riskRegression: riskRegression – R documentation

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Risk Regression Fits a regression model for the risk of an event – allowing for competing risks.

Description

This is a wrapper for the function comp.risk from the timereg package. The main difference is one marks variables in the formula that should have a time-dependent effect whereas in comp.risk one marks variables that should have a time constant (proportional) effect.

Usage

riskRegression(
  formula,
  data,
  times,
  link = "relative",
  cause,
  conf.int = TRUE,
  cens.model,
  cens.formula,
  max.iter = 50,
  conservative = TRUE,
  ...
)

Arguments

`formula`	Formula where the left hand side specifies the event history event.history 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.
`conf.int`	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 (IFPW). 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.
`max.iter`	Maximal number of iterations.
`conservative`	If `TRUE` use variance formula that ignores the contribution by the estimate of the inverse of the probability of censoring weights
`...`	Further arguments passed to `comp.risk`

Author(s)

Thomas A. Gerds tag@biostat.ku.dk, Thomas H. Scheike ts@biostat.ku.dk

References

Thomas A Gerds, Thomas H Scheike, and Per K Andersen. Absolute risk regression for competing risks: interpretation, link functions, and prediction. Statistics in medicine, 31(29):3921–3930, 2012.

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

library(prodlim)
data(Melanoma,package="riskRegression")
## tumor thickness on the log-scale
Melanoma$logthick <- log(Melanoma$thick)

# Single binary factor

## absolute risk regression
library(survival)
library(prodlim)
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,conf.int=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,conf.int=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
library(rms)
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 performance
Score(list(ARR=fit2.arr,AJ=fit2.aj,LRR=fit2.lrr),formula=Hist(time,status)~1,data=Melanoma)


# multiple regression
library(riskRegression)
library(prodlim)
# 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: strata(age)
multi.arr <- ARR(Hist(time,status)~tp(thick,power=0)+strata(age)+sex+ulcer,
                 data=Melanoma,
                 cause=1)

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

## 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,conf.int=FALSE)

# prediction performance
x <- Score(list(fit.arr2a,fit.arr2b,fit.lrr),
             data=Melanoma,
             formula=Hist(time,status)~1,
             cause=1,
             split.method="none")

riskRegression

Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

v2020.12.08

GPL (>= 2)

Authors

Thomas Alexander Gerds [aut, cre], Paul Blanche [ctb], Rikke Mortensen [ctb], Marvin Wright [ctb], Nikolaj Tollenaar [ctb], John Muschelli [ctb], Ulla Brasch Mogensen [ctb], Brice Ozenne [aut] (<https://orcid.org/0000-0001-9694-2956>)

Initial release

riskRegression

Description

Usage

Arguments

Author(s)

References

Examples

riskRegression

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