README.md
In raredd/cmprsk2: cmprsk2
cmprsk2
Extensions for the cmprsk package.
See intro vignette
To install:
# install.packages('devtools')
devtools::install_github('raredd/cmprsk2', build_vignettes = TRUE)
crr formula method
## model deaths with ltx and withdraw as competing events
cr1 <- crr2(Surv(futime, event(censored) == death) ~ age + sex + abo,
data = transplant)
## include an all-cause death overall survival model
cr2 <- crr2(Surv(futime, event(censored) == death) ~ age + sex + abo,
data = transplant,
cox = Surv(futime, event == 'death') ~ age + sex + abo)
crr2 summary methods
summary(cr1)
## $`CRR: death`
## HR L95 U95 p
## age 1.02 1.00 1.04 0.09
## sexf 0.65 0.40 1.07 0.09
## aboB 1.48 0.70 3.12 0.31
## aboAB 1.14 0.34 3.87 0.83
## aboO 1.48 0.85 2.56 0.17
##
## $`CRR: ltx`
## HR L95 U95 p
## age 0.99 0.99 1.00 0.15
## sexf 1.09 0.93 1.29 0.28
## aboB 0.70 0.54 0.91 0.01
## aboAB 0.91 0.59 1.39 0.65
## aboO 0.59 0.49 0.70 0.00
##
## $`CRR: withdraw`
## HR L95 U95 p
## age 0.98 0.95 1.02 0.28
## sexf 1.42 0.75 2.68 0.28
## aboB 2.35 0.82 6.74 0.11
## aboAB 3.04 0.81 11.43 0.10
## aboO 2.37 1.04 5.41 0.04
library('htmlTable')
summary(
cr2,
html = TRUE, n = TRUE, ref = TRUE,
htmlArgs = list(
caption = 'CRR models.',
rgroup = c('Age', 'Sex', 'Blood type'),
rnames = c('+1 year change', 'Female', 'B', 'AB', 'O'),
css.cell = 'white-space: nowrap; padding: 0px 5px 0px;'
)
)
CRR models.
Cox PH
CRR: death
CRR: ltx
CRR: withdraw
Totaln = 797 (%)
Eventsn = 66 (8)
HR (95% CI)
p
Eventsn = 66 (8)
HR (95% CI)
p
Eventsn = 618 (78)
HR (95% CI)
p
Eventsn = 37 (5)
HR (95% CI)
p
Age
+1 year change
797 (100)
66 (100)
1.02 (0.99, 1.04)
0.16
66 (100)
1.02 (1.00, 1.04)
0.090
618 (100)
0.99 (0.99, 1.00)
0.15
37 (100)
0.98 (0.95, 1.02)
0.28
Sex
Female
438 (55)
42 (64)
42 (64)
332 (54)
17 (46)
B
359 (45)
24 (36)
0.69 (0.42, 1.14)
0.15
24 (36)
0.65 (0.40, 1.07)
0.093
286 (46)
1.09 (0.93, 1.29)
0.28
20 (54)
1.42 (0.75, 2.68)
0.28
Blood type
AB
317 (40)
21 (32)
21 (32)
261 (42)
8 (22)
O
102 (13)
10 (15)
1.15 (0.54, 2.45)
0.72
10 (15)
1.48 (0.70, 3.12)
0.31
77 (12)
0.70 (0.54, 0.91)
0.008
6 (16)
2.35 (0.82, 6.74)
0.11
NA
40 (5)
3 (5)
1.18 (0.35, 3.96)
0.79
3 (5)
1.14 (0.34, 3.87)
0.83
32 (5)
0.91 (0.59, 1.39)
0.65
3 (8)
3.04 (0.81, 11.43)
0.10
NA
338 (42)
32 (48)
0.99 (0.57, 1.73)
0.98
32 (48)
1.48 (0.85, 2.56)
0.17
248 (40)
0.59 (0.49, 0.70)
< 0.001
20 (54)
2.37 (1.04, 5.41)
0.040
cuminc formula method
## can use same formula as crr2
ci1 <- cuminc2(Surv(futime, event(censored) == death) ~ abo,
data = transplant)
## but event indicator is not required
ci2 <- cuminc2(Surv(futime, event(censored)) ~ sex,
data = transplant)
cuminc2 summary methods
summary(ci1)
