riskRegression (version 0.0.7)

riskRegression: Risk Regression

Description

Fits a regression model for the risk of an event -- allowing for competing risks.

Usage

ARR(formula, data, times,cause,cens.model,cens.formula,...)
LRR(formula, data, times,cause,cens.model,cens.formula,...)
riskRegression(formula,
	 data,
	 times,
	 link = "relative",
	 cause,
	 confint = TRUE,
	 cens.model,
	 cens.formula,
	 numSimu = 0,
	 maxiter = 50,
	 silent = 1,
	 convLevel = 6,
	 ...)

Arguments

formula

Formula where the left hand side specifies the event history response and the right hand side the linear predictor. See examples.

data

The data for fitting the model in which includes all the variables included in formula.

times

Vector of times. For each time point in times estimate the baseline risk and the timevarying coefficients.

link

"relative" for the absolute risk regression model. "logistic" for the logistic risk regression model. "prop" for the Fine-Gray regression model.

cause

The cause of interest.

confint

If TRUE return the iid decomposition, that can be used to construct confidence bands for predictions.

cens.model

Specified the model for the (conditional) censoring distribution used for deriving weights (ICPW). Defaults to "KM" (the Kaplan-Meier method ignoring covariates) alternatively it may be "Cox" (Cox regression).

cens.formula

Right hand side of the formula used for fitting the censoring model. If not specified the right hand side of formula is used.

numSimu

Number of simulations in resampling.

silent

Set this to 0 to see some system messages during fitting.

maxiter

Maximal number of iterations.

convLevel

Integer between 1 and 10, specifying the convergence criterion for the fitting algorithm as 10^{-convLevel}.

...

Not used.

Details

This is a sister of the function comp.risk (timereg package).

References

Gerds, TA and Scheike, T and Andersen, PK (2011) Absolute risk regression for competing risks: interpretation, link functions and prediction Research report 11/8. Department of Biostatistics, University of Copenhagen

Scheike, Zhang and Gerds (2008), Predicting cumulativeincidence 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(Melanoma)
## tumor thickness on the log-scale
Melanoma$logthick <- log(Melanoma$thick)

# {{{ Single binary factor

## absolute risk regression 
fit.arr <- ARR(Hist(time,status)~sex,data=Melanoma,cause=1)
print(fit.arr)
# show predicted cumulative incidences
plot(fit.arr,col=3:4,newdata=data.frame(sex=c("Female","Male")))

## compare with non-parametric Aalen-Johansen estimate
library(prodlim)
fit.aj <- prodlim(Hist(time,status)~sex,data=Melanoma)
plot(fit.aj,confint=FALSE)
plot(fit.arr,add=TRUE,col=3:4,newdata=data.frame(sex=c("Female","Male")))

## with time-dependent effect
fit.tarr <- ARR(Hist(time,status)~strata(sex),data=Melanoma,cause=1)
plot(fit.tarr,newdata=data.frame(sex=c("Female","Male")))

## logistic risk regression
fit.lrr <- LRR(Hist(time,status)~sex,data=Melanoma,cause=1)
summary(fit.lrr)

# }}}

# {{{ Single continuous factor

## tumor thickness on the log-scale
Melanoma$logthick <- log(Melanoma$thick)

## absolute risk regression 
fit2.arr <- ARR(Hist(time,status)~logthick,data=Melanoma,cause=1)
print(fit2.arr)
# show predicted cumulative incidences
plot(fit2.arr,col=1:5,newdata=data.frame(logthick=quantile(Melanoma$logthick)))

## comparison with nearest neighbor non-parametric Aalen-Johansen estimate
library(prodlim)
fit2.aj <- prodlim(Hist(time,status)~logthick,data=Melanoma)
plot(fit2.aj,confint=FALSE,newdata=data.frame(logthick=quantile(Melanoma$logthick)))
plot(fit2.arr,add=TRUE,col=1:5,lty=3,newdata=data.frame(logthick=quantile(Melanoma$logthick)))

## logistic risk regression
fit2.lrr <- LRR(Hist(time,status)~logthick,data=Melanoma,cause=1)
summary(fit2.lrr)

## change model for censoring weights
fit2a.lrr <- LRR(Hist(time,status)~logthick,data=Melanoma,cause=1,cens.model="cox",cens.formula=~sex+epicel+ulcer+age+logthick)
summary(fit2a.lrr)

##  compare prediction errors
library(pec
plot(pec(list(ARR=fit2.arr,AJ=fit2.aj,LRR=fit2.lrr),data=Melanoma,maxtime=3000))
# }}}

# {{{ multiple regression

# absolute risk model
multi.arr <- ARR(Hist(time,status)~logthick+sex+age+ulcer,data=Melanoma,cause=1)

# stratified model allowing different baseline risk for the two gender
multi.arr <- ARR(Hist(time,status)~thick+strata(sex)+age+ulcer,data=Melanoma,cause=1)

# stratify by a continuous variable
multi.arr <- ARR(Hist(time,status)~tp(thick,power=1)+strata(sex)+age+ulcer,data=Melanoma,cause=1)

fit.arr2a <- ARR(Hist(time,status)~const(thick,power=1),data=Melanoma,cause=1)
fit.arr2b <- ARR(Hist(time,status)~timevar(thick),data=Melanoma,cause=1)
summary(fit.arr)

## logistic risk model
fit.lrr <- LRR(Hist(time,status)~thick,data=Melanoma,cause=1)
summary(fit.lrr)

# }}}



## nearest neighbor non-parametric Aalen-Johansen estimate
library(prodlim)
fit.aj <- prodlim(Hist(time,status)~thick,data=Melanoma)
plot(fit.aj,confint=FALSE)

# prediction error
library(pec)
x <- pec(list(fit.arr2,fit.arr2a,fit.arr2b,fit.lrr),data=Melanoma,formula=Hist(time,status)~1,cause=1,B=10,splitMethod="none")