Discover and read the best of Twitter Threads about #statstwitter

Most recents (11)

"Between 2006 and 2015, ... colorectal cancer rates increased by 3.47 percent among Canadian men under age 50. And from 2010 to 2015, rates increased by 4.45 percent among women under age 50."

Let's check the data ...
1/2

nyti.ms/2Yyk9zx
Who teaches that fitting a regression line is drawing a straight line and drawing as many other straight lines as needed and wherever needed?
2/2

@f2harrell @statsepi @MaartenvSmeden #epitwitter #statstwitter #peerreviewed @JAMANetworkOpen
jamanetwork.com/journals/jaman…
I am adding a reference to the method here, which I didn't know about (and which I still don't understand why that can be applied to 45 datapoints without over fitting--maybe someone can explain).
Read 3 tweets
@statsepi @ashtroid22 @Epi_D_Nique @healthstatsdude @stephensenn (1/n) The problem Senn is referring to here, was solved by Mindel C. Sheps in NEJM in 1958. I proposed a counterfactual causal model that formalizes the intuition behind her argument in this paper: degruyter.com/view/j/em.2018…
@statsepi @ashtroid22 @Epi_D_Nique @healthstatsdude @stephensenn (2/n) Consider this example: If people both Russia and Norway are randomized to play Russian Roulette at the end of every year, and the baseline risk of death in a year i 0.5% in Norway and 1.0% in Russia: Calculate how many people will die in the active arm in each country.
@statsepi @ashtroid22 @Epi_D_Nique @healthstatsdude @stephensenn (3/n) If you do the maths, you will find that 17.5% die in the active arm in Russia, and 17.1% die in the active arm in Norway. This means risk ratio is 17.5 in Russia and 35 in Norway. Odds ratio is 21 in Russia and 41 in Norawy, and survival ratio is 5/6 in both countries
Read 10 tweets
@KemiDoll @AdanZBecerra1 @WhitneyEpi That is not just a critque of racial disparity analysis, that is a significant vote of non-confidence in the current regression industry in contemporary epidemiology and public health in general. #Epitwitter #statstwitter #scitwitter
@KemiDoll @AdanZBecerra1 @WhitneyEpi "In this article, we highlight empirical examples from the literature demonstrating limitations of overreliance on interaction terms in health disparities research; we further suggest approaches for moving beyond interaction terms when assessing these disparities" #Epitwitter.
@KemiDoll @AdanZBecerra1 @WhitneyEpi This quote from the abstract applies in general, as regression against interactions is provably mathematically incapable of resolving the strongly geometric feed-backs that dominate population health. #epitwitter
Read 4 tweets
Cool moment today re: academic power of #statstwitter: looking through the reference list for a paper that I'm working on and seeing so many folks that I've 'met' through Twitter on the list of papers cited...
Read 3 tweets
A few people have DMed me about career advice as in "How do I get started in statistics?" I'm not where I want to be in life yet! But I have switched fields *multiple* times. So here are my tips on moving from insider to outsider ...
#epitwitter #statstwitter #datascience
1. Google a few people who started where you are and got to where you want to be. Look at their resume, work backwards and copy what they did. (This doesn't always work, especially if you are the first person like you.)
2. Find a person who's where you want to be. Tell them your background honestly. If they tell you that you aren't suited to their industry, ignore that for now. Get them to name one concrete gap that you have. Fill the gap. If you don't know how, then ask. Repeat as needed.
Read 9 tweets
The reductionist view of economists like @kevinmilligan, @GK_Fellows, & @trevortombe that healthcare and public health can be summarized or even reasoned about in terms of costs and returns on investments is the bane of my existence. #EpiTwitter #SciTwitter #StatsTwitter
The opinions of economists like @kevinmilligan, @GK_Fellows, & @trevortombe buoy up the worst instincts and shallowest decisions of healthcare administrators around the world. It is a constant battle of my career. #EpiTwitter #SciTwitter #StatsTwitter
Public health outcomes exist within a multi-dimension, perhaps even infinite dimensional, space of hazard functions, none of which is susceptible to the techniques of economic analysis. @kevinmilligan @GK_Fellows @trevortombe #EpiTwitter #SciTwitter #StatsTwitter
Read 5 tweets
Hello all #dataviz fanatics! It’s time to kick off our #datavizbook #epibookclub for @kjhealy’s new book, Data Visualization.

Remember, if your book hasn’t arrived yet or you’re waiting on the library, you can read it here: socviz.co

#epitwitter #datascience
This week, we’re reading the Preface and Chapter 1: Look at Data.

I’ll post some highlights from each and then I hope you’ll chime in with your thoughts, comments, questions, etc.
In the Preface we get a nice overview of the goal of this book: the why and how of good data visualization for beginners, including practical applications in R with ggplot2.

The book doesn’t assume any prior knowledge of R, & covers everything #dataviz from scatterplots to maps.
Read 18 tweets
Are you still shaking off the holiday? I know I am!

How about a #cartooncausalinference #tweetorial about casual graphs to ease us into the new year?

#epitwitter #DAGsfordocs #FOAMed #MedEd #statstwitter #econtwitter
The most common type of causal graph (at least on #epitwitter) is the directed acyclic graph, or #DAG.

DAGs have two main components: variables (also called nodes), and arrows (also called edges).

In the DAG below, there are 3 variables: sleeping, Santa, and presents.
The variables are ordered based on time β€” you have to go to sleep before Santa can come to your house & then he’ll leave presents!

Causation and time both flow in the direction of the arrows.
Read 24 tweets
πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„

I’m feeling festive! I want to spread the cheer this holiday season by giving the #GiftOfCode for making beautiful figures! #EpiTwitter #StatsTwitter

πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…πŸŽ„πŸŽ…
When I was starting out, I remember how it would take HOURS to get my figures looking just right.

Nothing was more valuable than example code for learning different #DataViz options 🎁
First up:

These options in #stata for making this graph presentation-ready!

adjustrcspline, link(logit) scheme(vg_palec) ///
ylabel(, nogrid angle(horizontal)) ///
xtitle("X (Units)", size(medium)) ytitle("Outcome Risk (%)", size(medium)) ///
graphregion(fcolor(white))
Read 7 tweets
Random thoughts about confidence intervals:

When we teach precision & accuracy, we often use images of a target, like these from the Wikipedia article: en.m.wikipedia.org/wiki/Accuracy_…

But I think this analogy leads to confusion about the interpretation of the confidence interval. 1/4
I’ve often heard students describe their confidence interval as if it were the fixed target, and the β€œtrue” value was the arrow that may or may not land on the target 95% of the time.... 2/4
But, the β€œtrue” value is the part they should view as fixed.

Instead, I propose explaining precision, accuracy, and confidence intervals with ring toss.

The β€œtruth” is in a fixed place, and it’s the confidence interval ring that may or may not land where you want it to. 3/4
Read 4 tweets
More problems with logistic regression in the medical literature:
(PubMed listing, if you prefer: ncbi.nlm.nih.gov/pubmed/27756470)
Read 28 tweets

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