stdRecal: Standard Logistic Recalibration

In ClinicalUtilityRecal: Recalibration Methods for Improved Clinical Utility of Risk Scores

Description

Produces recalibration intercept, slope and corresponding recalibrated risk scores using standard logistic rexalibration. Recalibration slope and intercept using logistic recalibration method, develop by Cox (1958), then used to calculated the recalibrated risk score

Usage

stdRecal(y,p)

Arguments

y	Vector of binary outcomes, with 1 indicating event (case) and 0 indicating no event (controls)
p	Vector of risk score values

Value

stdRisk	Vector of recalibrated risks under standard logistic recalibration
alpha	Two-element vector containing logistic recalibration intercept and slope

Author(s)

Anu Mishra

References

Cox, D. R. (1958). Two further applications of a model for binary regression. Biometrika, 45(3/4), 562-565.

Harrell Jr, F. E. (2015). Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer.

Examples

#load data
data(fakeData)

res <- stdRecal(p=fakeData$p, y =fakeData$y)
p.std <- res$stdRecal
coef.dt <- res$alpha

Example output

ClinicalUtilityRecal documentation built on April 17, 2020, 1:26 a.m.