Description
Prediction Model Validation and Updating.
Description
Evaluate the predictive performance of an existing (i.e. previously developed) prediction/ prognostic model given relevant information about the existing prediction model (e.g. coefficients) and a new dataset. Provides a range of model updating methods that help tailor the existing model to the new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques to aggregate multiple existing prediction models on the new data are also provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. (2018) <doi:10.1002/sim.7586>).
README.md
predRupdate 
The goal of predRupdate is to provide a suite of functions for validating a existing (i.e. previously developed) prediction/ prognostic model, and for applying model updating methods to said model, according to an available dataset.
Installation
The package can be installed from CRAN as follows:
install.packages("predRupdate")
Development version
You can install the development version of predRupdate from GitHub with::
# install.packages("devtools")
devtools::install_github("GlenMartin31/predRupdate")
Example
One main use of this package is to externally validate an existing (previously developed) prediction model. This can be achieved with the following code:
# create a data.frame of the model coefficients, with columns being variables
coefs_table <- data.frame("Intercept" = -3.4,
"SexM" = 0.306,
"Smoking_Status" = 0.628,
"Diabetes" = 0.499,
"Creatinine" = 0.538)
#pass this into pred_input_info()
Existing_Logistic_Model <- pred_input_info(model_type = "logistic",
model_info = coefs_table)
summary(Existing_Logistic_Model)
#validate this model against an available dataset
pred_validate(x = Existing_Logistic_Model,
new_data = SYNPM$ValidationData,
binary_outcome = "Y")
Getting help
If you encounter a bug, please file an issue with a minimal reproducible example on GitHub.