R/restricted.mean.R
resmeanATE.RdUnder the standard causal assumptions we can estimate the average treatment effect E(Y(1) - Y(0)). We need Consistency, ignorability ( Y(1), Y(0) indep A given X), and positivity.
resmeanATE(formula, data, model = "exp", ...)The first covariate in the specification of the competing risks regression model must be the treatment effect that is a factor. If the factor has more than two levels then it uses the mlogit for propensity score modelling. We consider the outcome mint(T;tau) or I(epsion==cause1)(t- min(T;t)) that gives years lost due to cause "cause" depending on the number of causes. The default model is the exp(X^ beta) and otherwise a linear model is used.
Estimates the ATE using the the standard binary double robust estimating equations that are IPCW censoring adjusted.
library(mets); data(bmt); bmt$event <- bmt$cause!=0; dfactor(bmt) <- tcell~tcell
out <- resmeanATE(Event(time,event)~tcell+platelet,data=bmt,time=40,treat.model=tcell~platelet)
summary(out)
#>
#> n events
#> 408 241
#>
#> 408 clusters
#> coeffients:
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) 2.852563 0.062496 2.730074 2.975052 0.0000
#> tcell1 0.021289 0.122983 -0.219753 0.262330 0.8626
#> platelet 0.303313 0.090772 0.125404 0.481222 0.0008
#>
#> exp(coeffients):
#> Estimate 2.5% 97.5%
#> (Intercept) 17.33214 15.33402 19.5906
#> tcell1 1.02152 0.80272 1.3000
#> platelet 1.35434 1.13361 1.6181
#>
#> Average Treatment effects (G-formula) :
#> Estimate Std.Err 2.5% 97.5% P-value
#> treat0 19.25887 0.95918 17.37892 21.13882 0.0000
#> treat1 19.67326 2.22868 15.30513 24.04139 0.0000
#> treat:1-0 0.41439 2.41150 -4.31207 5.14085 0.8636
#>
#> Average Treatment effects (double robust) :
#> Estimate Std.Err 2.5% 97.5% P-value
#> treat0 19.3224 1.0515 17.2614 21.3834 0.0000
#> treat1 21.5582 3.8016 14.1072 29.0091 0.0000
#> treat:1-0 2.2358 4.1989 -5.9940 10.4655 0.5944
#>
#>
out1 <- resmeanATE(Event(time,cause)~tcell+platelet,data=bmt,cause=1,time=40,
treat.model=tcell~platelet)
summary(out1)
#>
#> n events
#> 408 157
#>
#> 408 clusters
#> coeffients:
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) 2.806269 0.069619 2.669817 2.942721 0.0000
#> tcell1 -0.374141 0.247689 -0.859602 0.111320 0.1309
#> platelet -0.491646 0.164934 -0.814911 -0.168381 0.0029
#>
#> exp(coeffients):
#> Estimate 2.5% 97.5%
#> (Intercept) 16.54807 14.43733 18.9674
#> tcell1 0.68788 0.42333 1.1178
#> platelet 0.61162 0.44268 0.8450
#>
#> Average Treatment effects (G-formula) :
#> Estimate Std.Err 2.5% 97.5% P-value
#> treat0 14.53176 0.95705 12.65599 16.40754 0.0000
#> treat1 9.99611 2.37816 5.33501 14.65721 0.0000
#> treat:1-0 -4.53566 2.57515 -9.58286 0.51155 0.0782
#>
#> Average Treatment effects (double robust) :
#> Estimate Std.Err 2.5% 97.5% P-value
#> treat0 14.5200247 0.9576900 12.6429868 16.3970625 0.0000
#> treat1 9.4568371 2.4051437 4.7428421 14.1708321 0.0001
#> treat:1-0 -5.0631876 2.5856762 -10.1310197 0.0046446 0.0502
#>
#>