Fack, a cox model I'm working on is warning me on infinite estimates.
I'm going to follow @carlislerainey.bsky.social 's advice and report the coxph fit with infinite estimates and the coxphf fit, but was wondering if there is a simple implementation of coxph in stan
I'm going to follow @carlislerainey.bsky.social 's advice and report the coxph fit with infinite estimates and the coxphf fit, but was wondering if there is a simple implementation of coxph in stan
April 26, 2025 at 4:06 PM
Fack, a cox model I'm working on is warning me on infinite estimates.
I'm going to follow @carlislerainey.bsky.social 's advice and report the coxph fit with infinite estimates and the coxphf fit, but was wondering if there is a simple implementation of coxph in stan
I'm going to follow @carlislerainey.bsky.social 's advice and report the coxph fit with infinite estimates and the coxphf fit, but was wondering if there is a simple implementation of coxph in stan
Ihr schlagt doch bei rechtszensierten Daten (Dauer von Geschäftsprozessen) auch erstmal eine CoxPH Analyse vor, oder?
#nerd_alert
#nerd_alert
September 4, 2026 at 10:30 AM
Ihr schlagt doch bei rechtszensierten Daten (Dauer von Geschäftsprozessen) auch erstmal eine CoxPH Analyse vor, oder?
#nerd_alert
#nerd_alert
Inference for smooth functionals of M-estimands in survival models, like regularized coxPH and the beta-geometric model (see our experiments section) are one application of this approach.
May 12, 2025 at 5:56 PM
Inference for smooth functionals of M-estimands in survival models, like regularized coxPH and the beta-geometric model (see our experiments section) are one application of this approach.
Hat übrigens prima geklappt. Ich kannte Kaplan-Meier und CoxPH bislang nur aus der Medizin, es in Prozessanalyse zu verwenden war regelrecht erfrischend.
September 4, 2026 at 4:18 PM
Hat übrigens prima geklappt. Ich kannte Kaplan-Meier und CoxPH bislang nur aus der Medizin, es in Prozessanalyse zu verwenden war regelrecht erfrischend.
Survival analysis is a branch of statistics used to analyze the expected duration of time until one or more events happen, such as death in biological organisms or failure in mechanical systems.
#survfit #survdiff #coxph #survivalanalysis #dataanalysis #rprogramming #rlanguage #rquiz #learnRlanguage
#survfit #survdiff #coxph #survivalanalysis #dataanalysis #rprogramming #rlanguage #rquiz #learnRlanguage
Master Survival Analysis in R
In this post, we will learn about Survival Analysis in R. Survival analysis is a branch of statistics used to analyze the expected duration of time until one or more events happen, such as death in biological organisms or failure in mechanical systems. In R, the survival package is the gold standard for this analysis. Note that survival analysis includes several techniques that are used for modeling the time to an event.
rfaqs.com
April 10, 2026 at 6:48 PM
Survival analysis is a branch of statistics used to analyze the expected duration of time until one or more events happen, such as death in biological organisms or failure in mechanical systems.
#survfit #survdiff #coxph #survivalanalysis #dataanalysis #rprogramming #rlanguage #rquiz #learnRlanguage
#survfit #survdiff #coxph #survivalanalysis #dataanalysis #rprogramming #rlanguage #rquiz #learnRlanguage
New CRAN package coxstream with initial version 0.1.0
#rstats
https://cran.r-project.org/package=coxstream
#rstats
https://cran.r-project.org/package=coxstream
CRAN: Package coxstream
Fits the Cox proportional hazards model using a single descending-order pass per Newton-Raphson iteration. Peak RAM is O(p^2) regardless of the number of rows, making it suitable for datasets that do not fit in memory. Produces identical coefficients to survival::coxph() with Efron tie correction.
cran.r-project.org
June 20, 2026 at 2:02 PM
New CRAN package coxstream with initial version 0.1.0
#rstats
https://cran.r-project.org/package=coxstream
#rstats
https://cran.r-project.org/package=coxstream
no mind-blowing insight here, just revealing that I've never formally studied the theory behind survival analysis and always just used survival::coxph() without thinking too much about what it's doing
October 27, 2023 at 1:45 AM
no mind-blowing insight here, just revealing that I've never formally studied the theory behind survival analysis and always just used survival::coxph() without thinking too much about what it's doing
New on CRAN: coxstream (0.1.0). View at https://CRAN.R-project.org/package=coxstream
coxstream: Memory-Efficient Cox Proportional Hazards via Streaming Newton-Raphson
Fits the Cox proportional hazards model using a single descending-order pass per Newton-Raphson iteration. Peak RAM is O(p^2) regardless of the number of rows, making it suitable for datasets that do not fit in memory. Produces identical coefficients to survival::coxph() with Efron tie correction.
