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 16.718647 1.162628 14.588407
#> tcell.0..platelet.1 1 40 9.728016 1.609499 7.033849
#> tcell.1..platelet.0 2 40 9.953058 3.221203 5.278056
#> tcell.1..platelet.1 3 40 8.302397 2.871793 4.214767
#> tcell.0..platelet.0.1 0 50 21.367831 1.476647 18.661101
#> tcell.0..platelet.1.1 1 50 12.979253 2.047517 9.527304
#> tcell.1..platelet.0.1 2 50 12.645366 4.089961 6.708456
#> tcell.1..platelet.1.1 3 50 11.809339 3.673686 6.418465
#> upper_intF_1
#> tcell.0..platelet.0 19.15995
#> tcell.0..platelet.1 13.45413
#> tcell.1..platelet.0 18.76891
#> tcell.1..platelet.1 16.35436
#> tcell.0..platelet.0.1 24.46716
#> tcell.0..platelet.1.1 17.68192
#> tcell.1..platelet.0.1 23.83638
#> tcell.1..platelet.1.1 21.72801
#>
#> $estimate$intF_2
#> strata times intF_2 se.intF_2 lower_intF_2
#> tcell.0..platelet.0 0 40 6.121405 0.8509979 4.661408
#> tcell.0..platelet.1 1 40 6.388328 1.2998315 4.287395
#> tcell.1..platelet.0 2 40 10.497731 2.8144210 6.207118
#> tcell.1..platelet.1 3 40 9.264319 2.9840973 4.927606
#> tcell.0..platelet.0.1 0 50 8.149712 1.0945194 6.263607
#> tcell.0..platelet.1.1 1 50 8.690047 1.7124397 5.905904
#> tcell.1..platelet.0.1 2 50 14.608620 3.7302702 8.856401
#> tcell.1..platelet.1.1 3 50 12.075080 3.8902302 6.421823
#> upper_intF_2
#> tcell.0..platelet.0 8.038689
#> tcell.0..platelet.1 9.518773
#> tcell.1..platelet.0 17.754191
#> tcell.1..platelet.1 17.417710
#> tcell.0..platelet.0.1 10.603763
#> tcell.0..platelet.1.1 12.786681
#> tcell.1..platelet.0.1 24.096897
#> tcell.1..platelet.1.1 22.705012
#>
#>
#> $total.years.lost
#> [1] 22.84005 16.11634 20.45079 17.56672 29.51754 21.66930 27.25399 23.88442
#>
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 6.905e-47
#> tcell=0, platelet=1 9.728 1.609 6.573 12.88 1.502e-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 1.861e-47
#> tcell=0, platelet=1 12.98 2.048 8.966 16.99 2.312e-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 6.329e-13
#> tcell=0, platelet=1 6.388 1.300 3.841 8.936 8.890e-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 8.15 1.095 6.004 10.29 9.627e-14
#> tcell=0, platelet=1 8.69 1.712 5.334 12.05 3.882e-07
#> tcell=1, platelet=0 14.61 3.730 7.297 21.92 8.994e-05
#> tcell=1, platelet=1 12.08 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] 6.991 1.985 3.0991 10.88 0.0004302
#> [p1] - [p3] 6.766 3.425 0.0535 13.48 0.0482015
#> [p1] - [p4] 8.416 3.098 2.3439 14.49 0.0065978
#> ────────────────────────────────────────────────────────────
#> Null Hypothesis:
#> [p1] - [p2] = 0
#> [p1] - [p3] = 0
#> [p1] - [p4] = 0
#>
#> chisq = 17.643, df = 3, p-value = 0.0005211
#>
#> $testintF_2
#> Estimate Std.Err 2.5% 97.5% P-value
#> [p1] - [p2] -0.2669 1.554 -3.312 2.778 0.8636
#> [p1] - [p3] -4.3763 2.940 -10.139 1.386 0.1366
#> [p1] - [p4] -3.1429 3.103 -9.225 2.939 0.3111
#> ────────────────────────────────────────────────────────────
#> Null Hypothesis:
#> [p1] - [p2] = 0
#> [p1] - [p3] = 0
#> [p1] - [p4] = 0
#>
#> chisq = 3.0579, df = 3, p-value = 0.3828
#>
#> $estimate
#> $estimate$intF_1
#> strata times intF_1 se.intF_1 lower_intF_1 upper_intF_1
#> tcell=0, platelet=0 0 40 16.718647 1.162628 14.588407 19.15995
#> tcell=0, platelet=1 1 40 9.728016 1.609499 7.033849 13.45413
#> tcell=1, platelet=0 2 40 9.953058 3.221203 5.278056 18.76891
#> tcell=1, platelet=1 3 40 8.302397 2.871793 4.214767 16.35436
#>
#> $estimate$intF_2
#> strata times intF_2 se.intF_2 lower_intF_2 upper_intF_2
#> tcell=0, platelet=0 0 40 6.121405 0.8509979 4.661408 8.038689
#> tcell=0, platelet=1 1 40 6.388328 1.2998315 4.287395 9.518773
#> tcell=1, platelet=0 2 40 10.497731 2.8144210 6.207118 17.754191
#> tcell=1, platelet=1 3 40 9.264319 2.9840973 4.927606 17.417710
#>
#>
#> $total.years.lost
#> [1] 22.84005 16.11634 20.45079 17.56672
#>
e1 <- estimate(drm1)
estimate(e1, rbind(c(1, -1, 0, 0)))
#> Estimate Std.Err 2.5% 97.5% P-value
#> [tcell=0, platelet=0].... 6.991 1.985 3.099 10.88 0.0004302
#> ────────────────────────────────────────────────────────────
#> Null Hypothesis:
#> [tcell=0, platelet=0] - [tcell=0, platelet=1] = 0
#>
#> chisq = 12.3964, df = 1, p-value = 0.0004302