View source: R/restricted.mean.R
resmeanIPCW | R Documentation |
Simple and fast version for IPCW regression for just one time-point thus fitting the model
E( min(T, t) | X ) = exp( X^T beta)
or in the case of competing risks data
E( I(epsilon=1) (t - min(T ,t)) | X ) = exp( X^T beta)
thus given years lost to cause.
resmeanIPCW( formula, data, cause = 1, time = NULL, beta = NULL, offset = NULL, weights = NULL, cens.weights = NULL, cens.model = ~+1, se = TRUE, kaplan.meier = TRUE, cens.code = 0, no.opt = FALSE, method = "nr", model = "exp", augmentation = NULL, h = NULL, MCaugment = NULL, Ydirect = NULL, ... )
formula |
formula with outcome (see |
data |
data frame |
cause |
cause of interest |
time |
time of interest |
beta |
starting values |
offset |
offsets for partial likelihood |
weights |
for score equations |
cens.weights |
censoring weights |
cens.model |
only stratified cox model without covariates |
se |
to compute se's based on IPCW |
kaplan.meier |
uses Kaplan-Meier for IPCW in contrast to exp(-Baseline) |
cens.code |
gives censoring code |
no.opt |
to not optimize |
method |
for optimization |
model |
exp or linear |
augmentation |
to augment binomial regression |
h |
h for estimating equation |
MCaugment |
iid of h and censoring model |
Ydirect |
to bypass the construction of the response Y=min(T,tau) and use this instead |
... |
Additional arguments to lower level funtions |
When the status is binary assumes it is a survival setting and default is to consider outcome Y=min(T,t), if status has more than two levels, then computes years lost due to that particular cause, thus
Based on binomial regresion IPCW response estimating equation:
X ( Δ (min(T , t))/G_c(min(T_i,t)) - exp( X^T beta)) = 0
for IPCW adjusted responses. Here
Δ(min(T,t)) I ( min(T ,t) ≤q C )
is indicator of being uncensored.
Can also solve the binomial regresion IPCW response estimating equation:
h(X) X ( Δ (min(T, t))/G_c(min(T_i,t)) - exp( X^T beta)) = 0
for IPCW adjusted responses where $h$ is given as an argument together with iid of censoring with h.
By using appropriately the h argument we can also do the efficient IPCW estimator estimator.
Variance is based on
∑ w_i^2
also with IPCW adjustment, and naive.var is variance under known censoring model.
When Ydirect is given it solves :
X ( Δ( min(T,t)) Ydirect /G_c(min(T_i,t)) - exp( X^T beta)) = 0
for IPCW adjusted responses.
Censoring model may depend on strata.
Thomas Scheike
data(bmt); bmt$time <- bmt$time+runif(nrow(bmt))*0.001 # E( min(T;t) | X ) = exp( a+b X) with IPCW estimation out <- resmeanIPCW(Event(time,cause!=0)~tcell+platelet+age,bmt, time=50,cens.model=~strata(platelet),model="exp") summary(out) ### same as Kaplan-Meier for full censoring model bmt$int <- with(bmt,strata(tcell,platelet)) out <- resmeanIPCW(Event(time,cause!=0)~-1+int,bmt,time=30, cens.model=~strata(platelet,tcell),model="lin") estimate(out) out1 <- phreg(Surv(time,cause!=0)~strata(tcell,platelet),data=bmt) rm1 <- resmean.phreg(out1,times=30) summary(rm1) ## competing risks years-lost for cause 1 out <- resmeanIPCW(Event(time,cause)~-1+int,bmt,time=30,cause=1, cens.model=~strata(platelet,tcell),model="lin") estimate(out) ## same as integrated cumulative incidence rmc1 <- cif.yearslost(Surv(time,cause!=0)~cause+strata(tcell,platelet),data=bmt,times=30) summary(rmc1)
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