Summary method for an object of class 'probtrans'. It prints a selection of the estimated transition probabilities, and, if requested, also of the variances.
Arguments
- object
Object of class 'probtrans', containing estimated transition probabilities from and to all states in a multi-state model
- times
Time points at which to evaluate the transition probabilites
- from
Specifies from which state the transition probabilities are to be printed. Should be subset of 1:S, with S the number of states in the multi-state model. Default is print from state 1 only. User can specify from=0 to print transition probabilities from all states
- to
Specifies the transition probabilities to which state are to be printed. User can specify to=0 to print transition probabilities to all states. This is also the default
- variance
Whether or not the standard errors of the estimated transition probabilities should be printed; default is
TRUE- conf.int
The proportion to be covered by the confidence intervals, default is 0.95
- conf.type
The type of confidence interval, one of "log", "none", or "plain". Defaults to "log"
- extend
logical value: if
TRUE, prints information for all specified times, even if there are no subjects left at the end of the specified times. This is only valid if the times argument is present- ...
Further arguments to print
Value
Function summary.probtrans returns an object of class
"summary.probtrans", which is a list (for each from state) of
transition probabilities at the specified (or all) time points. The
print method of a summary.probtrans doesn't return a value.
Author
Hein Putter H.Putter@lumc.nl
Examples
# First run the example of probtrans
tmat <- trans.illdeath()
tg <- data.frame(illt=c(1,1,6,6,8,9),ills=c(1,0,1,1,0,1),
dt=c(5,1,9,7,8,12),ds=c(1,1,1,1,1,1),
x1=c(1,1,1,0,0,0),x2=c(6:1))
tglong <- msprep(time=c(NA,"illt","dt"),status=c(NA,"ills","ds"),
data=tg,keep=c("x1","x2"),trans=tmat)
tglong <- expand.covs(tglong,c("x1","x2"))
cx <- coxph(Surv(Tstart,Tstop,status)~x1.1+x2.2+strata(trans),
data=tglong,method="breslow")
newdata <- data.frame(trans=1:3,x1.1=c(0,0,0),x2.2=c(0,1,0),strata=1:3)
HvH <- msfit(cx,newdata,trans=tmat)
pt <- probtrans(HvH,predt=0)
# Default, prediction from state 1
summary(pt)
#>
#> Prediction from state 1 :
#> time pstate1 pstate2 pstate3 se1 se2 se3
#> 1 0 1.0000000 0.00000000 0.000000000 0.00000000 0.00000000 0.00000000
#> 2 1 0.9299816 0.06204689 0.007971491 0.08750408 0.08250577 0.02915069
#> 3 5 0.9299816 0.00000000 0.070018378 0.08750408 0.00000000 0.08750408
#> 4 6 0.6776902 0.25229141 0.070018378 0.22689332 0.21903899 0.08750408
#> 5 7 0.6776902 0.12614570 0.196164083 0.22689332 0.12638316 0.14680748
#> 6 8 0.4757726 0.12614570 0.398081646 0.23614089 0.12638316 0.19877253
#> 7 9 0.0000000 0.47577265 0.524227350 0.00000000 0.53115171 0.53115171
#> 8 12 0.0000000 0.00000000 1.000000000 0.00000000 0.00000000 0.00000000
