Discover and read the best of Twitter Threads about #epitwitter

Most recents (24)

Quick thread on the recent Dutch KOMPAS trial in @JAMAInternalMed pubmed.ncbi.nlm.nih.gov/32065601-effec… 1/
@JAMAInternalMed Thanks to @brzoskos for tagging me when this came out last week
TL;DR version: doing nothing is fine when you are doing CT scans in peeps with GFR > 30
Isn’t this similar to AMACING? Yes and No, See thread: 2/
KOMPAS was a non-inferiority RCT, funded by the Achmea Healthcare Foundation
Compared 250 mL of 1.4% sodium bicarbonate administered in a 1-hour infusion before CT vs doing *nothing* 3/
Read 32 tweets
Okay, since @KevinMKruse now seems to have given his imprimatur to this piece in the @washingtonpost today about #nCoV2019 & past plagues, it seems time for a mini-thread about #medhist & hot takes. #epitwitter: you might want to listen in on this, as it effects you, too.
@KevinMKruse @washingtonpost The @washingtonpost piece by Eisenberg et al. makes 3 main points: 1) that the #BlackDeath (the #plague pandemic usually dated to the mid-14thC) is the most commonly invoked analogy when people think of epidemics; 2) that not all "plague" epidemics/pandemics were alike; and 3) ..
@KevinMKruse @washingtonpost ... that there's an "outbreak narrative" that "we replay .. as a script with each new outbreak — whether real or fictional." First, some background on what #histmed (History of Medicine) is: it's probably pretty much as you would assume from its name. The field of history that ..
Read 17 tweets
Have you ever calculated the sample size for an #abtest and come up with a sample size that is bigger than you can ever practically get?

Does this mean you shouldn't run the test?

No!

A paper thread for #MarketingAcad #EconTwitter #Measure #epitwitter 1/17
@marketsensi and I thought about this and came to the conclusion that the standard hypothesis test used to analyze A/B tests doesn’t fit well with the marketing problem that we are usually trying to solve.
2/17
Hypothesis tests are used by academics who want to find small effects with high confidence, but in marketing we care about the big effects. Big effects are where the profit comes from!
3/17
Read 17 tweets
Hello #epitwitter and #statstwitter,
There was a recent discussion about the Hosmer-Lemeshow goodness of fit test. I thought it would be interesting to talk to Dr. Lemeshow (who is not on twitter) about his thoughts on the test. This thread has some highlights from our chat. 1/n
In the late 70’s, Hosmer and Lemeshow were struggling with the question “How do you know that the probabilities produced by logistic regression models reflect reality?” This was the motivation for developing the Hosmer-Lemeshow goodness of fit test. 2/n
As with a linear regression, we can’t only look at the estimates from the model to know if the fit is good. For linear regression we can look at a plot (e.g., residual plot) to assess model fit. 3/n
Read 15 tweets
#Epitwitter Fun with Numbers:

It’s possible to put bounds on the case-fatality rate (CFR) for this outbreak with the available data.

A quick #tweetorial on the partial identification of absolute risk bounds!

1/n
According to the quoted tweet we have this data:

31 cases with known outcome
6 of whom died

227 with an unknown outcome

First, the CFR among known data: 6/31 = 19%

2/n
Next, we include unknown outcomes, taking them to their logical extremes (all live or all die).

The lower risk bounds is calculated under the “all live” extreme:

6/(31+227) = 2.3%

Given the available data, this is the lowest possible CFR (i.e., if no new cases arise).

3/n
Read 7 tweets
DAGWOOD abstract submitted to SER! But I also wanted to share it with y'all, because it's the single most surprisingle brain-warpingly difficult thing I think I've ever worked on, and I am super proud of what we've got so far.

A THREAD!

#epitwitter

1/n
DAGs are a way to organize and display causal inference models and their assumptions.

But where in the DAG are those assumptions?

They are hidden in the negative space between nodes, taking the idea of being transparent about our assumptions very literally.

2/n
For example, a causal model typically assumes a sharp (or negligible) causal null for any and all missing confounders. Those confounders are hidden somewhere in the ocean, but we can't see them in a typical DAG.