## $est
## 0 500 1000 1500 2000
## A death 0.00000 0.06191 0.06191 NA NA
## B death 0.00000 0.08799 0.08799 0.12717 0.12717
## AB death 0.00000 0.07317 NA NA NA
## O death 0.00289 0.09106 0.09598 0.09598 NA
## A ltx 0.00000 0.81022 0.84174 NA NA
## B ltx 0.00000 0.71905 0.77478 0.77478 0.77478
## AB ltx 0.02439 0.80488 NA NA NA
## O ltx 0.00000 0.69130 0.76563 0.78129 NA
## A withdraw 0.00000 0.02481 0.02481 NA NA
## B withdraw 0.00000 0.05886 0.05886 0.05886 0.05886
## AB withdraw 0.00000 0.07317 NA NA NA
## O withdraw 0.00000 0.04413 0.06470 0.06470 NA
##
## $var
## 0 500 1000 1500 2000
## A death 0.00000 0.00018 0.00018 NA NA
## B death 0.00000 0.00081 0.00081 0.00259 0.00259
## AB death 0.00000 0.00192 NA NA NA
## O death 0.00001 0.00024 0.00027 0.00027 NA
## A ltx 0.00000 0.00048 0.00046 NA NA
## B ltx 0.00000 0.00202 0.00233 0.00233 0.00233
## AB ltx 0.00059 0.00426 NA NA NA
## O ltx 0.00000 0.00064 0.00058 0.00062 NA
## A withdraw 0.00000 0.00008 0.00008 NA NA
## B withdraw 0.00000 0.00056 0.00056 0.00056 0.00056
## AB withdraw 0.00000 0.00177 NA NA NA
## O withdraw 0.00000 0.00012 0.00020 0.00020 NA
##
## $events
## 0 500 1000 1500 2000
## A death 0 20 20 21 21
## B death 0 9 9 10 10
## AB death 0 3 3 3 3
## O death 1 31 32 32 32
## A ltx 0 262 269 269 269
## B ltx 0 74 77 77 77
## AB ltx 1 33 33 33 33
## O ltx 0 234 254 256 256
## A withdraw 0 8 8 8 8
## B withdraw 0 6 6 6 6
## AB withdraw 0 3 3 3 3
## O withdraw 0 15 20 20 20
##
## $total_events
## A death B death AB death O death A ltx B ltx AB ltx O ltx
## 21 10 3 32 269 78 33 256
## A withdraw B withdraw AB withdraw O withdraw
## 8 6 3 20
##
## $total_groups
## A B AB O
## 325 103 41 346
##
## $total_atrisk
## 0 500 1000 1500 2000
## 811 94 20 2 1
summary(ci1, times = 0:10 * 100)$est
## 0 100 200 300 400 500 600 700 800 900 1000
## A death 0.00000 0.03390 0.04317 0.05562 0.06191 0.06191 0.06191 0.06191 0.06191 0.06191 0.06191
## B death 0.00000 0.04854 0.06796 0.07767 0.08799 0.08799 0.08799 0.08799 0.08799 0.08799 0.08799
## AB death 0.00000 0.04878 0.04878 0.04878 0.04878 0.07317 NA NA NA NA NA
## O death 0.00289 0.05250 0.07597 0.08185 0.08779 0.09106 0.09106 0.09106 0.09106 0.09106 0.09598
## A ltx 0.00000 0.48743 0.72831 0.77824 0.80346 0.81022 0.83623 0.84174 0.84174 0.84174 0.84174
## B ltx 0.00000 0.33010 0.50485 0.69903 0.71905 0.71905 0.74866 0.74866 0.74866 0.77478 0.77478
## AB ltx 0.02439 0.56098 0.60976 0.73171 0.80488 0.80488 NA NA NA NA NA
## O ltx 0.00000 0.23354 0.43015 0.54777 0.64341 0.69130 0.73601 0.75159 0.75580 0.76071 0.76563
## A withdraw 0.00000 0.00926 0.01853 0.02167 0.02481 0.02481 0.02481 0.02481 0.02481 0.02481 0.02481
## B withdraw 0.00000 0.02913 0.03883 0.04854 0.05886 0.05886 0.05886 0.05886 0.05886 0.05886 0.05886
## AB withdraw 0.00000 0.02439 0.07317 0.07317 0.07317 0.07317 NA NA NA NA NA
## O withdraw 0.00000 0.01167 0.02927 0.03810 0.04413 0.04413 0.04413 0.06470 0.06470 0.06470 0.06470
cuminc plotting methods
