In mwheymans/psfmi: Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

psfmi

The package provides functions to apply pooling, backward and forward selection of linear, logistic and Cox regression models across multiply imputed data sets using Rubin's Rules (RR). The D1, D2, D3, D4 and the median p-values method can be used to pool the significance of categorical variables (multiparameter test). The model can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of variables. Variables can also be forced in the model during selection.

Validation of the prediction models can be performed with cross-validation or bootstrapping across multiply imputed data sets and pooled model performance measures as AUC value, Reclassification, R-square, Hosmer and Lemeshow test, scaled Brier score and calibration plots are generated. Also a function to externally validate logistic prediction models across multiple imputed data sets is available and a function to compare models in multiply imputed data.

Installation

You can install the released version of psfmi with:

install.packages("psfmi")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("mwheymans/psfmi")

Citation

Cite the package as:

Martijn W Heymans (2021). psfmi: Prediction Model Pooling, Selection and Performance Evaluation 
Across Multiply Imputed Datasets. R package version 1.1.0. https://mwheymans.github.io/psfmi/ 

Examples

This example shows you how to pool a logistic regression model across 5 multiply imputed datasets and that includes two restricted cubic spline variables and a categorical, continuous and dichotomous variable. The pooling method that is used is method D1.

library(psfmi)

pool_lr <- psfmi_lr(data=lbpmilr, formula = Chronic ~ rcs(Pain, 3) + 
                      JobDemands + rcs(Tampascale, 3) + factor(Satisfaction) + 
                      Smoking, nimp=5, impvar="Impnr", method="D1")

pool_lr$RR_model

pool_lr$multiparm

This example shows you how to apply forward selection of the above model using a p-value of 0.05.

library(psfmi)

pool_lr <- psfmi_lr(data=lbpmilr, formula = Chronic ~ rcs(Pain, 3) + 
                      JobDemands + rcs(Tampascale, 3) + factor(Satisfaction) + 
                      Smoking, p.crit = 0.05, direction="FW", 
                      nimp=5, impvar="Impnr", method="D1")

pool_lr$RR_model_final

pool_lr$multiparm

More examples for logistic, linear and Cox regression models as well as internal and external validation of prediction models can be found on the package website or in the online book Applied Missing Data Analysis.

mwheymans/psfmi documentation built on June 30, 2023, 5:25 a.m.