R/binomial.regression.R
binregG.Rd
Computes G-estimator $$ \hat F(t,A=a) = n^{-1} \sum_i \hat F(t,A=a,Z_i) $$. Assumes that the first covariate is $A$. Gives influence functions of these risk estimates and SE's are based on these. If first covariate is a factor then all contrast are computed, and if continuous then considered covariate values are given by Avalues.
binregG(x, data, Avalues = c(0, 1), varname = NULL)
data(bmt); bmt$time <- bmt$time+runif(408)*0.001
bmt$event <- (bmt$cause!=0)*1
b1 <- binreg(Event(time,cause)~age+tcell+platelet,bmt,cause=1,time=50)
sb1 <- binregG(b1,bmt,Avalues=c(0,1,2))
summary(sb1)
#> G-estimator :
#> Estimate Std.Err 2.5% 97.5% P-value
#> risk0 0.4058 0.02588 0.3551 0.4566 1.981e-55
#> risk1 0.5120 0.03706 0.4393 0.5846 2.092e-43
#> risk2 0.6169 0.05517 0.5087 0.7250 5.030e-29
#>
#> Average Treatment effect: difference (G-estimator) :
#> Estimate Std.Err 2.5% 97.5% P-value
#> p1 0.1061 0.02623 0.0547 0.1575 5.227e-05
#> p2 0.2110 0.04960 0.1138 0.3082 2.098e-05
#>
#> Average Treatment effect: ratio (G-estimator) :
#> Estimate Std.Err 2.5% 97.5% P-value
#> [p1] 1.261478 0.06656797 1.131007 1.391949 8.565908e-05
#> [p2] 1.519944 0.12770722 1.269642 1.770245 4.673689e-05
#>
#> Average Treatment effect: 1-G (survival)-ratio (G-estimator) :
#> NULL
#>