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Posts by Justin L

Julie Josse March 18 Methods Series talk, "Risk Difference, Risk Ratio, Win Ratio: Key Properties for Transportability and Federated Causal Inference."

Julie Josse March 18 Methods Series talk, "Risk Difference, Risk Ratio, Win Ratio: Key Properties for Transportability and Federated Causal Inference."

Risk difference, risk ratio, win ratio.

Julie Josse, Senior Researcher at INRIA, presents next for the CAUSALab Methods Series @ki.se.

📆 March 18, 2025
⏰ 15.00 CEST/10.00 ET
📍 All Methods Series talks virtual

Register to attend 👇
stats.sender.net/forms/e7JD1d...
#causalinference #publichealth

1 year ago 13 2 1 1
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Hilarious. After the DOGErs set up the new government-wide all employee email system in late January OPM, where it is housed, sent out an advisory which states explicitly that no employee ever has to respond to any of the emails. See 4.2 and 4.3. www.opm.gov/media/kfpozk...

1 year ago 6039 2031 162 148

Are you interested in age or cohort or period effects? I have just updated my primer on the topic!
Now with a new framing, a handy flow chart to determine which approach may be suitable, and a new empirical example. What do people in Germany think about mothers who work? osf.io/preprints/ps...

1 year ago 148 37 4 3
When estimating a treatment effect with a cluster design, you need to include varying slopes, even if the fit gives warning messages. | Statistical Modeling, Causal Inference, and Social Science

When estimating a treatment effect with a cluster design, you need to include varying slopes, even if the fit gives warning messages.
statmodeling.stat.columbia.edu/2025/01/23/s...

1 year ago 53 15 3 5
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Upgrade your #causalinference arsenal.

A revision of our book "Causal Inference: What If" is available at miguelhernan.org/whatifbook

Thanks to everyone who suggested improvements, reported typos, and proposed new citations and material.

Enjoy the #WhatIfBook plus code and data. Also, it's free.

1 year ago 370 113 10 7
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Posted this at the old place, but thought folks on @bsky.app might like to know I finally met my "mother".

1 year ago 147114 9149 2922 529
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Introducing PowerLMM.js!

A new tool for power analysis of longitudinal linear mixed-effects models (LMMs) – with support for missing data, plus non-inferiority and equivalence tests.

powerlmmjs.rpsychologist.com

Would really appreciate your feedback as I refine this app! Details below 🧵👇

1 year ago 299 111 11 10
How to get your article rejected by the BMJ: 12 common statistical issues
How to get your article rejected by the BMJ: 12 common statistical issues YouTube video by Richard_D_Riley

Christmas 2024 gifts for your 'significant' other 🎄

1. How to get your article rejected by the BMJ (at Christmas or anytime)

Video: www.youtube.com/watch?v=iu4V...

Article: www.bmj.com/content/379/...

1 year ago 29 8 1 2
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It's that time of year to bring out our @bmj.com xmas paper On the 12th Day of Christmas, a Statistician Sent to Me... from 2022 (w/ @richarddriley.bsky.social) with our list of commonly seen statistical faux pas🎄

www.bmj.com/content/379/...

#StatsSky #EpiSky

1 year ago 21 10 0 0
Tiger Woods and John Daly meme. Where Tiger Woods - looking professional - is labelled "doubly-robust control for confounding". And John Daly - looking very relaxed and smoking a cigarette - is labelled "Selection bias".

Tiger Woods and John Daly meme. Where Tiger Woods - looking professional - is labelled "doubly-robust control for confounding". And John Daly - looking very relaxed and smoking a cigarette - is labelled "Selection bias".

STOP IGNORING SELECTION BIAS!
#EpiSky #StatsSky

1 year ago 67 11 2 0
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Woohoo, our paper is live!

This haiku summarizes it best:

Raw model results?
Stop! Hard to understand! Use
{marginaleffects}

1 year ago 190 31 5 1
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1 year ago 10 3 2 5
BEMC NOV 2024 - William Lowe - Collider bias (complimentary)
BEMC NOV 2024 - William Lowe - Collider bias (complimentary) YouTube video by Berlin Epidemiological Methods Colloquium

The organisers have sensibly kept this hidden as long as possible, but here's the talk.

youtu.be/m56YEkkWYzI

CW dad jokes, tendentious claims, Monty Hall problem.

Best watched at zero speed, while doing something actually productive instead.

