Discover and read the best of Twitter Threads about #causalinference

Most recents (24)

1/
Our findings on a fourth dose (2nd booster) of the Pfizer-BioNTech #COVID19 vaccine are now published.

Compared with 3 doses only, a fourth dose had 68% effectiveness against COVID-19 hospitalization during the Omicron era in persons over 60 years of age.

Interestingly...
2/
... this is yet another example of the need for good #observational studies that emulate a #TargetTrial.

Would it be better to have a real randomized trial? Yes

Do we have a randomized trial? No

Will we have a randomized trial? Perhaps, but too late for a timely decision.
3/
Last year, observational evidence was also used to recommend a first vaccine booster.

Our and others' studies provided evidence on the booster's protection against hospitalization after infection with Delta:


Policy makers listened. Lives were saved...
Read 8 tweets
1/
We emulated a target trial of two #COVID19 mRNA vaccines in the largest healthcare system in the US.

Both vaccines were similarly effective, with Moderna slightly better than Pfizer-BioNTech.

But that isn't the most important conclusion of our study.

2/
Spring of 2020: #COVID19 vaccines are developed.

October 2020: Results from randomized trial are announced.
businesswire.com/news/home/2021…

~6 months from development to evaluation of effectiveness.

Utterly impressive. Unprecedented.

Kudos to the pharmaceutical industry.

Now...
3/
December 2020: Vaccines become available.

December 2021: Where are the big randomized trials for COMPARATIVE effectiveness?

1 year, still *crickets*

Billions of taxpayer dollars and we don’t get to know which vaccine is better and safer?

Not so impressive, pharma industry.
Read 6 tweets
E-Cigarette or Vaping Product Use–associated Lung Injury: Developing a Research Agenda. An NIH Workshop Report

10/23/2019

“…biospecimens…are urgently needed to help define EVALI pathogenesis…”

“e-cigarette or vaping product use–associated lung injury”

#EVALI_investigation The NHLBI convened a workin...
Key Points of Agreement  1....
“The etiology of e-cigarette or vaping product use–associated lung injury (EVALI) remains unknown…a specific cause for EVALI has not been proven mechanistically.”

#Vitamin_E_Acetate #VEA #causaltwitter #Causalinference The etiology of e-cigarette...
Read 34 tweets
1/
Vaccine safety: We compared excess adverse events after #COVID19 vaccination (Pfizer-BioNTech) and after documented #SARSCoV2 infection.

nejm.org/doi/full/10.10…

Take-home message: Low excess risk of adverse events after vaccination, higher after infection.

Some thoughts👇
2/
Preferring #SARSCoV2 infection over vaccination has become even harder. (Remember: infection also increases the risk of severe disease/death)

This is a good illustration of how #randomized trials and #observational studies complement each other for better #causalinference...
3/
The original #randomized trial estimated vaccine effectiveness to prevent symptomatic infection, but was too small to quantify vaccine safety.

That's what #observational studies do.

Now a different sort of question: Why could we do this study in the first place?

2 reasons.
Read 8 tweets
1/ Readers ask: What's the simplest problem in which a combination of experimental and observational studies can be shown to be better than each study alone?
Ans. Consider X--->Z----> Y
with unobserved confounder between X & Z.
Query Q: Find P(y|do(x))
We have 2 valid estimands:
2/
ES1 = P(y|do(x)) estimable from the experiment
ES2 =SUM_z P(z|do(x))P(y|z), the first term is estimable from the experiment, the second from the observational study.
ES2 is better than ES1 for 3 reasons:
1. P(y|z) can rest on a larger sample
2. ES2 is composite (see
3/ (see ucla.in/2ocoWqq for advantage of composite estimators)
3. ES2 need not measure Y in the experimental study.
Remark: The validity of ES2 follows from do-calculus. Adding any edge to the graph invalidates ES2 and leaves ES1 the only estimand.
Read 4 tweets
1/ Q: Do we know more about whether the vaccines reduce transmission?

A: YES! Evidence continues to mount that the vaccines DO in fact reduce transmission.

