Summary method for an object of class 'probtrans'. It prints a selection of the estimated transition probabilities, and, if requested, also of the variances.
# S3 method for probtrans summary( object, times, from = 1, to = 0, variance = TRUE, conf.int = 0.95, conf.type = c("log", "none", "plain"), extend = FALSE, ... )
| 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 |
| 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 |
| ... | Further arguments to print |
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.
Hein Putter H.Putter@lumc.nl
# 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#> #> 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#> #> 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#> #> 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#> #> 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#> #> 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#> #> 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#> #> 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