Discover and read the best of Twitter Threads about #SurvivalAnalysis
Most recents (3)
Ever planned RCT w/ PFS and OS as formal endpoints?
Ever simulated PFS and OS for same set of patients?
In both cases: How did you model the fact that PFS and OS are highly dependent? E.g. we know that PFS <= OS and P(PFS = OS) > 0.
A (long) thread - but it' s worth it!
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Ever simulated PFS and OS for same set of patients?
In both cases: How did you model the fact that PFS and OS are highly dependent? E.g. we know that PFS <= OS and P(PFS = OS) > 0.
A (long) thread - but it' s worth it!
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We are very used to model dependency of an endpoint over time (= group-sequential designs) or over nested populations (= enrichment designs), b/c then it's not difficult to write down correlation between test statistics.
However, typically in trial planning PFS and OS...
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However, typically in trial planning PFS and OS...
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are treated as if they were independent and proportional hazards (PH) is assumed for both.
We propose to model dependency between PFS and OS using an illness-death multistate model (IDM), building on the work of Meller et al (2019) doi.org/10.1002/sim.82…
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We propose to model dependency between PFS and OS using an illness-death multistate model (IDM), building on the work of Meller et al (2019) doi.org/10.1002/sim.82…
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Going to break-down how easy it is to use #autoML and more specifically JADBio AutoML. If you need an account to try it out, head over to jadbio.com and grab a free Basic plan. Ready? #data #ML (1/16)
STEP 1: You start by creating a Project on JADBio and generating all your study #data. That could either be data that has been processed and normalized by a #Bioinformatician, or public data available in the known data repositories (2/16)
If you’re using software for #molecular diagnosis like our Partner’s @QIAGEN OmicSoft Lands platform, your data is ready to be uploaded on JADBio. #ML 3/16
*4th PLACE* of our most impactful articles published in @RSeriesa in 2020 according to @altmetric:
Tanner et al: doi.org/10.1111/rssa.1…
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Tanner et al: doi.org/10.1111/rssa.1…
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*CONTEXT* Dynamic prediction models provide predicted probabilities that can be updated over time as data become available. There is growing interest in the use of #machinelearning 🤖; yet, their use in the context of dynamic survival #prediction has been limited.
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*AIM* Show how #landmarking can be combined with a #machinelearning ensemble to improve prediction performance
*METHOD* Use of discrete time #survivalanalysis techniques to enable the use of #machinelearning algorithms for binary outcomes
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*METHOD* Use of discrete time #survivalanalysis techniques to enable the use of #machinelearning algorithms for binary outcomes
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