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Last data update: 2014.03.03

R: Create survival curves based on the IDP model

isurvfit

R Documentation

Create survival curves based on the IDP model

Description

This function creates survival curves from right censored data using the prior near-ignorance Dirichlet Process (IDP).

Usage

isurvfit(formula, data, s=0.5, weights, subset, display=TRUE,
         conf.type=c('exact',  'approx', 'none'), nsamples=10000,
         conf.int= .95)

Arguments

`formula`	a formula object, which must have a `Surv` object as the response on the left of the `~` operator and, if desired, terms separated by + operators on the right. For a single survival curve the right hand side should be `~ 1`.
`data`	a data frame in which to interpret the variables named in the `formula`, `subset` and `weights` arguments.
`s`	sets the value of the prior strength s of the Dirichlet Process.
`weights`	the weights must be finite and nonnegative; it is strongly recommended that they be strictly positive, since zero weights are ambiguous, compared to use of the `subset` argument.
`subset`	expression saying that only a subset of the rows of the data should be used in the fit.
`display`	determines whether the survival curves have to be plotted (TRUE) or not (FALSE).
`conf.type`	a variable saying how the credible interval shold be computed: 'exact': Monte-Carlo smapling from the exact distribution, 'approx': Gaussian approximation, 'none': no credible interval is computed.
`nsamples`	number pf samples used to approximate the credible intervals if `conf.type='exact'`.
`conf.int`	confidence level of the credible interval.

Details

The estimates are obtained using the IDP estimator by Mangili and others (2014) based on the prior near-ignorance Dirichlet Process model by Benavoli and others (2014).

Value

an object of class "isurvfit".

See isurvfit.object for details. Methods defined for survfit objects are print and plot.

References

Benavoli, A., Mangili, F., Zaffalon, M. and Ruggeri, F. (2014). Imprecise Dirichlet process with application to the hypothesis test on the probability that X < Y. ArXiv e-prints, http://adsabs.harvard.edu/abs/2014arXiv1402.2755B.

Mangili, F., Benavoli, A., Zaffalon, M. and de Campos, C. (2014). Imprecise Dirichlet Process for the estimate and comparison of survival functions with censored data.

Examples

data(aml)
fit <- isurvfit(Surv(time, cens) ~ 1, data=aml, display=TRUE, nsamples=1000) 
legend('topright', c("Lower expectation", 
          "Upper expectation","confidence intervals"), lty=c(1,1,2),lwd=c(1,2,1)) 
title("IDP survival curve (s=0.5) \nAcute Myelogenous Leukemia dataset")

data(Aids2)
dataset <- Aids2
dataset["time"]<-dataset[4]-dataset[3]
dataset[5]<-as.numeric(unlist(dataset[5]))
fit <- isurvfit(Surv(time, status) ~ T.categ, dataset,s=1,
	            subset=(!is.na(match(T.categ, c('blood','haem','het')))),
                nsamples=1000,conf.type='none')
legend('topright',c("Heterosexual contact","Hemophilia","Blood"),
            title="Transmission category:",lty=c(1,1,1),col=c(1,2,3),pch=c(1,2,3))
title("IDP survival curve (s=1) \nAids dataset")
print(fit)

leukemia.surv <- isurvfit(Surv(time, cens) ~ group, data = aml, display=FALSE) 
plot(leukemia.surv) 
legend(100, .9, c("Maintenance", "No Maintenance"), lty=c(1,1),lwd=c(2,1),
       col=c('black','red'),pch=c(1,2)) 
title("IDP Curves\nfor AML Maintenance Study")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(IDPSurvival)
Loading required package: Rsolnp
Loading required package: gtools
Loading required package: survival
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/IDPSurvival/isurvfit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: isurvfit
> ### Title: Create survival curves based on the IDP model
> ### Aliases: isurvfit print.isurvfit
> ### Keywords: survival IDP
> 
> ### ** Examples
> 
> data(aml)
> fit <- isurvfit(Surv(time, cens) ~ 1, data=aml, display=TRUE, nsamples=1000) 
> legend('topright', c("Lower expectation", 
+           "Upper expectation","confidence intervals"), lty=c(1,1,2),lwd=c(1,2,1)) 
> title("IDP survival curve (s=0.5) \nAcute Myelogenous Leukemia dataset")
> 
> data(Aids2)
> dataset <- Aids2
> dataset["time"]<-dataset[4]-dataset[3]
> dataset[5]<-as.numeric(unlist(dataset[5]))
> fit <- isurvfit(Surv(time, status) ~ T.categ, dataset,s=1,
+ 	            subset=(!is.na(match(T.categ, c('blood','haem','het')))),
+                 nsamples=1000,conf.type='none')
> legend('topright',c("Heterosexual contact","Hemophilia","Blood"),
+             title="Transmission category:",lty=c(1,1,1),col=c(1,2,3),pch=c(1,2,3))
> title("IDP survival curve (s=1) \nAids dataset")
> print(fit)
              n.records n.events n.censored
T.categ=het          41       17         24
T.categ=haem         46       29         17
T.categ=blood        78       76         18
> 
> leukemia.surv <- isurvfit(Surv(time, cens) ~ group, data = aml, display=FALSE) 
> plot(leukemia.surv) 
> legend(100, .9, c("Maintenance", "No Maintenance"), lty=c(1,1),lwd=c(2,1),
+        col=c('black','red'),pch=c(1,2)) 
> title("IDP Curves\nfor AML Maintenance Study") 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>