Fast Additive Hazards Model with Robust Standard Errors

Fits a fast Lin-Ying additive hazards model with a possibly stratified baseline. Robust variance is the default variance estimate in the summary.

Usage

aalenMets(formula, data = data, no.baseline = FALSE, ...)

Arguments

formula

Formula with a 'Surv' outcome (similar to coxph).

data

Data frame.

no.baseline

Logical; if TRUE, fits the model without baseline hazard estimation.

...

Additional arguments passed to phreg.

Value

An object of class "aalenMets" (extends "phreg") containing:

coef

Estimated coefficients.

var

Robust variance-covariance matrix.

iid

Influence functions.

intZHZ

Integrated ZHZ matrix.

gamma

Coefficient estimates.

Details

Influence functions (IID) follow the numerical order of the given cluster variable. Ordering by $id aligns the IID terms with the dataset order.

Author

Thomas Scheike

Examples

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
#> coefficients:
#>            Estimate       S.E.    dU^-1/2 P-value
#> tcell    -0.0129507  0.0041288  0.2304083  0.0017
#> platelet -0.0087471  0.0028053  0.1664259  0.0018
#> age       0.0066174  0.0013879  0.0789210  0.0000
#> 
#> exp(coefficients):
#>          Estimate    2.5%  97.5%
#> tcell     0.98713 0.97918 0.9952
#> platelet  0.99129 0.98586 0.9968
#> age       1.00664 1.00390 1.0094
#> 

## Comparison with timereg::aalen
## out2 <- timereg::aalen(
##   Surv(time, cause == 1) ~ const(tcell) + const(platelet) + const(age),
##   data = bmt
## )
## summary(out2)