1 year ago 30 7 3 2
Save the Dates! CAUSALab Summer Courses on Causal Inference

Week 1 Courses: June 16-20, 2025
No courses on June 19th in observance of Juneteenth. June 20th extended to full day.

Week 2 Courses: June 23-27, 2025

Save the Dates! CAUSALab Summer Courses on Causal Inference Week 1 Courses: June 16-20, 2025 No courses on June 19th in observance of Juneteenth. June 20th extended to full day. Week 2 Courses: June 23-27, 2025

Save the dates!

CAUSALab’s Summer Courses on #causalinference return June 2025. Detailed course information to be announced in the coming weeks.

Join our 2024-2025 course listserv to receive updates first: harvard.az1.qualtrics.com/jfe/form/SV_...

Learn more:
causalab.hsph.harvard.edu/courses/

1 year ago 16 7 0 0

As well as prognostic factors that *aren't* imbalanced! It's amazing. Links to good advice on "how" in the post below.

1 year ago 21 7 1 0
Mirrored histogram showing “weird” parts of the population: treated people who were unlikely to be treated, and untreated people who were likely to be treated

Mirrored histogram showing “weird” parts of the population: treated people who were unlikely to be treated, and untreated people who were likely to be treated

Mirrored histogram showing pseudo-populations of treated and untreated people that have been reweighted to be more comparable and unconfounded

Mirrored histogram showing pseudo-populations of treated and untreated people that have been reweighted to be more comparable and unconfounded

Table showing potential and realized outcomes for 9 simulated people

Table showing potential and realized outcomes for 9 simulated people

Before we calculate these different treatment effects with the realized outcomes instead of the hypothetical potential outcomes, let's look really quick at the practical difference between the true ATE, AT 1, and ATU. All three estimands are useful for policymaking!
The ATE is -15, implying that mosquito nets cause a 15 point reduction in malaria risk for every person in the country. This includes people who live at high elevations where mosquitoes don't live, people who live near mosquito-infested swamps, people who are rich enough to buy Bill Gates's mosquito laser, and people who can't afford a net but would really like to use one. If we worked in the Ministry of Health and wanted to know if we should make a new national program that gave everyone a free bed net, the overall reduction in risk is -15, which is probably pretty good!
The ATT is -16.29, which is bigger than the ATE. The effect of net usage is bigger for people who are already using the nets. This is because of underlying systematic reasons, or selection bias. Those using nets want to use them because they need them more or can access them more easily-they might live in areas more prone to mosquitoes, or they can afford to buy their own nets, or something else. They know themselves and understand some notion of their personal individual causal effect and seek out nets. If we removed access to their nets, it would have a strong effect.
The ATU is -13.63, which is smaller than the ATE. The effect of net usage is smaller for people who aren't using the nets. Again, this is because of selection bias. Those not using nets are likely not using them for systematic reasons-they live far away from mosquitoes, they've received a future malaria vaccine, they have some other form of mosquito abatement, or something else. Because they can read their own minds, they know that mosquito net use won't do much for them personally, so they don't seek out nets. If we expanded access to nets to them, they wouldn't benefit

Before we calculate these different treatment effects with the realized outcomes instead of the hypothetical potential outcomes, let's look really quick at the practical difference between the true ATE, AT 1, and ATU. All three estimands are useful for policymaking! The ATE is -15, implying that mosquito nets cause a 15 point reduction in malaria risk for every person in the country. This includes people who live at high elevations where mosquitoes don't live, people who live near mosquito-infested swamps, people who are rich enough to buy Bill Gates's mosquito laser, and people who can't afford a net but would really like to use one. If we worked in the Ministry of Health and wanted to know if we should make a new national program that gave everyone a free bed net, the overall reduction in risk is -15, which is probably pretty good! The ATT is -16.29, which is bigger than the ATE. The effect of net usage is bigger for people who are already using the nets. This is because of underlying systematic reasons, or selection bias. Those using nets want to use them because they need them more or can access them more easily-they might live in areas more prone to mosquitoes, or they can afford to buy their own nets, or something else. They know themselves and understand some notion of their personal individual causal effect and seek out nets. If we removed access to their nets, it would have a strong effect. The ATU is -13.63, which is smaller than the ATE. The effect of net usage is smaller for people who aren't using the nets. Again, this is because of selection bias. Those not using nets are likely not using them for systematic reasons-they live far away from mosquitoes, they've received a future malaria vaccine, they have some other form of mosquito abatement, or something else. Because they can read their own minds, they know that mosquito net use won't do much for them personally, so they don't seek out nets. If we expanded access to nets to them, they wouldn't benefit

From the archives: Have you (like me!) wondered what the ATT means and how it's different from average treatment effects? I use #rstats to explore why we care about (and how to calculate) the ATE, ATT, and ATU #polisky #episky #econsky www.andrewheiss.com/blog/2024/03...

