Extended Joint Models for Longitudinal and Survival Data
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Updated
May 26, 2024 - R
Extended Joint Models for Longitudinal and Survival Data
Code repository for the manuscript 'Validation of the performance of competing risks prediction models: a guide through modern methods' (published in BMJ)
Targeted Learning for Survival Analysis
R package for fitting joint models to time-to-event and longitudinal data
Code and results of Section 4 of the paper "Fine-Gray subdistribution hazard models to simultaneously estimate the absolute risk of different event types: cumulative total failure probability may exceed 1", by Peter Austin, Ewout Steyerberg & Hein Putter
Code and supplementary materials for the manuscript "Multiple imputation for cause-specific Cox models: assessing methods for estimation and prediction" (2022, Statistical Methods in Medical Research)
Code accompanying the manuscript "Why you should avoid using multiple Fine–Gray models: insights from (attempts at) simulating proportional subdistribution hazards data" (under review)
This repository includes the R code and data used in the analysis for the main manuscript of the paper. The cleaned data file consists of the data for only the 104 incident infections used. The ped.RDS file is an R data file for the piece-wise exponential data formulation of the survival data for the competing risks analysis. The descriptives fi…
A Time-Dependent Structural Model Between Latent Classes and Competing Risks Outcomes
Code/Data/Figure for "Facility profiling under competing risks using multivariate prognostic scores weighting"
Tests for trends in vaccine efficacy by genetic distance
Mixed proportional hazard competing risk model with the NPMLE
Code repository for the manuscript 'Multiple imputation of missing covariates when using the Fine–Gray model' (submitted)
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