par(mfrow = c(2, 2))
# ciplot(ci2)
plot(ci2, add = TRUE) ## equivalently
plot(ci2, split = 'event', add = TRUE, wh.events = 'est')
## convenience wrapper
par(mfrow = c(2, 2))
ciplot_by(
rhs = 'sex', time = 'futime', event = 'event',
data = transplant, by = 'abo', xlim = c(0, 1500),
events = FALSE, single = FALSE, events.total = 2100
)
extras
## pairwise gray tests
cuminc_pairs(ci1)$p.value
## $death
## A B AB O
## A NA 1.000 1.000 1
## B 0.391 NA 1.000 1
## AB 0.788 0.776 NA 1
## O 0.201 0.877 0.712 NA
##
## $ltx
## A B AB O
## A NA 0.035 0.805 0.000
## B 0.007 NA 0.442 0.442
## AB 0.805 0.162 NA 0.051
## O 0.000 0.147 0.013 NA
##
## $withdraw
## A B AB O
## A NA 0.446 0.446 0.253
## B 0.097 NA 1.000 1.000
## AB 0.089 0.753 NA 1.000
## O 0.042 0.885 0.402 NA
timepoints2(
ci2, html = TRUE,
htmlArgs = list(
caption = 'cuminc estimates at specific time points (<code>cuminc::timepoints</code>).'
)
)
cuminc estimates at specific time points (cuminc::timepoints).
0
500
1000
1500
2000
m death
0.002
0.091
0.097
0.104
0.104
f death
0.000
0.063
0.063
-
-
m ltx
0.000
0.738
0.786
0.800
0.800
f ltx
0.003
0.761
0.822
-
-
m withdraw
0.000
0.030
0.045
0.045
0.045
f withdraw
0.000
0.052
0.056
-
-
AIC(cr1$`CRR: death`)
## AIC
## 867.6161
sapply(cr1, BIC)
## CRR: death.BIC CRR: ltx.BIC CRR: withdraw.BIC
## 878.5644 7494.9446 496.6589
logLik(cr1$`CRR: death`)
## 'log Lik.' -428.8081 (df=5)
deviance(cr1$`CRR: death`)
## Call:
## crr(transplant[, "futime"], transplant[, "event"], cov1 = model.matrix(~age +
## sex + abo, transplant)[, -1L, drop = FALSE], cencode = "censored",
## failcode = "death", variance = TRUE, cengroup = rep(1L, nrow(transplant)),
## gtol = 1e-06, maxiter = 10, init = c(0L, 0L, 0L, 0L, 0L))
##
## Deviance = 7.15 on 5 df, 0.20984
crrFits(cr1$`CRR: death`)
## Model selection table
##
## 0: Null Model
##
## 1: Model 1 call:
## crr(transplant[, "futime"], transplant[, "event"], cov1 = model.matrix(~age +
## sex + abo, transplant)[, -1L, drop = FALSE], cencode = "censored",
## failcode = "death", variance = TRUE, cengroup = rep(1L, nrow(transplant)),
## gtol = 1e-06, maxiter = 10, init = c(0L, 0L, 0L, 0L, 0L))
##
##
## n loglik df k -2logLik -2logLik diff AIC AIC diff BIC BIC diff
## 0 797 -432.38 0 1 864.76 7.1484 864.76 0.0000 864.76 0.0
## 1 797 -428.81 5 6 857.62 0.0000 867.62 2.8516 878.56 13.8
crrwald.test(cr1$`CRR: death`)
## chi2 df P
## Overall 8.11151956 5 0.15019572
## age 2.86948310 1 0.09027386
## sexf 2.82220518 1 0.09296860
## aboB 1.04828004 1 0.30590354
## aboAB 0.04546327 1 0.83115446
## aboO 1.91025511 1 0.16693492
raredd/cmprsk2 documentation built on March 29, 2024, 5:34 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
cmprsk2
Extensions for the cmprsk package.
