dynpred

swMATH35364CRANdynpredMaRDI QIDQ47073

Companion Package to "Dynamic Prediction in Clinical Survival Analysis"

Hein Putter

Last update: 13 July 2015

Copyright license: GNU General Public License, version 3.0, GNU General Public License, version 2.0

Software version identifier: 0.1.2

Source code repository: https://github.com/cran/dynpred

Related Items (19)

Penalised logistic regression and dynamic prediction for discrete-time recurrent event data ⋮ PLS-Frailty Model for Cancer Survival Analysis Based on Gene Expression Profiles ⋮ Estimation of incident dynamic AUC in practice ⋮ Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time‐to‐event in presence of censoring and competing risks ⋮ Dynamic Bayesian adjustment of anticipatory covariates in retrospective data: application to the effect of education on divorce risk ⋮ Dynamic hazards modelling for predictive longevity risk assessment ⋮ Prediction of transplant-free survival in idiopathic pulmonary fibrosis patients using joint models for event times and mixed multivariate longitudinal data ⋮ Bayesian variable selection for survival data using inverse moment priors ⋮ The Kaplan-Meier Integral in the Presence of Covariates: A Review ⋮ Restricted mean survival time estimation: nonparametric and regression methods ⋮ Efficient and Robust Estimation of τ-year Risk Prediction Models Leveraging Time Varying Intermediate Outcomes ⋮ Dynamic Pseudo‐Observations: A Robust Approach to Dynamic Prediction in Competing Risks ⋮ Survival models and health sequences ⋮ Commentary to: ``Survival models and health sequences ⋮ Modeling potential time to event data with competing risks ⋮ Comparison of stopped Cox regression with direct methods such as pseudo-values and binomial regression ⋮ Cox regression models with functional covariates for survival data ⋮ Assessing the numerical integration of dynamic prediction formulas using the exact expressions under the joint frailty-copula model ⋮ Continuous and discrete-time survival prediction with neural networks

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