#vaccineswork
dearpandemic.org/do-vaccines-re…
2/ ➡️ While the Nerdy Girls are still dreaming of transmission being tested in the clinical trials (call us☎️), the data pouring in from around the world strongly suggests that the vaccines are very good (but not perfect) at reducing ALL infections & by extension transmission.
3/ Recall the clinical trials (largely) tested only participants who developed symptoms. One possibility is that vaccines reduce the severity of COVID-19 to the point of no symptoms, but still allow the virus to replicate in the nose and throat & potentially be spread to others.
Read 20 tweets
🧵 1/ @TamaraSurin’s smart (and *very* generous) take on recent @JAMA_current podcast on “structural racism”...
🧵 2/ Now here’s my take: I believe that the host, JAMA editor Edward Livingston, inadvertently makes a strong argument against his own thesis!
🧵3/ Dr. Livingston argues that using the word “racism” is counterproductive for addressing “very real” “structural problems in our society”
Read 23 tweets
1/
We've just confirmed the effectiveness of the Pfizer-BioNTech vaccine outside of randomized trials.

Details @NEJM: nejm.org/doi/full/10.10…

Yes, great news, but let's talk about methodological issues that arise when using #observational data to estimate vaccine effectiveness.
2/
A critical concern in observational studies of vaccine effectiveness is #confounding:

Suppose that people who get vaccinated have, on average, a lower risk of infection/disease than those who don't get vaccinated.

Then, even if the vaccine were useless, it'd look beneficial.
3/
To adjust for confounding:

We start by identifying potential confounders.

For example: Age
(vaccination campaigns prioritize older people and older people are more likely to develop severe disease)

Then we choose a valid adjustment method. In our paper, we matched on age.
Read 12 tweets
🧵I’ve written before about the undercurrent of misogyny I perceive in the public debate about in-person K-12 schooling in the US...1/
🧵I’ve stopped much writing about #SARSCoV2 & K-12: I think weight of evidence is strong that K-12 (esp for younger ages) can be operated w very low levels of in-school #SARSCoV2 transmission when precautions in place (new variants may change this) dontforgetthebubbles.com/evidence-summa… ...2/
🧵...and I’ve stopped writing about it because the debate seems polarized and hardened (although there’s a LOT of policy movement rn nationally...we’ll see how that all plays out). I just don’t have much to add that I think will change anyone’s mind 3/
Read 21 tweets
Too many broadly useful stats methods are masked in domain-specific language. In my new pair of posts, I discuss formula-free #causalinference design patterns to help data analysts recognize frameworks as they encounter them in everyday work

emilyriederer.netlify.app/post/causal-de…

1/3
I don't rehash the finer details; for that my resource round-up post catalogues the plethora of amazing books freely available from @_MiguelHernan @causalinf @CasualBrady @nickchk and more

emilyriederer.netlify.app/post/resource-…

2/3
Instead, I simply focus on the bare-bones frameworks. While econ and epi Twitter talk about CI nonstop, I'm struck by how underutilized some of the basics are in industry where we have rich high-dimensional panel data, well-defined but non-random treatment mechanism, etc
Read 3 tweets
@TaschnerNatalia @cnpaiva @FelipeBGSilva @MBittencourtMD Olá,
este paper tem muitos problemas, que como disse, serve para exemplo de aula.

