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#survivalanalysis — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #survivalanalysis, aggregated by home.social.

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. The Single Arm Survival tool in jamovi 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.

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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:
    👉 gmds.de/fileadmin/user_upload/

    #MetaAnalysis #CausalInference #SurvivalAnalysis #GMDS

  12. This article details PCIC’s category repurchase modeling using survival, ARIMA, and behavioral features, plus frequency‑recency ranking of items. hackernoon.com/pcic-model-desi #survivalanalysis

  13. 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:
    👉 gmds.de/fileadmin/user_upload/

    #MetaAnalysis #CausalInference #SurvivalAnalysis #GMDS

  14. 🎥 "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 talk covers the basics and shows how to use these R packages to create manuscript-ready outputs. Watch now! 🧬📉

    🔗 youtube.com/watch?v=TDWoIO8DuDs

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

    doi.org/10.25365/thesis.76098

    #survivalanalysis #rstats #biostat

  16. 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.

    danieldsjoberg.com/ggsurvfit

    #rstats #datascience #survivalanalysis #ggsurvfit #cran

  17. 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:

    cfp.jupytercon.com/2023/talk/A

    #PyData #SurvivalAnalysis #sklearn

  18. `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.`

    pubmed.ncbi.nlm.nih.gov/303701

    #survivalAnalysis #lifetimeData #statistics #estimation #bias

  19. 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.

    danieldsjoberg.com/ggsurvfit

    #rstats #datascience #survivalanalysis #ggsurvfit #cran

  20. 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.

    danieldsjoberg.com/ggsurvfit

    #rstats #datascience #survivalanalysis #ggsurvfit #cran

  21. 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.

    danieldsjoberg.com/ggsurvfit

    #rstats #datascience #survivalanalysis #ggsurvfit #cran

  22. 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.

    danieldsjoberg.com/ggsurvfit

    #rstats #datascience #survivalanalysis #ggsurvfit #cran