Joshua F Wiley, PhD

221 posts

Joshua F Wiley, PhD

Joshua F Wiley, PhD

@WileyResearch

Researcher @MonashUni working on understanding and improving #sleep and #mentalhealth in #cancer . Always interested to hear about unmet cancer needs.

Melbourne, Australia Katılım Ocak 2017
252 Takip Edilen301 Takipçiler
Joshua F Wiley, PhD retweetledi
Bei Bei
Bei Bei@BeiSci·
🐈‍⬛Cat blood donor needed: Desperately looking for a healthy cat aged 1-7 years (Type A, but if unknown will test) who would be willing to donate cat blood to our seriously ill kitty in metropolitan Melbourne area😰
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JBMRPlus
JBMRPlus@JBMRPlus·
📢New Editor’s Pick from @JBMRPlus Associate Editor @GabrielaLoots! Increased bone formation and accelerated bone mass accrual in a man presenting with diffuse osteosclerosis/high bone mass phenotype and adenocarcinoma of unknown primary. (1/8) doi.org/10.1002/jbm4.1…
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Ursula Sansom-Daly, PhD
Ursula Sansom-Daly, PhD@usansomdaly·
“We need to talk about the elephant in the room” Kids/teens need their friends close by& that’s still true when they have #cancer. Fab talk by Hanne Bækgård Larsen💪🏻on the power of bringing friends INTO the hospital. With props from @JulieMcCrossin on the humane approach!#Surv23
Ursula Sansom-Daly, PhD tweet mediaUrsula Sansom-Daly, PhD tweet mediaUrsula Sansom-Daly, PhD tweet media
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Joshua F Wiley, PhD
Joshua F Wiley, PhD@WileyResearch·
@Nat_Sleep @hagsie summarised from meta-analyses, so a mix of outcomes, but I'd say fast majority are self-report sleep measures (e.g., PSQI, etc.)
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Joshua F Wiley, PhD
Joshua F Wiley, PhD@WileyResearch·
@drcarolynee @drcarolynee I did not get to meet you, but I'd be happy to meet and discuss with you if you are interested in brainstorming how we might help your patients' sleep better.
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Joshua F Wiley, PhD
Joshua F Wiley, PhD@WileyResearch·
@smartin2018 That was probably my favorite experience too. I felt like there was lots to learn and a nice environment to share. Could format and embed graphs, etc. I want a purely social stack overflow w/ syntax highlighting built in.
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Stephen Martin
Stephen Martin@smartin2018·
No joke though, I actually liked Google+, despite it being super unintuitive at first. It was nothing but Bayesian stats and Linux on my feed. @WileyResearch may remember those days.
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Stephen Martin
Stephen Martin@smartin2018·
Man, Google+ really missed the optimal release time. By many years.
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Joshua F Wiley, PhD
Joshua F Wiley, PhD@WileyResearch·
xkcd.com/2347/. CRAN sent out an email to ~3,500 #Rstats package authors that one package has not been fixed & is scheduled for archival in 2 weeks. Then, packages that depend on it run into issues (incl #ggplot2 and thus much modern R graphics digital infrastructure).
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Dan Goldstein
Dan Goldstein@dggoldst·
Are there readings or decision trees to help students learn how to choose a mixed-effects (random effects) model specification? There's much written on syntax, less on the principles. Something with examples of both (and newer than Gelman & Hill) would be great!
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@klmr@mastodon.social
@[email protected]@klmr·
@WileyResearch @brshallo (There are several other packages that implement (part of) the same functionality, including the package ‘import’ that’s mentioned in the article. But in my — obviously quite biased — opinion, ‘box’ does it better than the others.)
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Solomon Kurz
Solomon Kurz@SolomonKurz·
@VincentAB @arthur_alb1 @noah_greifer @MarkAnnuncio @statsepi Okay, thanks for the direction. To be honest, this is really disheartening. Realistically, there’s like no chance I’m reading through econometrics books. Further, if the methods from the medical statisticians rely on econometrics, there’s no chance I’ll be adopting those, either.
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Solomon Kurz
Solomon Kurz@SolomonKurz·
Finally had the time to study Robinson & Jewell (1991; doi.org/10.2307/1403444). Though adding a covariate in logistic regression tends to increase the standard error for the treatment effect, it also tends to positively bias the coefficient for the treatment effect. 1/2
Solomon Kurz@SolomonKurz

Related to some recent discussions: When you have pre/post RCT data with a binary (0/1) outcome, does controlling for baseline give you the same kind of power boost for the average treatment effect as it does with Gaussian data? Seems like no (e.g., doi.org/10.2307/1403444).

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