Estimates the casewise concordance based on concordance and marginal estimates
derived from prodlim objects. Unlike test_casewise, this function
does not perform hypothesis testing but focuses on estimation and plotting.
Value
An object of class "casewise" containing:
- casewise
Matrix with time, casewise concordance, and standard errors.
- marg
Matrix with time, marginal CIF, and standard errors.
- concordance
Matrix with time, concordance, and standard errors.
- timer
Time points used.
- P1, se.P1
Extracted concordance values and SEs.
Examples
## Reduce Ex.Timings
library(prodlim)
data(prt);
prt <- force_same_cens(prt,cause="status")
### marginal cumulative incidence of prostate cancer
outm <- prodlim(Hist(time,status)~+1,data=prt)
times <- 60:100
cifmz <- predict(outm,cause=2,time=times,newdata=data.frame(zyg="MZ"))
cifdz <- predict(outm,cause=2,time=times,newdata=data.frame(zyg="DZ"))
### concordance for MZ and DZ twins
cc <- bicomprisk(Event(time,status)~strata(zyg)+id(id),data=prt,cause=c(2,2),prodlim=TRUE)
#> Strata 'DZ'
#> Strata 'MZ'
cdz <- cc$model$"DZ"
cmz <- cc$model$"MZ"
cdz <- casewise(cdz,outm,cause.marg=2)
cmz <- casewise(cmz,outm,cause.marg=2)
plot(cmz,ci=NULL,ylim=c(0,0.5),xlim=c(60,100),legend=TRUE,col=c(3,2,1))
par(new=TRUE)
plot(cdz,ci=NULL,ylim=c(0,0.5),xlim=c(60,100),legend=TRUE)
summary(cdz)
#> Casewise concordance and standard errors
#> time casewise conc se casewise
#> [1,] 59.5 0.0866 0.0865
#> [2,] 60.5 0.0659 0.0659
#> [3,] 61.6 0.0593 0.0593
#> [4,] 62.7 0.0483 0.0483
#> [5,] 63.7 0.0358 0.0358
#> [6,] 64.8 0.0279 0.0279
#> [7,] 65.8 0.0223 0.0223
#> [8,] 66.9 0.0197 0.0197
#> [9,] 68.0 0.0415 0.0297
#> [10,] 69.0 0.0335 0.0240
#> [11,] 70.1 0.0452 0.0264
#> [12,] 71.1 0.0855 0.0352
#> [13,] 72.2 0.0728 0.0300
#> [14,] 73.2 0.0888 0.0317
#> [15,] 74.3 0.1010 0.0321
#> [16,] 75.4 0.1020 0.0310
#> [17,] 76.4 0.1130 0.0318
#> [18,] 77.5 0.1230 0.0320
#> [19,] 78.5 0.1400 0.0334
#> [20,] 79.6 0.1470 0.0332
#> [21,] 80.7 0.1530 0.0329
#> [22,] 81.7 0.1460 0.0307
#> [23,] 82.8 0.1470 0.0298
#> [24,] 83.8 0.1600 0.0307
#> [25,] 84.9 0.1470 0.0282
#> [26,] 86.0 0.1620 0.0297
#> [27,] 87.0 0.1680 0.0300
#> [28,] 88.1 0.1820 0.0311
#> [29,] 89.1 0.1760 0.0301
#> [30,] 90.2 0.1950 0.0323
#> [31,] 91.2 0.2040 0.0332
#> [32,] 92.3 0.1970 0.0321
#> [33,] 93.4 0.1940 0.0315
#> [34,] 94.4 0.1970 0.0318
#> [35,] 95.5 0.1940 0.0314
#> [36,] 96.5 0.1930 0.0312
#> [37,] 97.6 0.2040 0.0330
#> [38,] 98.7 0.2010 0.0325
#> [39,] 99.7 0.1990 0.0322
#> [40,] 101.0 0.1980 0.0321
#> [41,] 102.0 0.1950 0.0316
#> [42,] 103.0 0.1940 0.0314
#> [43,] 104.0 0.1940 0.0314
#> [44,] 105.0 0.1930 0.0312
#> [45,] 106.0 0.1920 0.0311
#> [46,] 107.0 0.1920 0.0311
#> [47,] 108.0 0.1910 NaN
#>
#>
summary(cmz)
#> Casewise concordance and standard errors
#> time casewise conc se casewise
#> [1,] 60.6 0.519 0.2590
#> [2,] 61.6 0.466 0.2330
#> [3,] 62.7 0.380 0.1900
#> [4,] 63.7 0.285 0.1420
#> [5,] 64.8 0.286 0.1280
#> [6,] 65.8 0.228 0.1020
#> [7,] 66.9 0.295 0.1120
#> [8,] 67.9 0.306 0.1090
#> [9,] 68.9 0.327 0.1040
#> [10,] 70.0 0.338 0.0981
#> [11,] 71.0 0.345 0.0926
#> [12,] 72.1 0.399 0.0946
#> [13,] 73.1 0.414 0.0909
#> [14,] 74.2 0.426 0.0874
#> [15,] 75.2 0.388 0.0798
#> [16,] 76.3 0.391 0.0773
#> [17,] 77.3 0.410 0.0769
#> [18,] 78.4 0.392 0.0723
#> [19,] 79.4 0.410 0.0721
#> [20,] 80.5 0.423 0.0714
#> [21,] 81.5 0.400 0.0666
#> [22,] 82.6 0.442 0.0685
#> [23,] 83.6 0.446 0.0676
#> [24,] 84.7 0.433 0.0643
#> [25,] 85.7 0.413 0.0612
#> [26,] 86.8 0.389 0.0578
#> [27,] 87.8 0.396 0.0578
#> [28,] 88.9 0.396 0.0573
#> [29,] 89.9 0.399 0.0574
#> [30,] 91.0 0.386 0.0556
#> [31,] 92.0 0.400 0.0570
#> [32,] 93.1 0.393 0.0560
#> [33,] 94.1 0.415 0.0590
#> [34,] 95.2 0.477 0.0669
#> [35,] 96.2 0.493 0.0690
#> [36,] 97.3 0.511 0.0714
#> [37,] 98.3 0.507 0.0708
#> [38,] 99.4 0.500 0.0699
#> [39,] 100.0 0.525 0.0739
#> [40,] 101.0 0.520 0.0731
#> [41,] 103.0 0.514 0.0723
#> [42,] 104.0 0.541 0.0767
#> [43,] 105.0 0.541 0.0767
#>
#>