#> lower1 lower2 lower3 upper1 upper2 upper3
#> 1 1.0000000 0.000000000 0.000000e+00 1 0.0000000 0.0000000
#> 2 0.7733621 0.004579832 6.149053e-06 1 0.8406020 1.0000000
#> 3 0.7733621 0.000000000 6.045605e-03 1 0.0000000 0.8109318
#> 4 0.3515974 0.046014074 6.045605e-03 1 1.0000000 0.8109318
#> 5 0.3515974 0.017703886 4.524678e-02 1 0.8988274 0.8504549
#> 6 0.1798546 0.017703886 1.496046e-01 1 0.8988274 1.0000000
#> 7 0.0000000 0.053348184 7.195730e-02 0 1.0000000 1.0000000
#> 8 0.0000000 0.000000000 1.000000e+00 0 0.0000000 1.0000000
# Only from states 1 and 3
summary(pt, from=c(1, 3))
#>
#> Prediction from state 1 :
#> time pstate1 pstate2 pstate3 se1 se2 se3
#> 1 0 1.0000000 0.00000000 0.000000000 0.00000000 0.00000000 0.00000000
#> 2 1 0.9299816 0.06204689 0.007971491 0.08750408 0.08250577 0.02915069
#> 3 5 0.9299816 0.00000000 0.070018378 0.08750408 0.00000000 0.08750408
#> 4 6 0.6776902 0.25229141 0.070018378 0.22689332 0.21903899 0.08750408
#> 5 7 0.6776902 0.12614570 0.196164083 0.22689332 0.12638316 0.14680748
#> 6 8 0.4757726 0.12614570 0.398081646 0.23614089 0.12638316 0.19877253
#> 7 9 0.0000000 0.47577265 0.524227350 0.00000000 0.53115171 0.53115171
#> 8 12 0.0000000 0.00000000 1.000000000 0.00000000 0.00000000 0.00000000
#> lower1 lower2 lower3 upper1 upper2 upper3
#> 1 1.0000000 0.000000000 0.000000e+00 1 0.0000000 0.0000000
#> 2 0.7733621 0.004579832 6.149053e-06 1 0.8406020 1.0000000
#> 3 0.7733621 0.000000000 6.045605e-03 1 0.0000000 0.8109318
#> 4 0.3515974 0.046014074 6.045605e-03 1 1.0000000 0.8109318
#> 5 0.3515974 0.017703886 4.524678e-02 1 0.8988274 0.8504549
#> 6 0.1798546 0.017703886 1.496046e-01 1 0.8988274 1.0000000
#> 7 0.0000000 0.053348184 7.195730e-02 0 1.0000000 1.0000000
#> 8 0.0000000 0.000000000 1.000000e+00 0 0.0000000 1.0000000
#>
#> Prediction from state 3 :
#> time pstate1 pstate2 pstate3 se1 se2 se3 lower1 lower2 lower3 upper1 upper2
#> 1 0 0 0 1 0 0 0 0 0 1 0 0
#> 2 1 0 0 1 0 0 0 0 0 1 0 0
#> 3 5 0 0 1 0 0 0 0 0 1 0 0
#> 4 6 0 0 1 0 0 0 0 0 1 0 0
#> 5 7 0 0 1 0 0 0 0 0 1 0 0
#> 6 8 0 0 1 0 0 0 0 0 1 0 0
#> 7 9 0 0 1 0 0 0 0 0 1 0 0
#> 8 12 0 0 1 0 0 0 0 0 1 0 0
#> upper3
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1
#> 6 1
#> 7 1
#> 8 1
# Use from=0 for prediction from all states
summary(pt, from=0)
#>
#> Prediction from state 1 :
#> time pstate1 pstate2 pstate3 se1 se2 se3
#> 1 0 1.0000000 0.00000000 0.000000000 0.00000000 0.00000000 0.00000000
#> 2 1 0.9299816 0.06204689 0.007971491 0.08750408 0.08250577 0.02915069
#> 3 5 0.9299816 0.00000000 0.070018378 0.08750408 0.00000000 0.08750408
#> 4 6 0.6776902 0.25229141 0.070018378 0.22689332 0.21903899 0.08750408
#> 5 7 0.6776902 0.12614570 0.196164083 0.22689332 0.12638316 0.14680748
#> 6 8 0.4757726 0.12614570 0.398081646 0.23614089 0.12638316 0.19877253
#> 7 9 0.0000000 0.47577265 0.524227350 0.00000000 0.53115171 0.53115171
