reduce bias and 2) improve statistical efficiency.

Analyses using multiple imputation need to consider missing data in auxiliary variables

Abstract. Auxiliary variables are used in multiple imputation (MI) to reduce bias and increase efficiency. These variables may often themselves be incomple

doi.org

1 1 5

Paul Madley-Dowd @pmadleydowd.bsky.social · Nov 21

Work by @ahlqvistviktor.bsky.social @draipsych.bsky.social @karolinskainst.bsky.social @aarhusuni.bsky.social
@neilmdavies.bsky.social @danielberglind.bsky.social ... and many more... @natureportfolio.bsky.social

1

Paul Madley-Dowd @pmadleydowd.bsky.social · Nov 21

The phrase 200% higher looks a lot more alarming than twice the risk of 0.9%.

This is nothing new in the area of risk communication - but today it has annoyed me

1 2