Fast Lin-Ying additive hazards model with a possibly stratified baseline. Robust variance is default variance with the summary.
aalenMets(formula, data = data, no.baseline = FALSE, ...)
influence functions (iid) will follow numerical order of given cluster variable so ordering after $id will give iid in order of data-set.
data(bmt); bmt$time <- bmt$time+runif(408)*0.001
out <- aalenMets(Surv(time,cause==1)~tcell+platelet+age,data=bmt)
summary(out)
#>
#> n events
#> 408 161
#>
#> 408 clusters
#> coeffients:
#> Estimate S.E. dU^-1/2 P-value
#> tcell -0.0129630 0.0041295 0.2303820 0.0017
#> platelet -0.0087405 0.0028057 0.1664364 0.0018
#> age 0.0066215 0.0013881 0.0789239 0.0000
#>
#> exp(coeffients):
#> Estimate 2.5% 97.5%
#> tcell 0.98712 0.97916 0.9951
#> platelet 0.99130 0.98586 0.9968
#> age 1.00664 1.00391 1.0094
#>
## out2 <- timereg::aalen(Surv(time,cause==1)~const(tcell)+const(platelet)+const(age),data=bmt)
## summary(out2)