#> 8 12 0.0000000 0.00000000 1.000000000 0.00000000 0.00000000 0.00000000
#> lower1 lower2 lower3 upper1 upper2 upper3
#> 1 1.0000000 0.000000000 0.000000e+00 1 0.0000000 0.0000000
#> 2 0.7733621 0.004579832 6.149053e-06 1 0.8406020 1.0000000
#> 3 0.7733621 0.000000000 6.045605e-03 1 0.0000000 0.8109318
#> 4 0.3515974 0.046014074 6.045605e-03 1 1.0000000 0.8109318
#> 5 0.3515974 0.017703886 4.524678e-02 1 0.8988274 0.8504549
#> 6 0.1798546 0.017703886 1.496046e-01 1 0.8988274 1.0000000
#> 7 0.0000000 0.053348184 7.195730e-02 0 1.0000000 1.0000000
#> 8 0.0000000 0.000000000 1.000000e+00 0 0.0000000 1.0000000
#>
#> Prediction from state 2 :
#> time pstate1 pstate2 pstate3 se1 se2 se3 lower1 lower2 lower3 upper1 upper2
#> 1 0 0 1 0 0 0 0 0 1 0 0 1
#> 2 1 0 1 0 0 0 0 0 1 0 0 1
#> 3 5 0 0 1 0 0 0 0 0 1 0 0
#> 4 6 0 0 1 0 0 0 0 0 1 0 0
#> 5 7 0 0 1 0 0 0 0 0 1 0 0
#> 6 8 0 0 1 0 0 0 0 0 1 0 0
#> 7 9 0 0 1 0 0 0 0 0 1 0 0
#> 8 12 0 0 1 0 0 0 0 0 1 0 0
#> upper3
#> 1 0
#> 2 0
#> 3 1
#> 4 1
#> 5 1
#> 6 1
#> 7 1
#> 8 1
#>
#> Prediction from state 3 :
#> time pstate1 pstate2 pstate3 se1 se2 se3 lower1 lower2 lower3 upper1 upper2
#> 1 0 0 0 1 0 0 0 0 0 1 0 0
#> 2 1 0 0 1 0 0 0 0 0 1 0 0
#> 3 5 0 0 1 0 0 0 0 0 1 0 0
#> 4 6 0 0 1 0 0 0 0 0 1 0 0
#> 5 7 0 0 1 0 0 0 0 0 1 0 0
#> 6 8 0 0 1 0 0 0 0 0 1 0 0
#> 7 9 0 0 1 0 0 0 0 0 1 0 0
#> 8 12 0 0 1 0 0 0 0 0 1 0 0
#> upper3
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 1
#> 6 1
#> 7 1
#> 8 1
# Only to states 1 and 2
summary(pt, to=1:2)
#>
#> Prediction from state 1 :
#> time pstate1 pstate2 se1 se2 lower1 lower2 upper1
#> 1 0 1.0000000 0.00000000 0.00000000 0.00000000 1.0000000 0.000000000 1
#> 2 1 0.9299816 0.06204689 0.08750408 0.08250577 0.7733621 0.004579832 1
#> 3 5 0.9299816 0.00000000 0.08750408 0.00000000 0.7733621 0.000000000 1
#> 4 6 0.6776902 0.25229141 0.22689332 0.21903899 0.3515974 0.046014074 1
#> 5 7 0.6776902 0.12614570 0.22689332 0.12638316 0.3515974 0.017703886 1
#> 6 8 0.4757726 0.12614570 0.23614089 0.12638316 0.1798546 0.017703886 1
#> 7 9 0.0000000 0.47577265 0.00000000 0.53115171 0.0000000 0.053348184 0
#> 8 12 0.0000000 0.00000000 0.00000000 0.00000000 0.0000000 0.000000000 0
#> upper2
#> 1 0.0000000
#> 2 0.8406020
#> 3 0.0000000
#> 4 1.0000000
#> 5 0.8988274
#> 6 0.8988274
#> 7 1.0000000
#> 8 0.0000000
# Default is 95% confidence interval, change here to 90%
summary(pt, to=1:2, conf.int=0.90)
#>
#> Prediction from state 1 :
#> time pstate1 pstate2 se1 se2 lower1 lower2 upper1
#> 1 0 1.0000000 0.00000000 0.00000000 0.00000000 1.0000000 0.000000000 1
#> 2 1 0.9299816 0.06204689 0.08750408 0.08250577 0.7966352 0.006963449 1
#> 3 5 0.9299816 0.00000000 0.08750408 0.00000000 0.7966352 0.000000000 1
#> 4 6 0.6776902 0.25229141 0.22689332 0.21903899 0.3907183 0.060492876 1
#> 5 7 0.6776902 0.12614570 0.22689332 0.12638316 0.3907183 0.024275988 1
#> 6 8 0.4757726 0.12614570 0.23614089 0.12638316 0.2103026 0.024275988 1
