R: Predictions for Survival and Competings Risks Regression for...

predict.timereg {timereg} | R Documentation

## Predictions for Survival and Competings Risks Regression for timereg

### Description

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.

### Usage

    ## 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, ...)

### Arguments

`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

### Value

`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.

### Author(s)

Thomas Scheike, Jeremy Silver

### References

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.

### Examples

    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)

[Package timereg version 1.9.4 Index]