View source: R/predict-timereg.r
predict.timereg | R Documentation |
Make predictions based on the survival models (Aalen and Cox-Aalen) and the competing risks models for the cumulative incidence function (comp.risk). Computes confidence intervals and confidence bands based on resampling.
## S3 method for class 'timereg' predict( object, newdata = NULL, X = NULL, times = NULL, Z = NULL, n.sim = 500, uniform = TRUE, se = TRUE, alpha = 0.05, resample.iid = 0, ... )
object |
an object belonging to one of the following classes: comprisk, aalen or cox.aalen |
newdata |
specifies the data at which the predictions are wanted. |
X |
alternative to newdata, specifies the nonparametric components for predictions. |
times |
times in which predictions are computed, default is all time-points for baseline |
Z |
alternative to newdata, specifies the parametric components of the model for predictions. |
n.sim |
number of simulations in resampling. |
uniform |
computes resampling based uniform confidence bands. |
se |
computes pointwise standard errors |
alpha |
specificies the significance levelwhich cause we consider. |
resample.iid |
set to 1 to return iid decomposition of estimates, 3-dim matrix (predictions x times x subjects) |
... |
unused arguments - for S3 compatability |
time |
vector of time points where the predictions are computed. |
unif.band |
resampling based constant to construct 95% uniform confidence bands. |
model |
specifies what model that was fitted. |
alpha |
specifies the significance level for the confidence intervals. This relates directly to the constant given in unif.band. |
newdata |
specifies the newdata given in the call. |
RR |
gives relative risk terms for Cox-type models. |
call |
gives call for predict funtion. |
initial.call |
gives call for underlying object used for predictions. |
P1 |
gives cumulative inicidence predictions for competing risks models. Predictions given in matrix form with different subjects in different rows. |
S0 |
gives survival predictions for survival models. Predictions given in matrix form with different subjects in different rows. |
se.P1 |
pointwise standard errors for predictions of P1. |
se.S0 |
pointwise standard errors for predictions of S0. |
Thomas Scheike, Jeremy Silver
Scheike, Zhang and Gerds (2008), Predicting cumulative incidence probability by direct binomial regression, Biometrika, 95, 205-220.
Scheike and Zhang (2007), Flexible competing risks regression modelling and goodness of fit, LIDA, 14, 464-483 .
Martinussen and Scheike (2006), Dynamic regression models for survival data, Springer.
data(bmt); ## competing risks add<-comp.risk(Event(time,cause)~platelet+age+tcell,data=bmt,cause=1) ndata<-data.frame(platelet=c(1,0,0),age=c(0,1,0),tcell=c(0,0,1)) out<-predict(add,newdata=ndata,uniform=1,n.sim=1000) par(mfrow=c(2,2)) plot(out,multiple=0,uniform=1,col=1:3,lty=1,se=1) # see comp.risk for further examples. add<-comp.risk(Event(time,cause)~factor(tcell),data=bmt,cause=1) summary(add) out<-predict(add,newdata=ndata,uniform=1,n.sim=1000) plot(out,multiple=1,uniform=1,col=1:3,lty=1,se=1) add<-prop.odds.subdist(Event(time,cause)~factor(tcell), data=bmt,cause=1) out <- predict(add,X=1,Z=1) plot(out,multiple=1,uniform=1,col=1:3,lty=1,se=1) ## SURVIVAL predictions aalen function data(sTRACE) out<-aalen(Surv(time,status==9)~sex+ diabetes+chf+vf, data=sTRACE,max.time=7,n.sim=0,resample.iid=1) pout<-predict(out,X=rbind(c(1,0,0,0,0),rep(1,5))) head(pout$S0[,1:5]); head(pout$se.S0[,1:5]) par(mfrow=c(2,2)) plot(pout,multiple=1,se=0,uniform=0,col=1:2,lty=1:2) plot(pout,multiple=0,se=1,uniform=1,col=1:2) out<-aalen(Surv(time,status==9)~const(age)+const(sex)+ const(diabetes)+chf+vf, data=sTRACE,max.time=7,n.sim=0,resample.iid=1) pout<-predict(out,X=rbind(c(1,0,0),c(1,1,0)), Z=rbind(c(55,0,1),c(60,1,1))) head(pout$S0[,1:5]); head(pout$se.S0[,1:5]) par(mfrow=c(2,2)) plot(pout,multiple=1,se=0,uniform=0,col=1:2,lty=1:2) plot(pout,multiple=0,se=1,uniform=1,col=1:2) pout<-predict(out,uniform=0,se=0,newdata=sTRACE[1:10,]) plot(pout,multiple=1,se=0,uniform=0) #### cox.aalen out<-cox.aalen(Surv(time,status==9)~prop(age)+prop(sex)+ prop(diabetes)+chf+vf, data=sTRACE,max.time=7,n.sim=0,resample.iid=1) pout<-predict(out,X=rbind(c(1,0,0),c(1,1,0)),Z=rbind(c(55,0,1),c(60,1,1))) head(pout$S0[,1:5]); head(pout$se.S0[,1:5]) par(mfrow=c(2,2)) plot(pout,multiple=1,se=0,uniform=0,col=1:2,lty=1:2) plot(pout,multiple=0,se=1,uniform=1,col=1:2) pout<-predict(out,uniform=0,se=0,newdata=sTRACE[1:10,]) plot(pout,multiple=1,se=0,uniform=0) #### prop.odds model add<-prop.odds(Event(time,cause!=0)~factor(tcell),data=bmt) out <- predict(add,X=1,Z=0) plot(out,multiple=1,uniform=1,col=1:3,lty=1,se=1)
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