reclass: Retrospective risk reclassification table

Description

Retrospective table of risks predicted by two different methods, models, algorithms

Usage

reclass(object, reference, formula, data, time, cause, cuts = seq(0, 100, 25),
  digits = 2)

Arguments

object

Either a list with two elements. Each element should either be a vector with probabilities, or an object for which predictSurvProb or predictEventProb can extract predicted risk based on data.

reference

Reference prediction model.

formula

A survival formula as obtained either with prodlim::Hist or survival::Surv which defines the response in the data.

data

Used to extract the response from the data and passed on to predictEventProb to extract predicted event probabilities.

time

Time interest for prediction.

cause

For competing risk models the cause of interest. Defaults to all available causes.

cuts

Risk quantiles to group risks.

digits

Number of digits to show for the predicted risks

Value

reclassification tables: overall table and one conditional table for each cause and for subjects event free at time interest.

Details

All risks are multiplied by 100 before

Examples

Run this code

# NOT RUN {
library(survival)
set.seed(40)
d <- prodlim::SimSurv(400)
nd <- prodlim::SimSurv(400)
Models <- list("Cox.X2"=coxph(Surv(time,status)~X2,data=d,x=TRUE,y=TRUE),
               "Cox.X1.X2"=coxph(Surv(time,status)~X1+X2,data=d,x=TRUE,y=TRUE))
rc <- reclass(Models,formula=Surv(time,status)~1,data=nd,time=5)
print(rc)
plot(rc)

set.seed(40)
library(riskRegression)
library(prodlim)
dcr <- prodlim::SimCompRisk(400)
ndcr <- prodlim::SimCompRisk(400)
crPred5 <- list("X2"=predictEventProb(CSC(Hist(time,event)~X2,data=dcr),newdata=ndcr,times=5),
                "X1+X2"=predictEventProb(CSC(Hist(time,event)~X1+X2,data=dcr),newdata=ndcr,times=5))
rc <- reclass(crPred5,Hist(time,event)~1,data=ndcr,time=3)
print(rc)

reclass(crPred5,Hist(time,event)~1,data=ndcr,time=5,cuts=100*c(0,0.05,0.1,0.2,1))
# }