Lmst

The Single Arm Survival tool in jamovi #jsurvival module singlearm function
analyzes overall survival characteristics for phase II trials, cancer registries, and cohort studies. Simply input time-to-event data (or diagnosis/follow-up dates) and outcome status.
#Pathology #DigitalPathology #Biostatistics #Oncology #SurvivalAnalysis #jamovi

#statstab #456 Shall we count the living or the dead?

Thoughts: survival ratio -> if the intervention increases risk of the outcome
risk ratio -> if the intervention reduces risk of the outcome

#causalinference #riskratios #survivalanalysis #estimand

https://arxiv.org/abs/2106.06316v5

Registration is still possible for the GMDS ACADEMY 2025 (Hannover, October 20-23).
There will be three parallel workshops on meta analysis, causal inference and time-to-event analysis involving Wolfgang Viechtbauer (@wviechtb), Christian Röver, Sebastian Weber, Vanessa Didelez, Arthur Allignol, Oliver Kuß, Alexandra Strobel, Hannes Buchner, Xiaofei Liu and Ann-Kathrin Ozga.
See here for more details:
👉 https://www.gmds.de/fileadmin/user_upload/GMDS-Academy-2025.pdf

#MetaAnalysis #CausalInference #SurvivalAnalysis #GMDS

Announcement for the GMDS Academy 2025, listing topics and speakers.

This article details PCIC’s category repurchase modeling using survival, ARIMA, and behavioral features, plus frequency‑recency ranking of items. https://hackernoon.com/pcic-model-design-category-level-repurchase-prediction-and-frequencyrecency-item-ranking #survivalanalysis

Registration is open for the GMDS ACADEMY 2025 (Hannover, October 20-23).
There will be three parallel workshops on meta analysis, causal inference and time-to-event analysis involving Wolfgang Viechtbauer (@wviechtb), Christian Röver, Sebastian Weber, Vanessa Didelez, Arthur Allignol, Oliver Kuß, Alexandra Strobel, Hannes Buchner, Xiaofei Liu and Ann-Kathrin Ozga.
See here for more details:
👉 https://www.gmds.de/fileadmin/user_upload/GMDS-Academy-2025.pdf

#MetaAnalysis #CausalInference #SurvivalAnalysis #GMDS

Announcement of the GMDS Academy 2025, listing topics and speakers.

updating jamovi module for survival:
jsurvival work in progress
suggestions are welcome: https://github.com/sbalci/jsurvival
https://youtu.be/YY6Fs1F_2Xk
#jamovi #jsurvival #survivalanalysis #statistics #biostatistics

🎥 "Reporting Survival Analysis Results with gtsummary and ggsurvfit" 📊

Survival analysis is key for time-dependent endpoints, but making publication-ready tables and figures can be tough. This #NYRConf talk covers the basics and shows how to use these R packages to create manuscript-ready outputs. Watch now! 🧬📉

🔗 https://www.youtube.com/watch?v=TDWoIO8DuDs

#DataScience #SurvivalAnalysis #RStats

My thesis is finally not only handed in and graded but also in our universities thesis repository 🎉

https://www.doi.org/10.25365/thesis.76098

#survivalanalysis #rstats #biostat

v1.1.0 of {ggsurvfit} 📦 is on CRAN!

This release primarily includes updates to account for changes in the {survival}📦, but does include a few other gems.

https://www.danieldsjoberg.com/ggsurvfit

#rstats #datascience #survivalanalysis #ggsurvfit #cran

Image of a Kaplan-Meier survival curve created with the ggsurvfit R package

Recent #PeerReview work on #MixedModels and #GrowthModels had me going back to these classical texts on the genre:

1) https://journals.sagepub.com/doi/10.3102/01623737018004265

2) https://www.cambridge.org/core/journals/development-and-psychopathology/article/design-and-analysis-of-longitudinal-studies-of-development-and-psychopathology-in-context-statistical-models-and-methodological-recommendations/3F89776EE1F27BA4E4F49435AFBB7662

They are excellent tutorials on the most basic aspects of incorporating trajectories / functional change into such models. And the assumptions these require.

#HLM #History #SurvivalAnalysis

Really excited to attend #JupyterCon in Paris next month!

@vincent_m and I will give a full day tutorial on predictive Survival Analysis and Competing Risks modeling with a Gradient Boosting model assembled from generic scikit-learn building blocks. We will also introduce many concepts and model evaluation methodology using specialized libraries such as lifelines and scikit-survival.

Here is the full agenda for this session:

https://cfp.jupytercon.com/2023/talk/ACPULZ/

#PyData #SurvivalAnalysis #sklearn

`Introduction to the Cox model`

https://www.youtube.com/watch?v=CzHt22YL_-w

#coxModel #proportionalHazardModel #davidCox #sirDavidCox #survivalAnalysis #failureAnalysis #failureTimeAnalysis #forceOfMortality #reliabilityEngineering #kaplanMeier #failureDistribution #failureTimeDistribution #failureRate #hazardFunction #hazardRate #failureRate #nonParametric #model #modeling #statistics #stats #mortality #function #mathematics #math #proportionality #regression #regressionModel #leastSquares

`In most situations in our study, the Kalbfleisch Prentice estimator results in less bias and smaller mean squared error than the Breslow estimator. Their differences are especially clear at the tail of the distribution. The implications of such differences in applications are discussed. We advocate the use of Kalbfleisch Prentice estimator in practice, and further research on its properties.`

https://pubmed.ncbi.nlm.nih.gov/30370174/

#survivalAnalysis #lifetimeData #statistics #estimation #bias