Para focar em 1 tema, que vai de encontro ao que a Natalia escreveu e outros acima, são os vieses. Vieses assim não podem ser corrigidos com ajuste. Se fosse assim, tudo estaria resolvido. 1/n
@TaschnerNatalia @cnpaiva @FelipeBGSilva @MBittencourtMD Começa aqui, onde se cria um mecanismo perverso de viés de seleção. E isso criou essa disparidade entre os grupos que um mero ajuste multivariado não corrige. Também temos o immortal time bias, que é uma clássico erro. Esses são erros praticamente não passíveis de ajuste 2/n
@TaschnerNatalia @cnpaiva @FelipeBGSilva @MBittencourtMD Quanto a variáveis de confusão, isso já mudou muito na epidemiologia. E os erros vão desde a como lidar com variáveis contínuas e colocar variáveis pós exposição (collider-bias). Uma coisa simples que o Cox permite é time-dependent variable, por exemplo. 3/n
Read 8 tweets
I’m so excited! Past couple years, I’ve wanted to sit down & structurally* deconstuct Roland Fryer’s 2019 paper on racial difs in police use of force to use as a teaching example** re: #colliderbias. Alas life got in the way...1/
HT @l_farland
*analyze “structure” in a #DAG
#ColliderBias^ is rampant in US disparities research that uses admin data like hospital claims, police records. Almost all non-random samples will differentially select by race & other imp factors
^a selection bias with a particular structure in #causalinference #DAGs
2/
Side note: Dr. Chanelle Howe (@BrownUniversity) and I explore a different type of selection bias in this @EpidemiologyLWW paper

ncbi.nlm.nih.gov/pmc/articles/P…

Structural selection bias* is EVERYwhere in health disparities, research, y’all! 3/
Read 12 tweets
#SERweek2020 @casualinfer podcast being recorded LIVE NOW! @LucyStats @EpiEllie @AmJEpi
Great discussion about propensity score trimming and how we need to carefully interpret results. Trimming can change who is in the population.
When thinking about #MachineLearning and #CausalInference need to consider what you will be training it against
Read 7 tweets
1/ Super excited about our research on Medicare coverage delays among the uninsured and their effect on long-term PD use!

Published in the current issue of @MedicalCareLWW with @gchertow, Jay Bhattacharya, and Darius Lakdawalla. @SchaefferCenter
journals.lww.com/lww-medicalcar…
2/ We used a regression discontinuity design (a #causalinference method in observational studies to reduce selection bias) to identify the effect of this esoteric, but important, @CMSGov coverage policy
3/ The policy is strange and creates strange differences in the uninsured: most uninsured patients receive Medicare on day 1 of month 4 of dialysis (i.e., if you start dialysis June 1-30, you get Medicare Sept 1)
Read 19 tweets
Ingredients for a successful approach in managing pandemic:
1. Value Field experience
2. Utilise knowledge and skills
3. Abundant Precautions in communication
4. Unbiased analysis
5. Use Common sense

#2019nCoV #leadership #coronavirusindia
1ofN
6. Ethics should be as integral as epidemiological principles in the strategy

7. Ensuring Equity is the goal, not an accessory. Think of vulnerable before thinking of self preservation

2ofN #COVID__19 #pandemic #MigrantWorkers
8. Be open. Do not close doors to Ideas
People
Hypotheses
Alternatives

3ofN #2019nCoV #TheWHO #Asymptomatic
Read 6 tweets
I am excited to share my first working paper! @PHuenermund and I address why the interpretation of control variables in regression analysis should be avoided.

“On the Nuisance of Control variables in Regression Analysis”
arxiv.org/abs/2005.10314
#econometrics #causalinference /1
2/The interpretation of control variables is widespread within our research field. We checked the last five years of SMJ issues and found that 47% of papers that use parametric regression models with control variables also interpret or mention the results of their controls.
3/ We argue that this practice should be avoided because control variable estimates are unlikely to have causal interpretation themselves.
Read 6 tweets
#Causalinference that talks the talk and walks the walk.

Claim: "Continuing #breastcancer screening past age 75 doesn't reduce 8-year breast cancer mortality."

Emulation of a #TargetTrial led by @xabieradrian with Medicare data
doi.org/10.7326/M18-11…

Let the discussion start. Image
@xabieradrian @AnnalsofIM @HarvardEpi @harvard_data @HarvardBiostats @HarvardChanSPH @CMSGov @CMSgovPress @MonganInstitute @MassGeneralNews 2)

Because there's so much talk about #causalinference around here.

Computer scientists, economists, statisticians... talk a lot about the merits of #DeepLearning, instrumental variables, or whatever their preferred methodology is.

Everybody: This is your chance to shine. Image
3)

No more toy examples. A real world question:

"At what age should #breastcancer screening stop?"