1 year ago 205 44 8 5
Biostatistics for Biomedical Research - 16  Analysis of Observer Variability and Measurement Agreement

#statistics thought of the day:For measurement agreement/observer variability, consider powerful & easier to interpret (than variance components) all-relevant-pairs-mean-absolute-discrepancies, "relevant" chosen to match inter/intra observer interest hbiostat.org/bbr/obsvar #StatsSky #epiSky

1 year ago 37 4 3 0

Yo, Chris. Don’t forget about me 😈.

1 year ago 1 0 1 0

no idea what i’m doing on here yet but these starter packs seem cool. let’s put one together for the SEM crowd, yes?

please add folks and grow the community!

go.bsky.app/VYkpXdS

1 year ago 23 11 14 2
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Getting ready for winter adventures. #dogsofbluesky #adventuredogs

1 year ago 10 0 0 0
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Telling Stories with Data - 16  Multilevel regression with post-stratification

Was recommending to a colleague POSTSTRATIFICATION (*distant thunder*) and even MULTILEVEL REGRESSION AND POSTSTRATIFICATION (*closer thunder*). I pointed them at @rohanalexander.bsky.social's book, chapter 16, which is free online. Contains an election forecasting example.

1 year ago 94 15 2 1

Had a blast teaching this today! All the #rstats materials are available at talks.andrewheiss.com/2024-11-13_u...

1 year ago 101 18 4 2
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Reference Collection to push back against "Common Statistical Myths" Note: This topic is a wiki, meaning that this main body of the topic can be edited by others. Use the Reply button only to post questions or comments about material contained in the body, or to sugge...

In clinical research, you will often receive feedback on study design, stats, and/or data analysis from an editor or reviewer that is simply wrong. Here is a list of common "statistical myths" and references you can use to push back.

discourse.datamethods.org/t/reference-...

1 year ago 379 170 47 14
3  Measures of Evidence – Introduction to Bayes for Evaluating Treatments

#statistics thought of the day:Is an EHR randomization defective? Simple example contrasting frequentist and Bayesian inference. Would you rather have Pr(getting MORE heads than what we observed if a coin is fair),or Pr(Pr(heads) is outside [0.48, 0.52])? #stats #rstats hbiostat.org/bayes/bet/ev...

1 year ago 16 5 2 0
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Scientific reasoning driven by influential data: resuscitate dfstat! In biomedical literature, one of the most widely employed statistical procedures to analyze and visualize the association between two variables is linear regression. Data points that exert influence o...

Researchers find nearly 30% of papers “that stated significances (or their absence) are based on the presence of a single influential data point.”

1 year ago 13 9 0 0
Statistical Thinking - What Does a Statistical Method Assume? Sometimes it is unclear exactly what a specific statistical estimator or analysis method is assuming. This is especially true for methods that at first glance appear to be nonparametric when in realit...

New blog article on what it means for a statistical method to make an assumption. fharrell.com/post/assume

2 years ago 26 14 2 2
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Categorizing Continuous Variables Problems Caused by Categorizing Continuous Variables References | Key reference | Key Reference for Response Variables | More References | Myths About Risk Thresholds | Dichotomania Optimum decisio...

That’s one of the best papers ever on dichotomania. It’s listed as the key reference in discourse.datamethods.org/t/categorizi... #stats #statistics

1 year ago 15 7 0 0
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The collective hours of life I have wasted while formatting a document in the academic world. 😡.

1 year ago 2 0 0 0
Statistical Rethinking 2022 - Theatrical Trailer
Statistical Rethinking 2022 - Theatrical Trailer Montage of animations from the 2022 lectures. Playlist: https://www.youtube.com/playlist?list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN

Local registration for the 2024 round of my FREE OPEN ONLINE Statistical Rethinking course has begun. I'll open up registration on Sunday 3 December. See theatrical trailer below and other details on the course github page #stats 🧪 github.com/rmcelreath/s...

2 years ago 68 43 2 7