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Nothing prettier to us than a full Bike to Cure starting line ready to take off! 😍 Have you registered yet? bit.ly/3gCfWq8
Thanks for the clarification. I forgot our url changed
For anyone who hadn't realized yet, there is a new link for the web calculator to perform prediction model development sample size calculations according to @Richard_D_Riley et al's method riskcalc.org/samplesize/ #epitwitter
The @socmdm Career Achievement Award winner is Michael Kattan @CCLRI #SMDM22 (4/4) Thank you to everyone who nominated members for our awards and congratulations to all of this year's winners! 🏆
This week on the @ClevelandClinic Cancer Advances podcast I talk to @CleClinicMD radiation oncologist Rahul Tendulkar (@RTendulkarMD) and Chair Dept Quantitative Health Sciences @mike__kattan about use of nomograms for patients with #cancer my.clevelandclinic.org/podcasts/cance…
Computable phenotype of disease health states transition probabilities b/w health states estimates real-world impact! @JKurowskiMD @JPAchkarMD @CCLRI bit.ly/3HLDUf2
We can already anticipate the (aging) objections, but the idea of re-branding at least a subset of GS6 seems to be picking up momentum. Feels like the right side of history :) @uroegg @aleberlin2 @VickersBiostats @journotwit ascopubs.org/doi/full/10.12…
NEW PREPRINT Assessing performance and clinical usefulness in prediction models with survival outcomes: practical guidance for Cox proportional hazards models (with SAS and R code) medrxiv.org/content/10.110…
Most analyses that found response a surrogate ignore immortal time bias. Congratulations @sujataCLE and @DrGarciaAguilar for a rigorous analysis that avoids this common trap.
Response not a surrogate for survival in rectal cancer treated with NAT. Find why at academic.oup.com/oncolo/advance…
Fantastic work by @LalDennis and team! lerner.ccf.org/news/details/?… #epilepsy #genetics
The work from @BruceSchackman is extra acknowledge with the @socmdm Lusted student prize in Health Services, Outcomes, and Policy Research now named for Bruce Schackman! Congratulations 👏 #SMDM21
So many "regression versus machine learning" comparisons are not fair, as they don't allow non-linear trends for predictors in the regression model (eg using splines or polynomials). As such, they only serve to reveal a lack of understanding of good statistical practice
Thanks for comments in @TheScientistLLC article Melvin (@cancerphysicist & @IBCradiation & @lecteroide). One thing that's confusing though: the HR for GARD (close to 1, as you point out, 0.98) is PER UNIT GARD -- this is a continuous analysis. cont. thelancet.com/journals/lanon…
Thanks @TheScientistLLC for highlighting this work led by @jtorresroca @TheLancetOncol Time is NOW to finally test this in a prospective trial! @theabzlab @aleberlin2 @DrSpratticus @seanmmcbride @NRGonc @sueyom the-scientist.com/news-opinion/t…
the point of the nomogram in figure 4 is to show this, but we put things back into the ABSOLUTE world of each disease (rather than the abstraction needed for the pooled analysis where everything was RELATIVE).
while each patient requires a different amount of physical dose to increase 1 unit of GARD (based on radiosensitivity), imagine a scenario where you dose-escalate a patient by 10 GARD, this would yield a predicted HR = 0.98^10 = 0.82 and so on for other GARD changes
Estimating patients' individualized probability of an event of interest is challenging. A must-read book by Gerds and @mike__kattan clearly explains and guides you when developing and validating prediction models. #predictionmodels #machinelearning
TWEETORIAL linked here: twitter.com/CancerConnecto… #clecliniccancer @MoffittResearch @CCLRI @CWRUSOM @cwrumstp @CleClinicMD
TWEETORIAL on our new #radonc paper in @TheLancetOncol -- joint work from @CCLRI @MoffittNews @CWRUSOM 1/n Pan-cancer prediction of radiotherapy benefit using genomic-adjusted radiation dose (GARD): a cohort-based pooled analysis thelancet.com/journals/lanon… @OncoAlert
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