CRAN.R-project.org
June 20, 2026 at 3:02 PM
New on CRAN: coxstream (0.1.0). View at https://CRAN.R-project.org/package=coxstream
lithium-ion batteries. Specifically, we utilize five advanced models: the Cox-type models (Cox, CoxPH, and CoxTime) and two machine-learning-based models (DeepHit and MTLR). These models address the challenges of accurate RUL estimation by [3/7 of https://arxiv.org/abs/2503.13558v1]
March 19, 2025 at 6:05 AM
lithium-ion batteries. Specifically, we utilize five advanced models: the Cox-type models (Cox, CoxPH, and CoxTime) and two machine-learning-based models (DeepHit and MTLR). These models address the challenges of accurate RUL estimation by [3/7 of https://arxiv.org/abs/2503.13558v1]
Let me know how it goes. We have used mgcv to analyze discrete outcome data from 10s of million people, a scenario that coxph just gives up.
Pet projects (when I get time):
1) overlay #MPI to allow cluster level workloads
2) Port these #openmp codebases to #PDL in Perl & coarrays in #fortran
Pet projects (when I get time):
1) overlay #MPI to allow cluster level workloads
2) Port these #openmp codebases to #PDL in Perl & coarrays in #fortran
November 9, 2024 at 5:16 PM
Es ist etwas komplizierter, da zeitabhängige Interaktionsterme in der CoxPH Analyse verwendet wurden:
www.thelancet.com/journals/ecl...
www.thelancet.com/journals/ecl...
November 15, 2023 at 2:24 AM
Es ist etwas komplizierter, da zeitabhängige Interaktionsterme in der CoxPH Analyse verwendet wurden:
www.thelancet.com/journals/ecl...
www.thelancet.com/journals/ecl...
simulated fine-mapping data sets. We also illustrate CoxPH-SuSiE on real data by fine-mapping asthma loci using data from UK Biobank. This fine-mapping identified 14 asthma risk SNPs in 8 asthma risk loci, among which 6 had strong evidence for being [4/5 of https://arxiv.org/abs/2506.06233v1]
June 9, 2025 at 6:19 AM
simulated fine-mapping data sets. We also illustrate CoxPH-SuSiE on real data by fine-mapping asthma loci using data from UK Biobank. This fine-mapping identified 14 asthma risk SNPs in 8 asthma risk loci, among which 6 had strong evidence for being [4/5 of https://arxiv.org/abs/2506.06233v1]
of samples. We accomplish this by extending the "Sum of Single Effects" (SuSiE) method to the Cox proportional hazards (CoxPH) model. We demonstrate the benefits of the new method, "CoxPH-SuSiE", over existing BVSR methods for TTE outcomes in [3/5 of https://arxiv.org/abs/2506.06233v1]
June 9, 2025 at 6:19 AM
of samples. We accomplish this by extending the "Sum of Single Effects" (SuSiE) method to the Cox proportional hazards (CoxPH) model. We demonstrate the benefits of the new method, "CoxPH-SuSiE", over existing BVSR methods for TTE outcomes in [3/5 of https://arxiv.org/abs/2506.06233v1]
regression and the CoxPH model: this allows us to adapt and extend the so-called spectral method for rank regression to survival analysis. Our approach is versatile, naturally generalizing to several CoxPH variants, including deep models. We [3/4 of https://arxiv.org/abs/2505.22641v1]
May 29, 2025 at 6:21 AM
regression and the CoxPH model: this allows us to adapt and extend the so-called spectral method for rank regression to survival analysis. Our approach is versatile, naturally generalizing to several CoxPH variants, including deep models. We [3/4 of https://arxiv.org/abs/2505.22641v1]
the literature. Despite its popularity, wide deployment, and numerous variants, scaling CoxPH to large datasets and deep architectures poses a challenge, especially in the high-dimensional regime. We identify a fundamental connection between rank [2/4 of https://arxiv.org/abs/2505.22641v1]
May 29, 2025 at 6:21 AM
the literature. Despite its popularity, wide deployment, and numerous variants, scaling CoxPH to large datasets and deep architectures poses a challenge, especially in the high-dimensional regime. We identify a fundamental connection between rank [2/4 of https://arxiv.org/abs/2505.22641v1]
arXiv:2505.22641v1 Announce Type: new
Abstract: Survival analysis is widely deployed in a diverse set of fields, including healthcare, business, ecology, etc. The Cox Proportional Hazard (CoxPH) model is a semi-parametric model often encountered in [1/4 of https://arxiv.org/abs/2505.22641v1]
Abstract: Survival analysis is widely deployed in a diverse set of fields, including healthcare, business, ecology, etc. The Cox Proportional Hazard (CoxPH) model is a semi-parametric model often encountered in [1/4 of https://arxiv.org/abs/2505.22641v1]
May 29, 2025 at 6:21 AM
arXiv:2505.22641v1 Announce Type: new
Abstract: Survival analysis is widely deployed in a diverse set of fields, including healthcare, business, ecology, etc. The Cox Proportional Hazard (CoxPH) model is a semi-parametric model often encountered in [1/4 of https://arxiv.org/abs/2505.22641v1]
Abstract: Survival analysis is widely deployed in a diverse set of fields, including healthcare, business, ecology, etc. The Cox Proportional Hazard (CoxPH) model is a semi-parametric model often encountered in [1/4 of https://arxiv.org/abs/2505.22641v1]