Estimates the percentage of the years lost that is due to a cause and how covariates affects this percentage by doing ICPW regression.
binregRatio(
formula,
data,
cause = 1,
time = NULL,
beta = NULL,
type = c("II", "I"),
offset = NULL,
weights = NULL,
cens.weights = NULL,
cens.model = ~+1,
se = TRUE,
kaplan.meier = TRUE,
cens.code = 0,
no.opt = FALSE,
method = "nr",
augmentation = NULL,
outcome = c("cif", "rmtl"),
model = c("logit", "exp", "lin"),
Ydirect = NULL,
...
)formula with outcome (see coxph)
data frame
cause of interest (numeric variable)
time of interest
starting values
"II" adds augmentation term, and "I" classical outcome IPCW regression
offsets for partial likelihood
for score equations
censoring weights
only stratified cox model without covariates
to compute se's based on IPCW
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), "rmtl"=E( t- min(T, t) | X)"
logit, exp or lin(ear)
use this Y instead of outcome constructed inside the program, should be a matrix with two column for numerator and denominator.
Additional arguments to lower level funtions
Let the years lost be $$Y1= t- min(T ,) $$ and the years lost due to cause 1 $$Y2= I(epsilon==1) ( t- min(T ,t) $$ , then we model the ratio $$logit( E(Y2 | X)/E(Y1 | X)) = X^T \beta $$. Estimation is based on on binomial regresion IPCW response estimating equation: $$ X ( \Delta^{ipcw}(t) Y2 expit(X^T \beta) - Y1 ) = 0 $$ where $$\Delta^{ipcw}(t) = I((min(t,T)< C)/G_c(min(t,T)-)$$ is IPCW adjustment of the response $$Y(t)= I(T \leq t, \epsilon=1 )$$.
(type="I") sovlves this estimating equation using a stratified Kaplan-Meier for the censoring distribution. For (type="II") the default an additional censoring augmentation term $$X \int E(Y(t)| T>s)/G_c(s) d \hat M_c$$ is added.
The variance is based on the squared influence functions that are also returned as the iid component. naive.var is variance under known censoring model.
Censoring model may depend on strata (cens.model=~strata(gX)).
library(mets)
data(bmt); bmt$time <- bmt$time+runif(408)*0.001
rmst30 <- rmstIPCW(Event(time,cause!=0)~platelet+tcell+age,bmt,time=30,cause=1)
rmst301 <- rmstIPCW(Event(time,cause)~platelet+tcell+age,bmt,time=30,cause=1)
rmst302 <- rmstIPCW(Event(time,cause)~platelet+tcell+age,bmt,time=30,cause=2)
estimate(rmst30)
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) 2.6104 0.05744 2.49777 2.7229 0.000e+00
#> platelet 0.2450 0.08434 0.07971 0.4103 3.672e-03
#> tcell 0.1459 0.11873 -0.08680 0.3786 2.191e-01
#> age -0.1729 0.03794 -0.24723 -0.0985 5.217e-06
estimate(rmst301)
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) 2.4449 0.07291 2.3020 2.5878272 1.617e-246
#> platelet -0.4519 0.16739 -0.7800 -0.1238273 6.940e-03
#> tcell -0.4937 0.25146 -0.9866 -0.0008631 4.960e-02
#> age 0.2747 0.06538 0.1466 0.4028551 2.648e-05
estimate(rmst302)
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) 1.45849 0.1362 1.1915 1.7255 9.623e-27
#> platelet -0.01890 0.2224 -0.4548 0.4170 9.323e-01
#> tcell 0.40759 0.2648 -0.1113 0.9265 1.237e-01
#> age 0.04577 0.1191 -0.1877 0.2792 7.008e-01
## percentage of total cumulative incidence due to cause 1
rmstratioI <- rmstRatio(Event(time,cause)~platelet+tcell+age,bmt,time=30,
cause=1,outcome="rmst")
summary(rmstratioI)
#> n events
#> 408 154
#>
#> 408 clusters
#> coeffients:
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) 0.977110 0.178192 0.627860 1.326361 0.0000
#> platelet -0.412122 0.323292 -1.045763 0.221520 0.2024
#> tcell -0.855519 0.419219 -1.677174 -0.033865 0.0413
#> age 0.217628 0.168419 -0.112468 0.547724 0.1963
#>
#> exp(coeffients):
#> Estimate 2.5% 97.5%
#> (Intercept) 2.65677 1.87360 3.7673
#> platelet 0.66224 0.35142 1.2480
#> tcell 0.42506 0.18690 0.9667
#> age 1.24312 0.89363 1.7293
#>
#>
pp <- predict(rmstratioI,bmt)
ppb <- cbind(pp,bmt)
## percentage of total cumulative incidence due to cause 1
cifratio <- binregRatio(Event(time,cause)~platelet+tcell+age,bmt,time=30,cause=1)
summary(cifratio)
#> n events
#> 408 154
#>
#> 408 clusters
#> coeffients:
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) 0.91179 0.17554 0.56774 1.25585 0.0000
#> platelet -0.46503 0.31924 -1.09072 0.16066 0.1452
#> tcell -1.06112 0.41571 -1.87590 -0.24634 0.0107
#> age 0.21199 0.16115 -0.10386 0.52784 0.1884
#>
#> exp(coeffients):
#> Estimate 2.5% 97.5%
#> (Intercept) 2.48878 1.76427 3.5108
#> platelet 0.62812 0.33597 1.1743
#> tcell 0.34607 0.15322 0.7817
#> age 1.23614 0.90135 1.6953
#>
#>
pp <- predict(cifratio,bmt)
rmstratioI <- binregRatio(Event(time,cause)~platelet+tcell+age,bmt,
time=30,cause=1,outcome="rmst")
summary(rmstratioI)
#> n events
#> 408 154
#>
#> 408 clusters
#> coeffients:
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) 0.977110 0.178192 0.627860 1.326361 0.0000
#> platelet -0.412122 0.323292 -1.045763 0.221520 0.2024
#> tcell -0.855519 0.419219 -1.677174 -0.033865 0.0413
#> age 0.217628 0.168419 -0.112468 0.547724 0.1963
#>
#> exp(coeffients):
#> Estimate 2.5% 97.5%
#> (Intercept) 2.65677 1.87360 3.7673
#> platelet 0.66224 0.35142 1.2480
#> tcell 0.42506 0.18690 0.9667
#> age 1.24312 0.89363 1.7293
#>
#>
pp <- predict(rmstratioI,bmt)
ppb <- cbind(pp,bmt)