psfmi (version 1.4.0)

nri_est: Calculation of Net Reclassification Index measures

Description

nri_est Calculation of proportion of Reclassified persons and NRI for Cox Regression Models

Usage

nri_est(data, p0, p1, time, status, t_risk, cutoff)

Value

An object from which the following objects can be extracted:

• prop_up_case proportion of cases reclassified upwards.

• prop_down_case proportion of cases reclassified downwards.

• prop_up_ctr proportion of controls reclassified upwards.

• prop_down_ctr proportion of controls reclassified downwards.

• nri_plus proportion reclassified for events.

• nri_min proportion reclassified for nonevents.

• nri net reclassification improvement.

Arguments

data

Data frame with relevant predictors

p0

risk outcome probabilities for reference model.

p1

risk outcome probabilities for new model.

time

Character vector. Name of time variable.

status

Character vector. Name of status variable.

t_risk

Follow-up value to calculate cases, controls. See details.

cutoff

A numerical vector that defines the outcome probability cutoff values.

Author

Martijn Heymans, 2023

Details

Follow-up for which cases nd controls are determined. For censored cases before this follow-up the expected risk of being a case is calculated by using the Kaplan-Meier value to calculate the expected number of cases. These expected numbers are used to calculate the NRI proportions but are not shown by function nricens.

References

Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150(11):795-802.

Steyerberg EW, Pencina MJ. Reclassification calculations for persons with incomplete follow-up. Ann Intern Med. 2010;152(3):195-6; author reply 196-7.

Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11-21

Inoue E (2018). nricens: NRI for Risk Prediction Models with Time to Event and Binary Response Data. R package version 1.6, <https://CRAN.R-project.org/package=nricens>.

Examples

  library(survival)
  lbpmicox1 <- subset(psfmi::lbpmicox, Impnr==1) # extract dataset
  
  fit_cox0 <- 
    coxph(Surv(Time, Status) ~ Duration + Pain, data=lbpmicox1, x=TRUE)
  fit_cox1 <- 
    coxph(Surv(Time, Status) ~ Duration + Pain + Function + Radiation, 
    data=lbpmicox1, x=TRUE)

  p0 <- risk_coxph(fit_cox0, t_risk=80)
  p1 <- risk_coxph(fit_cox1, t_risk=80)
  
  nri <- nri_est(data=lbpmicox1,
                      p0=p0,
                      p1=p1,
                      time = "Time",
                      status = "Status",
                      cutoff=0.45,
                      t_risk=80)