See intro vignette
To install:
# install.packages('devtools')
devtools::install_github('raredd/cmprsk2', build_vignettes = TRUE)
crr formula method
## model deaths with ltx and withdraw as competing events
cr1 <- crr2(Surv(futime, event(censored) == death) ~ age + sex + abo,
data = transplant)
## include an all-cause death overall survival model
cr2 <- crr2(Surv(futime, event(censored) == death) ~ age + sex + abo,
data = transplant,
cox = Surv(futime, event == 'death') ~ age + sex + abo)
crr2 summary methods
summary(cr1)
## $`CRR: death`
## HR L95 U95 p
## age 1.02 1.00 1.04 0.09
## sexf 0.65 0.40 1.07 0.09
## aboB 1.48 0.70 3.12 0.31
## aboAB 1.14 0.34 3.87 0.83
## aboO 1.48 0.85 2.56 0.17
##
## $`CRR: ltx`
## HR L95 U95 p
## age 0.99 0.99 1.00 0.15
## sexf 1.09 0.93 1.29 0.28
## aboB 0.70 0.54 0.91 0.01
## aboAB 0.91 0.59 1.39 0.65
## aboO 0.59 0.49 0.70 0.00
##
## $`CRR: withdraw`
## HR L95 U95 p
## age 0.98 0.95 1.02 0.28
## sexf 1.42 0.75 2.68 0.28
## aboB 2.35 0.82 6.74 0.11
## aboAB 3.04 0.81 11.43 0.10
## aboO 2.37 1.04 5.41 0.04
library('htmlTable')
summary(
cr2,
html = TRUE, n = TRUE, ref = TRUE,
htmlArgs = list(
caption = 'CRR models.',
rgroup = c('Age', 'Sex', 'Blood type'),
rnames = c('+1 year change', 'Female', 'B', 'AB', 'O'),
css.cell = 'white-space: nowrap; padding: 0px 5px 0px;'
)
)
cuminc formula method
## can use same formula as crr2
ci1 <- cuminc2(Surv(futime, event(censored) == death) ~ abo,
data = transplant)
## but event indicator is not required
ci2 <- cuminc2(Surv(futime, event(censored)) ~ sex,
data = transplant)
cuminc2 summary methods
summary(ci1)
## $est
## 0 500 1000 1500 2000
## A death 0.00000 0.06191 0.06191 NA NA
## B death 0.00000 0.08799 0.08799 0.12717 0.12717
## AB death 0.00000 0.07317 NA NA NA
## O death 0.00289 0.09106 0.09598 0.09598 NA
## A ltx 0.00000 0.81022 0.84174 NA NA
## B ltx 0.00000 0.71905 0.77478 0.77478 0.77478
## AB ltx 0.02439 0.80488 NA NA NA
## O ltx 0.00000 0.69130 0.76563 0.78129 NA
## A withdraw 0.00000 0.02481 0.02481 NA NA
## B withdraw 0.00000 0.05886 0.05886 0.05886 0.05886
## AB withdraw 0.00000 0.07317 NA NA NA
## O withdraw 0.00000 0.04413 0.06470 0.06470 NA
##
## $var
## 0 500 1000 1500 2000
## A death 0.00000 0.00018 0.00018 NA NA
## B death 0.00000 0.00081 0.00081 0.00259 0.00259
## AB death 0.00000 0.00192 NA NA NA
## O death 0.00001 0.00024 0.00027 0.00027 NA
## A ltx 