prep.comp.risk | R Documentation |
Computes the weights of Geskus (2011) modified to the setting of the comp.risk function. The returned weights are 1/(H(T_i)*G_c(min(T_i,tau))) and tau is the max of the times argument, here H is the estimator of the truncation distribution and G_c is the right censoring distribution.
prep.comp.risk( data, times = NULL, entrytime = NULL, time = "time", cause = "cause", cname = "cweight", tname = "tweight", strata = NULL, nocens.out = TRUE, cens.formula = NULL, cens.code = 0, prec.factor = 100, trunc.mintau = FALSE )
data |
data frame for comp.risk. |
times |
times for estimating equations. |
entrytime |
name of delayed entry variable, if not given computes right-censoring case. |
time |
name of survival time variable. |
cause |
name of cause indicator |
cname |
name of censoring weight. |
tname |
name of truncation weight. |
strata |
strata variable to obtain stratified weights. |
nocens.out |
returns only uncensored part of data-frame |
cens.formula |
censoring model formula for Cox models for the truncation and censoring model. |
cens.code |
code for censoring among causes. |
prec.factor |
precision factor, for ties between censoring/even times, truncation times/event times |
trunc.mintau |
specicies wether the truncation distribution is evaluated in death times or death times minimum max(times), FALSE makes the estimator equivalent to Kaplan-Meier (in the no covariate case). |
Returns an object. With the following arguments:
dataw |
a data.frame with weights. |
The function wants to make two new variables "weights" and "cw" so if these already are in the data frame it tries to add an "_" in the names.
Thomas Scheike
Geskus (2011), Cause-Specific Cumulative Incidence Estimation and the Fine and Gray Model Under Both Left Truncation and Right Censoring, Biometrics (2011), pp 39-49.
Shen (2011), Proportional subdistribution hazards regression for left-truncated competing risks data, Journal of Nonparametric Statistics (2011), 23, 885-895
data(bmt) nn <- nrow(bmt) entrytime <- rbinom(nn,1,0.5)*(bmt$time*runif(nn)) bmt$entrytime <- entrytime times <- seq(5,70,by=1) ### adds weights to uncensored observations bmtw <- prep.comp.risk(bmt,times=times,time="time", entrytime="entrytime",cause="cause") ######################################### ### nonparametric estimates ######################################### ## {{{ ### nonparametric estimates, right-censoring only out <- comp.risk(Event(time,cause)~+1,data=bmt, cause=1,model="rcif2", times=c(5,30,70),n.sim=0) out$cum ### same as ###out <- prodlim(Hist(time,cause)~+1,data=bmt) ###summary(out,cause="1",times=c(5,30,70)) ### with truncation out <- comp.risk(Event(time,cause)~+1,data=bmtw,cause=1, model="rcif2", cens.weight=bmtw$cw,weights=bmtw$weights,times=c(5,30,70), n.sim=0) out$cum ### same as ###out <- prodlim(Hist(entry=entrytime,time,cause)~+1,data=bmt) ###summary(out,cause="1",times=c(5,30,70)) ## }}} ######################################### ### Regression ######################################### ## {{{ ### with truncation correction out <- comp.risk(Event(time,cause)~const(tcell)+const(platelet),data=bmtw, cause=1,cens.weight=bmtw$cw, weights=bmtw$weights,times=times,n.sim=0) summary(out) ### with only righ-censoring, standard call outn <- comp.risk(Event(time,cause)~const(tcell)+const(platelet),data=bmt, cause=1,times=times,n.sim=0) summary(outn) ## }}}
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