Analysis of Multivariate Event Times
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DESCRIPTION
NAMESPACE
NEWS
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README.org

Multivariate Event Times (mets)

Implementation of various statistical models for multivariate event history data. Including multivariate cumulative incidence models, and bivariate random effects probit models (Liability models)

Installation

install.packages("mets",dependencies=TRUE)

The development version may be installed directly from github (requires Rtools on windows and development tools (+Xcode) for Mac OS X):

devtools::install_github("kkholst/mets")

or download the source

git@github.com:kkholst/mets.git

Citation

To cite the mets package please use one of the following references

Klaus K. Holst and Thomas H. Scheike Jacob B. Hjelmborg (2015). The Liability Threshold Model for Censored Twin Data. Computational Statistics and Data Analysis. http://dx.doi.org/10.1016/j.csda.2015.01.014

Thomas H. Scheike and Klaus K. Holst and Jacob B. Hjelmborg (2013). Estimating heritability for cause specific mortality based on twin studies. Lifetime Data Analysis. http://dx.doi.org/10.1007/s10985-013-9244-x

BibTeX:

@Article{,
  title={The Liability Threshold Model for Censored Twin Data},
  author={Holst, Klaus K. and Scheike, Thomas H. and Hjelmborg, Jacob B.},
  year={2015},
  doi={10.1016/j.csda.2015.01.014},
  url={http://dx.doi.org/10.1016/j.csda.2015.01.014},
  journal={Computational Statistics and Data Analysis}
}


@Article{,
  title={Estimating heritability for cause specific mortality based on twin studies},
  author={Scheike, Thomas H. and Holst, Klaus K. and Hjelmborg, Jacob B.},
  year={2013},
  issn={1380-7870},
  journal={Lifetime Data Analysis},
  doi={10.1007/s10985-013-9244-x},
  url={http://dx.doi.org/10.1007/s10985-013-9244-x},
  publisher={Springer US},
  keywords={Cause specific hazards; Competing risks; Delayed entry; 
            Left truncation; Heritability; Survival analysis},
  pages={1-24},
  language={English}
}

Examples

library(mets)

data(prt) ## Prostate data example (sim)

## Bivariate competing risk, concordance estimates
p33 <- bicomprisk(Hist(time,status)~strata(zyg)+id(id),
                  data=prt,cause=c(2,2),return.data=1,prodlim=TRUE)

p33dz <- p33$model$"DZ"$comp.risk
p33mz <- p33$model$"MZ"$comp.risk

## Probability weights based on Aalen's additive model 
prtw <- ipw(Surv(time,status==0)~country, data=prt,
            cluster="id",weightname="w")


## Marginal model (wrongly ignoring censorings)
bpmz <- biprobit(cancer~1 + cluster(id), 
                 data=subset(prt,zyg=="MZ"), eqmarg=TRUE)

## Extended liability model
bpmzIPW <- biprobit(cancer~1 + cluster(id), 
                    data=subset(prtw,zyg=="MZ"), 
                    weight="w")
smz <- summary(bpmzIPW)

## Concordance
plot(p33mz,ylim=c(0,0.1),axes=FALSE,automar=FALSE,atrisk=FALSE)
axis(2); axis(1)
  
abline(h=smz$prob["Concordance",],lwd=c(2,1,1),col="darkblue")
## Wrong estimates:
abline(h=summary(bpmz)$prob["Concordance",],lwd=c(2,1,1),col="lightgray")

inst/misc/mets1.png