#> 7 9 0.0000000 0.47577265 0.00000000 0.53115171 0.0000000 0.075840205 0
#> 8 12 0.0000000 0.00000000 0.00000000 0.00000000 0.0000000 0.000000000 0
#> upper2
#> 1 0.0000000
#> 2 0.5528605
#> 3 0.0000000
#> 4 1.0000000
#> 5 0.6554930
#> 6 0.6554930
#> 7 1.0000000
#> 8 0.0000000
# Do not show variances (nor confidence intervals)
summary(pt, to=1:2, variance=FALSE)
#>
#> Prediction from state 1 :
#> time pstate1 pstate2
#> 1 0 1.0000000 0.00000000
#> 2 1 0.9299816 0.06204689
#> 3 5 0.9299816 0.00000000
#> 4 6 0.6776902 0.25229141
#> 5 7 0.6776902 0.12614570
#> 6 8 0.4757726 0.12614570
#> 7 9 0.0000000 0.47577265
#> 8 12 0.0000000 0.00000000
# Transition probabilities only at specified time points
summary(pt, times=seq(0, 15, by=3))
#>
#> Prediction from state 1 :
#> times pstate1 pstate2 pstate3 se1 se2 se3
#> 1 0 1.0000000 0.00000000 0.000000000 0.00000000 0.00000000 0.00000000
#> 2 3 0.9299816 0.06204689 0.007971491 0.08750408 0.08250577 0.02915069
#> 3 6 0.6776902 0.25229141 0.070018378 0.22689332 0.21903899 0.08750408
#> 4 9 0.0000000 0.47577265 0.524227350 0.00000000 0.53115171 0.53115171
#> 5 12 0.0000000 0.00000000 1.000000000 0.00000000 0.00000000 0.00000000
#> lower1 lower2 lower3 upper1 upper2 upper3
#> 1 1.0000000 0.000000000 0.000000e+00 1 0.000000 0.0000000
#> 2 0.7733621 0.004579832 6.149053e-06 1 0.840602 1.0000000
#> 3 0.3515974 0.046014074 6.045605e-03 1 1.000000 0.8109318
#> 4 0.0000000 0.053348184 7.195730e-02 0 1.000000 1.0000000
#> 5 0.0000000 0.000000000 1.000000e+00 0 0.000000 1.0000000
# Last specified time point is larger than last observed, not printed
# Use extend=TRUE as in summary.survfit
summary(pt, times=seq(0, 15, by=3), extend=TRUE)
#>
#> Prediction from state 1 :
#> times pstate1 pstate2 pstate3 se1 se2 se3
#> 1 0 1.0000000 0.00000000 0.000000000 0.00000000 0.00000000 0.00000000
#> 2 3 0.9299816 0.06204689 0.007971491 0.08750408 0.08250577 0.02915069
#> 3 6 0.6776902 0.25229141 0.070018378 0.22689332 0.21903899 0.08750408
#> 4 9 0.0000000 0.47577265 0.524227350 0.00000000 0.53115171 0.53115171
#> 5 12 0.0000000 0.00000000 1.000000000 0.00000000 0.00000000 0.00000000
#> 6 15 0.0000000 0.00000000 1.000000000 0.00000000 0.00000000 0.00000000
#> lower1 lower2 lower3 upper1 upper2 upper3
#> 1 1.0000000 0.000000000 0.000000e+00 1 0.000000 0.0000000
#> 2 0.7733621 0.004579832 6.149053e-06 1 0.840602 1.0000000
#> 3 0.3515974 0.046014074 6.045605e-03 1 1.000000 0.8109318
#> 4 0.0000000 0.053348184 7.195730e-02 0 1.000000 1.0000000
#> 5 0.0000000 0.000000000 1.000000e+00 0 0.000000 1.0000000
#> 6 0.0000000 0.000000000 1.000000e+00 0 0.000000 1.0000000
# Different types of confidence intervals, default is log
summary(pt, times=seq(0, 15, by=3), conf.type="plain")
#>
#> Prediction from state 1 :
#> times pstate1 pstate2 pstate3 se1 se2 se3
#> 1 0 1.0000000 0.00000000 0.000000000 0.00000000 0.00000000 0.00000000
#> 2 3 0.9299816 0.06204689 0.007971491 0.08750408 0.08250577 0.02915069
