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predieval (version 0.1.1)

Assessing Performance of Prediction Models for Predicting Patient-Level Treatment Benefit

Description

Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

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install.packages('predieval')

Monthly Downloads

152

Version

0.1.1

License

GPL (>= 2)

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Maintainer

Orestis Efthimiou

Last Published

April 19th, 2022

Functions in predieval (0.1.1)

simbinary

Simulate data for a binary outcome
bencalibr

Plotting calibration for benefit of a prediction model
datbinary

Simulated dataset, binary outcome
logit

Logit
predieval

Calculating measures for calibration for benefit for a prediction model
expit

Expit
datcont

Simulated dataset, continuous outcome
simcont

Simulate data for a prediction model of a continuous outcome