• flexsurv

  • Referenced in 20 articles [sw15470]
  • package flexsurv: Flexible Parametric Survival and Multi-State Models. Flexible parametric models for time...
  • E-Surge

  • Referenced in 12 articles [sw11780]
  • with a version of a memory model where survival can be handled directly...
  • uniCox

  • Referenced in 16 articles [sw19131]
  • prediction in the Cox model. Univarate shrinkage prediction for survival analysis using...
  • pseudosurv

  • Referenced in 8 articles [sw29281]
  • been proposed for direct regression modeling of the survival function, the restricted mean and cumulative ... function “pseudosurv” compute pseudo-values for modeling the survival function based on the Kaplan–Meier...
  • mvis

  • Referenced in 11 articles [sw37353]
  • micombine fits a wide variety of regression models to a multiply imputed dataset, combining ... using Rubin’s rules, and supports survival analysis models (stcox and streg), categorical data models...
  • pec

  • Referenced in 5 articles [sw15816]
  • Prediction Error Curves for Risk Prediction Models in Survival Analysis ... Validation of risk predictions obtained from survival models and competing risk models based on censored...
  • mpr

  • Referenced in 4 articles [sw21870]
  • Multi-parameter regression survival modeling: an alternative to proportional hazards. It is standard practice ... covariates to enter a parametric model through a single distributional parameter of interest, for example ... scale parameter in many standard survival models. Indeed, the well-known proportional hazards model ... parameter regression” (MPR) modeling and explore its use in a survival analysis context. We find...
  • JMfit

  • Referenced in 4 articles [sw14766]
  • Joint models of longitudinal and survival data. Joint models for longitudinal and survival data ... been developed for fitting the joint model, no software packages are currently available for simultaneously ... longitudinal component and the survival component of the model separately as well as the contribution ... data to the fit of the survival model. To fulfill this need, we develop...
  • ipred

  • Referenced in 30 articles [sw08001]
  • Improved predictive models by indirect classification and bagging for classification, regression and survival problems...
  • streg

  • Referenced in 4 articles [sw37360]
  • Parametric frailty and shared frailty survival models. Frailty models are the survival data analog...
  • brms

  • Referenced in 32 articles [sw19099]
  • Stan. Fit Bayesian generalized (non-)linear multilevel models using Stan for full Bayesian inference ... Poisson, survival, response times, ordinal, zero-inflated, hurdle, and even non-linear models...
  • RcmdrPlugin.survival

  • Referenced in 4 articles [sw24620]
  • survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves...
  • psbcGroup

  • Referenced in 3 articles [sw24007]
  • psbcGroup: Penalized Parametric and Semiparametric Bayesian Survival Models with Shrinkage and Grouping Priors. Algorithms ... fitting penalized parametric and semiparametric Bayesian survival models with shrinkage and grouping priors...
  • SVRc

  • Referenced in 5 articles [sw40646]
  • analysis. A crucial challenge in predictive modeling for survival analysis is managing censored observations ... hazards model is the standard tool for the analysis of continuous censored survival data ... data (SVRc) for improved analysis of medical survival data. SVRc leverages the high-dimensional capabilities ... lung cancer. Compared with the traditional Cox model, SVRc achieves significant improvement in overall accuracy...
  • p3state.msm

  • Referenced in 12 articles [sw04377]
  • Analyzing survival data , Analyzing survival data from illness-death model...
  • stpm2

  • Referenced in 2 articles [sw40187]
  • Stata module to estimate flexible parametric survival models. stpm2 fits flexible parametric survival models (Royston ... Parmar models). stpm2 can be used with single- or multiple-record ... single- or multiple-failure st data. Survival models can be fitted on the log cumulative ... addition, stpm2 can fit relative survival models by use of the bhazard() option. Post-estimation...
  • SmoothHazard

  • Referenced in 4 articles [sw14710]
  • package for fitting illness-death (and survival) model with possibly interval-censored data for transition...
  • survxai

  • Referenced in 2 articles [sw28940]
  • Visualization of the Local and Global Survival Model Explanations. Survival models may have very different ... creating a unified representation of a survival models, which can be further processed by various ... survival explainers. Tools implemented in ’survxai’ help to understand how input variables are used ... model and what impact do they have on the final model prediction. Currently, four explanation...
  • Tick

  • Referenced in 6 articles [sw26586]
  • processes, and tools for generalized linear models and survival analysis. The core of the library...
  • GBMCI

  • Referenced in 2 articles [sw11476]
  • direct optimization of concordance index. Survival analysis focuses on modeling and predicting the time ... hazard. Here we propose a nonparametric model for survival analysis that does not explicitly assume ... most widely used metrics in survival model performance evaluation. We implemented our model ... model against other popular survival models with a large-scale breast cancer prognosis dataset...