danielegiardiello / prediction_performance_survival

Code repository for the manuscript: 'Assessing performance in prediction models with survival outcomes: practical guidance for Cox proportional hazards models' (published in Annals of Internal Medicine)

Assessing performance and clinical usefulness in prediction models with survival outcomes: practical guidance for Cox proportional hazards models

R and SAS and (basic) Python code repository for the manuscript 'Assessing performance and clinical usefulness in prediction models with survival outcomes: practical guidance for Cox proportional hazards models' published in Annals of Internal Medicine.

Journal version of the manuscript is here.
A preprint version of the manuscript is here.

The repository contains the following code:

• Minimal and essential code to develop and validate a risk prediction model with survival outcomes when both development and validation data are available. People with basic or low statistical knowledge and basic R programming knowledge are encouraged to use these files. To reproduce the main results of the manuscript, this script is sufficient.
  More elaborated output can be found here. The corresponding .Rmd source code is here.
  Another nice R code was motivated by the paper of Royston & Altman is provided by Martijn Heymans here. Minimal and essential code is also available in Python here. More elaborated output in RMarkdown based on Python code is available here with the corresponding .Rmd source code here.

• Minimal and essential code to validate a risk prediction model in a external data when model equation of a developed risk prediction model is available. More elaborated output can be found here. The corresponding .Rmd source code is here.

• Extensive output and code to develop and validate a risk prediction model with a survival outcome. The .Rmd source code is here. People with advanced knowledge in statistics are encouraged to use these files.

External functions and figures are available in the corresponding subfolders.

SAS code is available here.

Usage

You can either download a zip file containing the directory, or you can clone it by using

git clone https://github.com/danielegiardiello/Prediction_performance_survival.git

In either case, you can then use the Prediction_performance_survival.Rproj file to open and Rstudio session in the directory you have just downloaded. You may then knit both rmarkdown files, or run them line-by-line.

The collaboration with STRATOS topic groups 6 and 8 was essential to provide this work.

Contributions

Name	Affiliation	Role
Daniele Giardiello	The Netherlands Cancer Institute (NL) Leiden University Medical Center (NL) EURAC research (IT)	Author/maintainer and creator of R and Python code
David McLernon	University of Aberdeen (UK)	Author of SAS code
Laure Wynants	Maastrict University (NL) KU Leuven (BE)	Review of minimal .R/.Rmd codes
Nan van Geloven	Leiden University Medical Center (NL)	Review of 02_.Rmd code
Maarten van Smeden	University Medical Centre Utrecht (NL)	Review of 03_.Rmd code
Edouard Bonneville	Leiden University Medical Center (NL)	Contributor
Terry Therneau	Mayo Clinic (US)	Contributor

prediction_performance_survival's Issues

internal validation - caclibration

Hello!

would it be possible to add the code for the internal validation for calibration.

Thank you!