0.00000 0.00048 0.00046 NA NA
## B ltx 0.00000 0.00202 0.00233 0.00233 0.00233
## AB ltx 0.00059 0.00426 NA NA NA
## O ltx 0.00000 0.00064 0.00058 0.00062 NA
## A withdraw 0.00000 0.00008 0.00008 NA NA
## B withdraw 0.00000 0.00056 0.00056 0.00056 0.00056
## AB withdraw 0.00000 0.00177 NA NA NA
## O withdraw 0.00000 0.00012 0.00020 0.00020 NA
##
## $events
## 0 500 1000 1500 2000
## A death 0 20 20 21 21
## B death 0 9 9 10 10
## AB death 0 3 3 3 3
## O death 1 31 32 32 32
## A ltx 0 262 269 269 269
## B ltx 0 74 77 77 77
## AB ltx 1 33 33 33 33
## O ltx 0 234 254 256 256
## A withdraw 0 8 8 8 8
## B withdraw 0 6 6 6 6
## AB withdraw 0 3 3 3 3
## O withdraw 0 15 20 20 20
##
## $total_events
## A death B death AB death O death A ltx B ltx AB ltx O ltx
## 21 10 3 32 269 78 33 256
## A withdraw B withdraw AB withdraw O withdraw
## 8 6 3 20
##
## $total_groups
## A B AB O
## 325 103 41 346
##
## $total_atrisk
## 0 500 1000 1500 2000
## 811 94 20 2 1
summary(ci1, times = 0:10 * 100)$est
## 0 100 200 300 400 500 600 700 800 900 1000
## A death 0.00000 0.03390 0.04317 0.05562 0.06191 0.06191 0.06191 0.06191 0.06191 0.06191 0.06191
## B death 0.00000 0.04854 0.06796 0.07767 0.08799 0.08799 0.08799 0.08799 0.08799 0.08799 0.08799
## AB death 0.00000 0.04878 0.04878 0.04878 0.04878 0.07317 NA NA NA NA NA
## O death 0.00289 0.05250 0.07597 0.08185 0.08779 0.09106 0.09106 0.09106 0.09106 0.09106 0.09598
## A ltx 0.00000 0.48743 0.72831 0.77824 0.80346 0.81022 0.83623 0.84174 0.84174 0.84174 0.84174
## B ltx 0.00000 0.33010 0.50485 0.69903 0.71905 0.71905 0.74866 0.74866 0.74866 0.77478 0.77478
## AB ltx 0.02439 0.56098 0.60976 0.73171 0.80488 0.80488 NA NA NA NA NA
## O ltx 0.00000 0.23354 0.43015 0.54777 0.64341 0.69130 0.73601 0.75159 0.75580 0.76071 0.76563
## A withdraw 0.00000 0.00926 0.01853 0.02167 0.02481 0.02481 0.02481 0.02481 0.02481 0.02481 0.02481
## B withdraw 0.00000 0.02913 0.03883 0.04854 0.05886 0.05886 0.05886 0.05886 0.05886 0.05886 0.05886
## AB withdraw 0.00000 0.02439 0.07317 0.07317 0.07317 0.07317 NA NA NA NA NA
## O withdraw 0.00000 0.01167 0.02927 0.03810 0.04413 0.04413 0.04413 0.06470 0.06470 0.06470 0.06470
cuminc plotting methods
par(mfrow = c(2, 2))
# ciplot(ci2)
plot(ci2, add = TRUE) ## equivalently
plot(ci2, split = 'event', add = TRUE, wh.events = 'est')
## convenience wrapper
par(mfrow = c(2, 2))
ciplot_by(
