Computes the Restricted Mean Time Lost (RMTL) for competing risks based on the integrated Aalen-Johansen estimator.
Arguments
- formula
Formula for
phregobject withstratato indicate strata, or+1if no strata.- data
Data frame for calculations.
- cens.code
Censoring code (needed to separate event codes from censorings).
- times
Possible times for which to report restricted mean. Summary displays estimates for these times.
- ...
Additional arguments passed to
phreg.
Value
An object of class "resmean_phreg" containing:
- cumhaz
Matrix of cumulative hazards (years lost).
- se.cumhaz
Standard errors.
- intF1times
Years lost at specified times.
- causes
Vector of cause codes.
Details
A set of time points can be given to be returned in the summary, but the function
computes years-lost for all event times and can be plotted/viewed.
The RMTL for a specific time-point can also be computed using the rmstIPCW function.
Examples
data(bmt)
bmt$time <- bmt$time + runif(408) * 0.001
## Years lost decomposed into causes
drm1 <- cif_yearslost(Event(time, cause) ~ strata(tcell, platelet), data = bmt, times = c(40, 50))
par(mfrow = c(1, 2))
plot(drm1, cause = 1, se = 1)
plot(drm1, cause = 2, se = 1)
summary(drm1)
#> $estimate
#> $estimate$intF_1
#> strata times intF_1 se.intF_1 lower_intF_1
#> tcell.0..platelet.0 0 40 16.718643 1.162627 14.588405
#> tcell.0..platelet.1 1 40 9.731559 1.609585 7.037121
#> tcell.1..platelet.0 2 40 9.953077 3.221209 5.278067
#> tcell.1..platelet.1 3 40 8.302397 2.871799 4.214760
#> tcell.0..platelet.0.1 0 50 21.367845 1.476646 18.661116
#> tcell.0..platelet.1.1 1 50 12.985913 2.047935 9.533103
#> tcell.1..platelet.0.1 2 50 12.645385 4.089967 6.708466
#> tcell.1..platelet.1.1 3 50 11.809303 3.673690 6.418429
#> upper_intF_1
#> tcell.0..platelet.0 19.15994
#> tcell.0..platelet.1 13.45767
#> tcell.1..platelet.0 18.76894
#> tcell.1..platelet.1 16.35438
#> tcell.0..platelet.0.1 24.46718
#> tcell.0..platelet.1.1 17.68930
#> tcell.1..platelet.0.1 23.83641
#> tcell.1..platelet.1.1 21.72800
#>
#> $estimate$intF_2
#> strata times intF_2 se.intF_2 lower_intF_2
#> tcell.0..platelet.0 0 40 6.120930 0.8509983 4.660947
#> tcell.0..platelet.1 1 40 6.388230 1.2998312 4.287303
#> tcell.1..platelet.0 2 40 10.497757 2.8144225 6.207140
#> tcell.1..platelet.1 3 40 9.264228 2.9840677 4.927558
#> tcell.0..platelet.0.1 0 50 8.148258 1.0944538 6.262295
#> tcell.0..platelet.1.1 1 50 8.689754 1.7124193 5.905655
#> tcell.1..platelet.0.1 2 50 14.608646 3.7302732 8.856421
#> tcell.1..platelet.1.1 3 50 12.074989 3.8902005 6.421775
#> upper_intF_2
#> tcell.0..platelet.0 8.038235
#> tcell.0..platelet.1 9.518684
#> tcell.1..platelet.0 17.754217
#> tcell.1..platelet.1 17.417538
#> tcell.0..platelet.0.1 10.602202
#> tcell.0..platelet.1.1 12.786358
#> tcell.1..platelet.0.1 24.096928