Hard to think critically about invisible things.

3/n
Read 20 tweets
Out today! Our paper in @NatureMedicine linking rising temperatures with increases in suicides, assaults, drownings and transport accidents. Summary below. @ImperialCollege @Grantham_IC @Columbia @EarthInstitute @Harvard @HarvardChanSPH @nature: nature.com/articles/s4159…
@NatureMedicine @imperialcollege @Grantham_IC @Columbia @earthinstitute @Harvard @HarvardChanSPH @nature @Revkin @francescadomin8 @ColumbiaMSPH @DrMariaNeira @WHO @DiarmidCL @joygmt @ImperialSPH @greenimperial Temperatures that deviate from the long-term local norm affect human health, and are projected to become more frequent as the global climate changes. There are limited data on how such anomalies affect deaths from injuries.
@NatureMedicine @imperialcollege @Grantham_IC @Columbia @earthinstitute @Harvard @HarvardChanSPH @nature @Revkin @francescadomin8 @ColumbiaMSPH @DrMariaNeira @WHO @DiarmidCL @joygmt @ImperialSPH @greenimperial We found that a 2°C anomalously warm year, as envisioned under the Paris Climate Agreement, would be associated with an estimated 2,135 (95% credible interval 1,906–2,368) additional injury deaths (1% increase of total injury deaths)
Read 21 tweets
Starting 2020 off right by getting the benefit of hindsight & reading some classic papers.

#epielliereads
1st up, Take the Con out of Econometrics by Edward Leamer, 1983: all about pros & cons of randomized & observational studies plus answers to many of recurrent #econtwitter, #statstwitter, & #epitwitter arguments & even why #ML can’t do causal inference!
jstor.org/stable/1803924
In Part I randomized experiments vs natural experiments, aka observational studies, Leamer provides a nice summary of some of the problems that can arise in RCTs including that randomization guarantees validity *on average* but that chance imbalance can make any given RCT biased
Read 13 tweets
I promised @LSadinski some thoughts about working with co-authors, esp. as a early career investigator. I hope the following thread provides some useful insights. #epitwitter 1/11
Working with co-authors is a bit like Goldilocks & the 3 Bears. You are Goldilocks. Your co-authors are the 3 bears: either too little, too much, or just right. Your job is to help them all to be just right. 2/11
Your goal is to get the feedback you need, when you need it, in the right quantity, and with the right guidance. That can be a challenge! 3/11
Read 11 tweets
Okay #epitwitter and #genepitwitter, let’s talk about how statistical and biological gene-environment interactions relate to each other (or not). \thread (part 1)
TL;DR 1: the distribution of a trait conditional on genotype and exposure at the population level (whether there is a statistical interaction or not) is consistent with 1,000s of possible biological models.
TL;DR 2: conversely, knowing that a gene product and an exposure or exposure byproduct physically interact at the molecular or cellular level need not say anything about what’s happening at the population level.
Read 27 tweets
I've seen so many people reference/tweet @_MiguelHernan's seminal paper The Hazard of Hazard Ratios and, to my shame, only just got to reading it.
1/
ncbi.nlm.nih.gov/pmc/articles/P…
Aside: twitter has felt a bit less informative and more negative recently, and I miss people's fun threads/tweetorials so here's a thread.
2/
When people have mentioned this article, I feel like the message has been ‘the average HR has an inbuilt selection bias and cannot be interpreted causally’ – which it doesn’t claim at all!
3/
Read 13 tweets
Good morning, #epitwitter!
I’m a co-author on @JessGeraldYoung’s new paper out now in Trials & I want to tell you all about it!

Do you use longitudinal data? Are your measurements often enough? Do you know what “often enough” is?

Time for a #tweetorial!
trialsjournal.biomedcentral.com/articles/10.11…
If exposure happens once, then we only need to worry about confounders once too.

But if exposure can happen over time, so can confounding! & if exposure happens every day, so can confounding😰

But we usually don’t have data everyday. Is that bad? Classic epi answer: it depends!
How often do we need to measure our exposure and confounders to be able to adequately adjust for confounding?