rhs = 'sex', time = 'futime', event = 'event',
data = transplant, by = 'abo', xlim = c(0, 1500),
events = FALSE, single = FALSE, events.total = 2100
)
extras
## pairwise gray tests
cuminc_pairs(ci1)$p.value
## $death
## A B AB O
## A NA 1.000 1.000 1
## B 0.391 NA 1.000 1
## AB 0.788 0.776 NA 1
## O 0.201 0.877 0.712 NA
##
## $ltx
## A B AB O
## A NA 0.035 0.805 0.000
## B 0.007 NA 0.442 0.442
## AB 0.805 0.162 NA 0.051
## O 0.000 0.147 0.013 NA
##
## $withdraw
## A B AB O
## A NA 0.446 0.446 0.253
## B 0.097 NA 1.000 1.000
## AB 0.089 0.753 NA 1.000
## O 0.042 0.885 0.402 NA
timepoints2(
ci2, html = TRUE,
htmlArgs = list(
caption = 'cuminc estimates at specific time points (<code>cuminc::timepoints</code>).'
)
)
cuminc::timepoints).
0
500
1000
1500
2000
m death
0.002
0.091
0.097
0.104
0.104
f death
0.000
0.063
0.063
-
-
m ltx
0.000
0.738
0.786
0.800
0.800
f ltx
0.003
0.761
0.822
-
-
m withdraw
0.000
0.030
0.045
0.045
0.045
f withdraw
0.000
0.052
0.056
-
-
AIC(cr1$`CRR: death`)
## AIC
## 867.6161
sapply(cr1, BIC)
## CRR: death.BIC CRR: ltx.BIC CRR: withdraw.BIC
## 878.5644 7494.9446 496.6589
logLik(cr1$`CRR: death`)
## 'log Lik.' -428.8081 (df=5)
deviance(cr1$`CRR: death`)
## Call:
## crr(transplant[, "futime"], transplant[, "event"], cov1 = model.matrix(~age +
## sex + abo, transplant)[, -1L, drop = FALSE], cencode = "censored",
## failcode = "death", variance = TRUE, cengroup = rep(1L, nrow(transplant)),
## gtol = 1e-06, maxiter = 10, init = c(0L, 0L, 0L, 0L, 0L))
##
## Deviance = 7.15 on 5 df, 0.20984
crrFits(cr1$`CRR: death`)
## Model selection table
##
## 0: Null Model
##
## 1: Model 1 call:
## crr(transplant[, "futime"], transplant[, "event"], cov1 = model.matrix(~age +
## sex + abo, transplant)[, -1L, drop = FALSE], cencode = "censored",
## failcode = "death", variance = TRUE, cengroup = rep(1L, nrow(transplant)),
## gtol = 1e-06, maxiter = 10, init = c(0L, 0L, 0L, 0L, 0L))
##
##
## n loglik df k -2logLik -2logLik diff AIC AIC diff BIC BIC diff
## 0 797 -432.38 0 1 864.76 7.1484 864.76 0.0000 864.76 0.0
## 1 797 -428.81 5 6 857.62 0.0000 867.62 2.8516 878.56 13.8
crrwald.test(cr1$`CRR: death`)
## chi2 df P
## Overall 8.11151956 5 0.15019572
## age 2.86948310 1 0.09027386
## sexf 2.82220518 1 0.09296860
## aboB 1.04828004 1 0.30590354
## aboAB 0.04546327 1 0.83115446
## aboO 1.91025511 1 0.16693492
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