#> 3 6 0.6776902 0.25229141 0.070018378 0.22689332 0.21903899 0.08750408
#> 4 9 0.0000000 0.47577265 0.524227350 0.00000000 0.53115171 0.53115171
#> 5 12 0.0000000 0.00000000 1.000000000 0.00000000 0.00000000 0.00000000
#> lower1 lower2 lower3 upper1 upper2 upper3
#> 1 1.0000000 0 0 1 0.0000000 0.00000000
#> 2 0.7584768 0 0 1 0.2237552 0.06510579
#> 3 0.2329875 0 0 1 0.6815999 0.24152322
#> 4 0.0000000 0 0 0 1.0000000 1.00000000
#> 5 0.0000000 0 1 0 0.0000000 1.00000000
summary(pt, times=seq(0, 15, by=3), conf.type="no")
#>
#> Prediction from state 1 :
#> times pstate1 pstate2 pstate3 se1 se2 se3
#> 1 0 1.0000000 0.00000000 0.000000000 0.00000000 0.00000000 0.00000000
#> 2 3 0.9299816 0.06204689 0.007971491 0.08750408 0.08250577 0.02915069
#> 3 6 0.6776902 0.25229141 0.070018378 0.22689332 0.21903899 0.08750408
#> 4 9 0.0000000 0.47577265 0.524227350 0.00000000 0.53115171 0.53115171
#> 5 12 0.0000000 0.00000000 1.000000000 0.00000000 0.00000000 0.00000000
# When the number of time points specified is larger than 12, head and tail is shown
x <- summary(pt, times=seq(5, 8, by=0.25))
x
#>
#> Prediction from state 1 (head and tail):
#> times pstate1 pstate2 pstate3 se1 se2 se3 lower1
#> 1 5.00 0.9299816 0.0000000 0.07001838 0.08750408 0.000000 0.08750408 0.7733621
#> 2 5.25 0.9299816 0.0000000 0.07001838 0.08750408 0.000000 0.08750408 0.7733621
#> 3 5.50 0.9299816 0.0000000 0.07001838 0.08750408 0.000000 0.08750408 0.7733621
#> 4 5.75 0.9299816 0.0000000 0.07001838 0.08750408 0.000000 0.08750408 0.7733621
#> 5 6.00 0.6776902 0.2522914 0.07001838 0.22689332 0.219039 0.08750408 0.3515974
#> 6 6.25 0.6776902 0.2522914 0.07001838 0.22689332 0.219039 0.08750408 0.3515974
#> lower2 lower3 upper1 upper2 upper3
#> 1 0.00000000 0.006045605 1 0 0.8109318
#> 2 0.00000000 0.006045605 1 0 0.8109318
#> 3 0.00000000 0.006045605 1 0 0.8109318
#> 4 0.00000000 0.006045605 1 0 0.8109318
#> 5 0.04601407 0.006045605 1 1 0.8109318
#> 6 0.04601407 0.006045605 1 1 0.8109318
#>
#> ...
#> times pstate1 pstate2 pstate3 se1 se2 se3
#> 8 6.75 0.6776902 0.2522914 0.07001838 0.2268933 0.2190390 0.08750408
#> 9 7.00 0.6776902 0.1261457 0.19616408 0.2268933 0.1263832 0.14680748
#> 10 7.25 0.6776902 0.1261457 0.19616408 0.2268933 0.1263832 0.14680748
#> 11 7.50 0.6776902 0.1261457 0.19616408 0.2268933 0.1263832 0.14680748
#> 12 7.75 0.6776902 0.1261457 0.19616408 0.2268933 0.1263832 0.14680748
#> 13 8.00 0.4757726 0.1261457 0.39808165 0.2361409 0.1263832 0.19877253
#> lower1 lower2 lower3 upper1 upper2 upper3
#> 8 0.3515974 0.04601407 0.006045605 1 1.0000000 0.8109318
#> 9 0.3515974 0.01770389 0.045246782 1 0.8988274 0.8504549
#> 10 0.3515974 0.01770389 0.045246782 1 0.8988274 0.8504549
#> 11 0.3515974 0.01770389 0.045246782 1 0.8988274 0.8504549
#> 12 0.3515974 0.01770389 0.045246782 1 0.8988274 0.8504549
#> 13 0.1798546 0.01770389 0.149604637 1 0.8988274 1.0000000