Need to compare the mortality of women under two dynamic screening strategies using a database of insurance claims with time-varying treatments and confounders.

How'd you do it? Image
Read 5 tweets
Hype: "Give me data on millions on people and my algorithms will spit out gold."

Truth: Lots of data + sophisticated methods do not guarantee correct effect estimates.

Our empirical demonstration of the limits of observational data for #causalinference:
academic.oup.com/aje/article-ab…
<2% of deaths are from colorectal cancer. Even if screening prevented all colorectal cancer deaths (which is impossible), the effect wouldn't exceed 2%.

No matter what adjustment method @xabieradrian and us used, our effect estimate from a large claims database was always >5%. Image
Please join us in publishing your failures. It is the best way to fight hype in #causalinference from complex longitudinal data.

Algorithms may help but, at the end of the day, either you do or don't have data on treatments, outcomes, and confounders. It is really that simple.
Read 3 tweets
Award-winning, free material on #causalinference – here’s an overview on the 2013-2018 winners of the „Causality in Statistics Education Award“, awarded by .@AmstatNews, and initiated by .@yudapearl
In 2018, the award went to Jonas Peters, Dominik Janzing, and @bschoelkopf for their open-access textbook “Elements of Causal Inference”. This is the best textbook on the connection between machine learning and causal inference. It’s almost self-contained.
I think it is very useful for students in ML, stats, and physics, and is certainly very interesting for social scientists, because it views familiar topics from a different angle. mitpress.mit.edu/books/elements…
Read 12 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
Happy Tuesday, #epitwitter! There’s been a lot of discussion #onhere about #causalinference lately, but that’s not all that epi is about.

For this week’s #epichat with @epiellie, let’s talk about descriptive epi!

#epiellie
To kick things off, an #epiquiz:

How often do you do descriptive epidemiology in your work or research?

(click to see poll options)
Next question — this one’s for discussion so share your thoughts!

What differentiates descriptive epi from causal inference epi?

My take: descriptive epi is about who, what, where, & when; causal inference epi is about why, how, and what would happen if we changed something.
Read 11 tweets
1/3 I am curious, do you know of any paper that states explicitly the asymptotic equivalence of PS and the "adjustment formula"?? I have only seen it in Causality,
ch 11, not elsewhere, Why? Perhaps someone is interested in marketing it as a magic wand? I am also curious to know
2/3 how many readers hear this equivalence for the first time. It says that regardless of what covariates you use, as the number of samples increases, the bias of the PS estimator converges to the bias of the adjustment estimator. If many hear about it for the first time, it will
3/3 serve as an example of information stifling that CI needs to liberate itself from. Anyone knows what the new CI textbooks say about PS? I would use it as a litmus test for authors understanding of modern CI. #causalinference #Bookofwhy @causalinf
Read 3 tweets
Hey everyone! Here’s a #tweetorial on our new paper on why we often can’t make #causalinference using #distancetocare as an exposure or instrument! cc: @epiellie
There are 3 problems with #distancetocare as an exposure for estimating causal effects. Let’s walk through them.
The first problem that probably comes to mind is #confounding 🙀Choices about where to live and where to locate care facilities are complicated and depend on a lot of things we might not be able to measure (e.g. socioeconomic status).
Read 10 tweets
Our classification of #DataScience tasks is now in print.

We can only do 3 things with data:
1. description
2. prediction
3. counterfactual prediction (including #causalinference)

Each task requires different data, methods, and subject-matter knowledge.
amstat.tandfonline.com/doi/full/10.10…
Corollary: As much as we love data analysis competitions, they aren't a good fit for causal inference.

Researchers who turn to #causalinference competitions for practical advice are like the farmer who asked the local university for help to increase his cows' milk production... Image
... A sophisticated theoretician wrote back: “I have the solution, but it works only in the case of a spherical cow in a vacuum.”

Reducing #causalinference to blind data analysis works only in artificial settings with no colliders, unmeasured confounders.
projecteuclid.org/euclid.ss/1555… Image
Read 3 tweets

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