R/binomial.regression.R
binregATE.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.
binregATE(
formula,
data,
cause = 1,
time = NULL,
beta = NULL,
treat.model = ~+1,
cens.model = ~+1,
offset = NULL,
weights = NULL,
cens.weights = NULL,
se = TRUE,
kaplan.meier = TRUE,
cens.code = 0,
no.opt = FALSE,
method = "nr",
augmentation = NULL,
outcome = c("cif", "rmst", "rmst-cause"),
model = "exp",
Ydirect = NULL,
...
)formula with outcome (see coxph)
data frame
cause of interest
time of interest
starting values
logistic treatment model given covariates
only stratified cox model without covariates
offsets for partial likelihood
for score equations
censoring weights
to compute se's with IPCW adjustment, otherwise assumes that IPCW weights are known
uses Kaplan-Meier for IPCW in contrast to exp(-Baseline)
gives censoring code
to not optimize
for optimization
to augment binomial regression
can do CIF regression "cif"=F(t|X), "rmst"=E( min(T, t) | X) , or "rmst-cause"=E( I(epsilon==cause) ( t - mint(T,t)) ) | X)
possible exp model for E( min(T, t) | X)=exp(X^t beta) , or E( I(epsilon==cause) ( t - mint(T,t)) ) | X)=exp(X^t beta)
use this Y instead of outcome constructed inside the program (e.g. I(T< t, epsilon=1)), then uses IPCW vesion of the Y, set outcome to "rmst" to fit using the model specified by model
Additional arguments to lower level funtions
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. If there are no censorings this is the same as ordinary logistic regression modelling.
Estimates the ATE using the the standard binary double robust estimating equations that are IPCW censoring adjusted. Rather than binomial regression we also consider a IPCW weighted version of standard logistic regression logitIPCWATE.
The original version of the program with only binary treatment binregATEbin take binary-numeric as input for the treatment, and also computes the ATT and ATC, average treatment effect on the treated (ATT), E(Y(1) - Y(0) | A=1), and non-treated, respectively. Experimental version.
data(bmt)
dfactor(bmt) <- ~.
brs <- binregATE(Event(time,cause)~tcell.f+platelet+age,bmt,time=50,cause=1,
treat.model=tcell.f~platelet+age)
summary(brs)
#>
#> n events
#> 408 160
#>
#> 408 clusters
#> coeffients:
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) -0.195296 0.131037 -0.452123 0.061532 0.1361
#> tcell.f1 -0.637346 0.359010 -1.340993 0.066301 0.0759
#> platelet -0.351900 0.247162 -0.836329 0.132530 0.1545
#> age 0.419785 0.106904 0.210258 0.629312 0.0001
#>
#> exp(coeffients):
#> Estimate 2.5% 97.5%
#> (Intercept) 0.82259 0.63628 1.0635
#> tcell.f1 0.52869 0.26159 1.0685
#> platelet 0.70335 0.43330 1.1417
#> age 1.52163 1.23400 1.8763
#>
#> Average Treatment effects (G-formula) :
#> Estimate Std.Err 2.5% 97.5% P-value
#> treat0 0.4287845 0.0275311 0.3748245 0.4827444 0.0000
#> treat1 0.2896149 0.0664282 0.1594181 0.4198117 0.0000
#> treat:1-0 -0.1391696 0.0723524 -0.2809777 0.0026385 0.0544
#>
#> Average Treatment effects (double robust) :
#> Estimate Std.Err 2.5% 97.5% P-value
#> treat0 0.428414 0.027645 0.374230 0.482597 0.0000
#> treat1 0.254948 0.064321 0.128882 0.381014 0.0001
#> treat:1-0 -0.173466 0.069774 -0.310220 -0.036712 0.0129
#>
#>
brsi <- binregATE(Event(time,cause)~tcell.f+tcell.f*platelet+tcell.f*age,bmt,time=50,cause=1,
treat.model=tcell.f~platelet+age)
summary(brsi)
#>
#> n events
#> 408 160
#>
#> 408 clusters
#> coeffients:
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) -0.1639029 0.1330757 -0.4247265 0.0969207 0.2181
#> tcell.f1 -0.9605103 0.4940801 -1.9288895 0.0078688 0.0519
#> platelet -0.4729820 0.2709874 -1.0041074 0.0581435 0.0809
#> age 0.4267673 0.1116644 0.2079091 0.6456256 0.0001
#> tcell.f1:platelet 0.7995261 0.6966528 -0.5658883 2.1649406 0.2511
#> tcell.f1:age -0.1298374 0.4266685 -0.9660922 0.7064175 0.7609
#>
#> exp(coeffients):
#> Estimate 2.5% 97.5%
#> (Intercept) 0.84882 0.65395 1.1018
#> tcell.f1 0.38270 0.14531 1.0079
#> platelet 0.62314 0.36637 1.0599
#> age 1.53230 1.23110 1.9072
#> tcell.f1:platelet 2.22449 0.56786 8.7141
#> tcell.f1:age 0.87824 0.38057 2.0267
#>
#> Average Treatment effects (G-formula) :
#> Estimate Std.Err 2.5% 97.5% P-value
#> treat0 0.427926 0.027581 0.373868 0.481985 0.0000
#> treat1 0.269269 0.069599 0.132858 0.405680 0.0001
#> treat:1-0 -0.158657 0.074720 -0.305106 -0.012208 0.0337
#>
#> Average Treatment effects (double robust) :
#> Estimate Std.Err 2.5% 97.5% P-value
#> treat0 0.428394 0.027646 0.374208 0.482579 0.0000
#> treat1 0.257193 0.066194 0.127455 0.386931 0.0001
#> treat:1-0 -0.171201 0.071541 -0.311419 -0.030982 0.0167
#>
#>