km_estimates function

Kaplan-Meier risk estimates for Net Reclassification Index analysis

km_estimates Kaplan-Meier risk estimates for Net Reclassification Index analysis for Cox Regression Models

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

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.

Returns

An object from which the following objects can be extracted:

• data dataset.
• prob_orig outcome risk probabilities at t_risk for reference model.
• prob_new outcome risk probabilities at t_risk for new model.
• time name of time variable.
• status name of status variable.
• cutoff cutoff value for survival probability.
• t_risk follow-up time used to calculate outcome (risk) probabilities.
• reclass_totals table with total reclassification numbers.
• reclass_cases table with reclassification numbers for cases.
• reclass_controls table with reclassification numbers for controls.
• totals totals of controls, cases, censored cases.
• km_est totals of cases calculated using Kaplan-Meiers risk estimates.
• nri_est reclassification measures.

Details

Follow-up for which cases and 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. (These are not shown by function 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)
  
  res_km <- km_estimates(data=lbpmicox1,
                      p0=p0,
                      p1=p1,
                      time = "Time",
                      status = "Status",
                      cutoff=0.45,
                      t_risk=80)

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.

Author(s)

Martijn Heymans, 2023