We did a simulation study to find out!
Read 13 tweets
So excited for @sherrirose long-awaited workshop on computational health economics & outcomes. @UCSF_Epibiostat #epitwitter
She leads by calling for value of interdisciplinary research. Need both strong theory & practical/relevant for practice. Sometimes theoretical ideal and practicability are in conflict. Callout for articles on methods grounded in real problems for journal @biostatistics.
@biostatistics Computational health economics (#econtwitter): how can we affect policy?
Data first, methods second. Usefulness of electronic health database is a new resource, but usefulness for research really varies (fancy stats doesn't solve major data problems)
Read 28 tweets
THREAD: How do you plan for BIG projects? I just planned out my writing schedule for my dissertation proposal and thought I'd share.

#AcademicTwitter #PhDTwitter #EpiTwitter
Step 1: What's your weekly schedule look like in terms of PRODUCTIVITY BLOCKS? (shoutout to @CharlieGilkey for introducing me to these - check out his free block planner!) 1/n
Bailey, what's with all the shade? Light gray = potential time off (hello weekend 😎) or may not always be able to write (hello walk-in stats consultant job! 🤓) Dark gray = when I can't write because of journal club, peer led, etc. but those don't occur weekly. Or gym time! 2/n
Read 10 tweets
JOB ALERT: if you are an aspiring trial statistician (or an experienced trial statistician seeking a change of scenery) that would consider a move to Pittsburgh, feel free to DM me for details.
Background in stats, biostats, epi, or any related field, but clinical trial experience (student experience counts) is highly preferred. The group doing the hiring is specifically looking for someone with interest in trials.
Tossing this out to #statstwitter and #epitwitter for a signal boost.
Read 3 tweets
THE OBESITY PARADOX IS NOT A PARADOX

A tweetstorm by a frustrated epidemiologist

I was disappointed to see an "obesity paradox" article in @AmJEpi

Instead writing a letter to the editor, I decided that Twitter is a better way to reach ppl on #epitwitter #medtwitter

1/
Before I begin: I have the utmost respect for @easchisterman and the @AmJEpi team. But, I have a particularly strong reaction to articles claiming to have evidence of a “true” obesity paradox.

Science moves forward through scholarly debate. Let’s keep the discussion courteous!
Also, PLEASE RT and share with colleagues on #epitwitter #medtwitter #statstwitter #academictwitter

I'd love to engage and answer any questions you may have!

Here's the article I'm tweeting about:
ncbi.nlm.nih.gov/pubmed/31504124
Read 11 tweets
If you are moderating a focus group, please be aware of the introvert participants. Extroverts would dominate the discussion and... #phdchat #qualitativeresearch #AcademicChatter #AcademicTwitter #epitwitter #meded /1
is your duty as the moderator to verbalize any nonverbal cue of the introverts to invite them to participate. Example: “Mike I noticed you smiling when Karen mentioned … What do you think about it?”. #phdchat #qualitativeresearch #AcademicChatter /2
“I noticed that you put a surprise face when … was mentioned. What are your thoughts?” Everyone should be involved in the discussion as early as possible. #phdchat #qualitativeresearch #AcademicChatter /3
Read 5 tweets
Hello #epitwitter! Time for an @epiellie @AmJEpi tweetorial.

Today’s topic is the Target Trial Framework for #causalinference and how to apply it to improving observational studies.

#epiellie
So, what is the #targettrial framework?

Well it’s not a new method! Instead think of it as pedagogical device that provides a structured way to build your research question and study design for observational studies and minimizes the potential for bias.
What does that mean?

To design an observational study, we first think about what the ideal hypothetical randomized trial (target trial) is that would let us answer our research question.

