Skip to content

A simulation study looking at which combinations of missing data handling methods across a prediction model's pipeline are compatible, and which ones lead to bias.

Notifications You must be signed in to change notification settings

antoniatsv/R-Compatibility-Sim-Study

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

88 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Compatibility of Missing Data Handling Methods across the Clinical Prediction Model Pipeline

Repo for the paper entitled "Compatibility of Missing Data Handling Methods across the Clinical Prediction Model Pipeline", which is currently in-preparation.

This paper investigates the impact of using different missing data imputation methods on estimated predictive performance of a prediction model across development, validation and deployment of a CPM. The paper aims to determine which combinations of imputation methods are compatible across the prediction model pipeline.

The repo contains the coding scripts and results from the simulation and empirical study, described in the paper as follows:

Code sub-folder

This contains the R scripts used in the simulation study. Additionally, this folder also contains the R script used to analyse the NCORR data, which was used as part of the empirical study described in the paper. Much of the simulation code was run on the computational shared facility (CSF) at the University of Manchester.

Outputs sub-folder

This contains a .RDS file of the results of the simulation (summarised) and an .RDS file of the summarised NCORR results.

About

A simulation study looking at which combinations of missing data handling methods across a prediction model's pipeline are compatible, and which ones lead to bias.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 2

  •  
  •