#> tcell.1..platelet.1.1 22.704840
#>
#>
#> $total.years.lost
#> [1] 22.83957 16.11979 20.45083 17.56663 29.51610 21.67567 27.25403 23.88429
#>
estimate(drm1, cause = 1)
#> [[1]]
#> Estimate Std.Err 2.5% 97.5% P-value
#> tcell=0, platelet=0 16.719 1.163 14.440 19.00 6.904e-47
#> tcell=0, platelet=1 9.732 1.610 6.577 12.89 1.485e-09
#> tcell=1, platelet=0 9.953 3.221 3.640 16.27 2.003e-03
#> tcell=1, platelet=1 8.302 2.872 2.674 13.93 3.840e-03
#>
#> [[2]]
#> Estimate Std.Err 2.5% 97.5% P-value
#> tcell=0, platelet=0 21.37 1.477 18.474 24.26 1.860e-47
#> tcell=0, platelet=1 12.99 2.048 8.972 17.00 2.283e-10
#> tcell=1, platelet=0 12.65 4.090 4.629 20.66 1.989e-03
#> tcell=1, platelet=1 11.81 3.674 4.609 19.01 1.306e-03
#>
estimate(drm1, cause = 2)
#> [[1]]
#> Estimate Std.Err 2.5% 97.5% P-value
#> tcell=0, platelet=0 6.121 0.851 4.453 7.789 6.355e-13
#> tcell=0, platelet=1 6.388 1.300 3.841 8.936 8.894e-07
#> tcell=1, platelet=0 10.498 2.814 4.982 16.014 1.915e-04
#> tcell=1, platelet=1 9.264 2.984 3.416 15.113 1.906e-03
#>
#> [[2]]
#> Estimate Std.Err 2.5% 97.5% P-value
#> tcell=0, platelet=0 8.148 1.094 6.003 10.29 9.691e-14
#> tcell=0, platelet=1 8.690 1.712 5.333 12.05 3.884e-07
#> tcell=1, platelet=0 14.609 3.730 7.297 21.92 8.994e-05
#> tcell=1, platelet=1 12.075 3.890 4.450 19.70 1.910e-03
#>
## Comparing populations
drm1 <- cif_yearslost(Event(time, cause) ~ strata(tcell, platelet), data = bmt, times = 40)
summary(drm1, contrast = list(1:4))
#> $testintF_1
#> Estimate Std.Err 2.5% 97.5% P-value
#> [p1] - [p2] 6.987 1.986 3.09545 10.88 0.0004333
#> [p1] - [p3] 6.766 3.425 0.05347 13.48 0.0482026
#> [p1] - [p4] 8.416 3.098 2.34386 14.49 0.0065980
#>
#> $testintF_2
#> Estimate Std.Err 2.5% 97.5% P-value
#> [p1] - [p2] -0.2673 1.554 -3.312 2.778 0.8634
#> [p1] - [p3] -4.3768 2.940 -10.140 1.386 0.1366
#> [p1] - [p4] -3.1433 3.103 -9.225 2.939 0.3111
#>
#> $estimate
#> $estimate$intF_1
#> strata times intF_1 se.intF_1 lower_intF_1 upper_intF_1
#> tcell=0, platelet=0 0 40 16.718643 1.162627 14.588405 19.15994
#> tcell=0, platelet=1 1 40 9.731559 1.609585 7.037121 13.45767
#> tcell=1, platelet=0 2 40 9.953077 3.221209 5.278067 18.76894
#> tcell=1, platelet=1 3 40 8.302397 2.871799 4.214760 16.35438
#>
#> $estimate$intF_2
#> strata times intF_2 se.intF_2 lower_intF_2 upper_intF_2
#> tcell=0, platelet=0 0 40 6.120930 0.8509983 4.660947 8.038235
#> tcell=0, platelet=1 1 40 6.388230 1.2998312 4.287303 9.518684
#> tcell=1, platelet=0 2 40 10.497757 2.8144225 6.207140 17.754217
#> tcell=1, platelet=1 3 40 9.264228 2.9840677 4.927558 17.417538
#>
#>
#> $total.years.lost
#> [1] 22.83957 16.11979 20.45083 17.56663
#>
e1 <- estimate(drm1)
estimate(e1, rbind(c(1, -1, 0, 0)))
#> Estimate Std.Err 2.5% 97.5% P-value
#> [tcell=0, platelet=0].... 6.987 1.986 3.095 10.88 0.0004333