Then, we try to match our observational study as closely as possible to that trial design.
Read 20 tweets
@shiftkeylabs @DigiHealthCA @dalfcs @DalWiTS You do understand that healthcare wait lists are not standard queuing processes like supermarket checkouts, or call centres, right? #scitwitter #statstwitter #epitwitter
@shiftkeylabs @DigiHealthCA @dalfcs @DalWiTS 1. There are strong screening and threshold effects, where clinical gatekeepers roughly objectively assess patients for access to the wait list. #scitwitter #statstwitter #epitwitter
@shiftkeylabs @DigiHealthCA @dalfcs @DalWiTS 2. There are anticipatory effects where clinical gatekeepers will aggressively enrol patients to wait list if they know new resources are being added, and conversely will limit access when they know resources are going to be constrained. #scitwitter #statstwitter #epitwitter
Read 7 tweets
Good luck @dryiu_verna and @jkenney with the AHS review when @EYnews doesn't even understand the principle of the Nash Equilibrium. Hint: it's why we have problems like resistant strains of infections. #ableg #abpoli #scitwitter #statstwitter #epitwitter #NotEvenWrong
@dryiu_verna @jkenney @EYnews In healthcare the Nash Equilibrium occurs because clinicians try to do what's best for the immediate problem right in front of them, without view to the future clinicians or broader society. #ableg #abpoli #scitwitter #statstwitter #epitwitter #NotEvenWrong
@dryiu_verna @jkenney @EYnews The result is that we end up trying to cure ageing through the primary, ambulatory, or inpatient care. #ableg #abpoli #scitwitter #statstwitter #epitwitter #NotEvenWrong
Read 9 tweets
1/ #EpiTwitter tweetstorm coming

Warning: strong opinions

The analysis is naive and the findings are ridiculous; the fact that it was published is a sign that when medical journal editors hear "deep learning AI" their brains stop working.
2/ first of all, the sheer improbability of the claim. 

That you can predict skin cancer in general pop in the next year w AUC 0.9 (and 0.8+ based on medications alone)

That is nuts.

PHOTOGRAPHS OF SUSPICIOUS SKIN LESIONS have worst test characteristics.
3/ it's as if all previous knowledge of non-melanoma skin cancer epidemiology was wiped out.

Age doesn't matter much. Medications not known to be associated w skin cancer matter a lot.

Many of the meds are taken for years/decades, so couldn't have a huge impact on 1-year risk
Read 17 tweets
After the wonderful @EANBoard #SoMe4Epis module on social media for public health professionals, on request:

[THREAD] Here’s my bag of resources and explanations for beginners, to get started on social media

#epitwitter
Kicking off with an introduction to social media for scientists by @hollybik & @MiriamGoldste

All in the familiar academic format of a journal article: dx.plos.org/10.1371/journa…

#scicomm @PLOSBiology
So why engage in social media as an epidemiologist or public health professional?

It helps to build trust in science!

#PLOSONE: Using selfies to challenge public stereotypes of scientists

#scicomm #scientistswhoselfie
Read 17 tweets
WTAF😠 @sciencemagazine

The conceptual idea behind this paper should trigger an instant desk-rejection in any Journal with a shred of dignity.

@cecilejanssens @TimothyLash @tpahern @LaurenAnneWise @EpiEllie #medtwitter #epitwitter @VPrasadMDMPH @OwenJones84

1/4
A few of many questions:

1) What was the research question addressed?

2) What was the suggested added value to mankind from the study proposal?

3) What kind of scientist pitches such a study proposal?

4) Importantly: What kind of scientists says "Great idea, let's go"?

2/4
5) Which Institutional Board and Ethics Committee approves such study proposal?

6) Who funds such study proposal?

7) @ The Authors: Were you unable to find a better cause for your intellect, competences and resources? Really?

3/4
Read 5 tweets
Hi #pediatricians, our work on #disparities in #disability id. was just cited by the @AmerAcadPeds in their #policy statement on the impact of #racism on children's health. Our most recent study was cited. If helpful, here is a thread of our work to date. #RacismAndHealth (1)
@AmerAcadPeds Here is the abstract and link the the study cited in the @AmerAcadPeds policy statement. journals.sagepub.com/doi/pdf/10.310…. We find that children of color are less likely to be identified as having disabilities than similarly situated students who are White in U.S. schools (2)
@AmerAcadPeds Here is an example finding. Of U.S. 4th grade students displaying clinically significant reading difficulties, 74% of White students are receiving special education. The contrasting percentages for Black, Hispanic, and American Indian students? 44%, 43%, and 48%, respectively (3)
Read 15 tweets

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