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mets (version 0.2.6)

Analysis of Multivariate Event Times

Description

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

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Install

install.packages('mets')

Monthly Downloads

19,799

Version

0.2.6

License

GPL (>= 2)

Maintainer

Klaus K Holst

Last Published

December 17th, 2013

Functions in mets (0.2.6)

Derivatives of the bivariate normal cumulative distribution function

Multivariate Cumulative Incidence Function example data set

binomial.twostage

Fits Clayton-Oakes or bivariate Plackett (OR) models for binary data using marginals that are on logistic form. If clusters contain more than two times, the algoritm uses a compososite likelihood based on all pairwise bivariate models.

Aalen frailty model

Cross-odds-ratio, OR or RR risk regression for competing risks

Estimates the casewise concordance based on Concordance and marginal estimate using prodlim but no testing

Fast approximation

Estimation of concordance in bivariate competing risks data

For internal use

Menarche data set

Concordance test Compares two concordance estimates

Fits two-stage model for describing depdendence in survival data using marginals that are on cox or aalen form using the twostage funcion, but call is different and easier and the data manipulation build into the function. Useful in particular for family design data.

Clayton-Oakes model with piece-wise constant hazards

Estimates the casewise concordance based on Concordance and marginal estimate using timereg and performs test for independence

simClaytonOakes

Simulate from the Clayton-Oakes frailty model

Fast Cox PH regression

Stutter data set

Classic twin model for quantitative traits

Dermal ridges data (monozygotic twins)

plack Computes concordance for or.cif based model, that is Plackett random effects model

printcasewisetest

prints Concordance test

Liability model for twin data

Concordance Computes concordance and probandwise concordance

Summary for dependence models for competing risks

easy.binomial.twostage

Fits two-stage binomial for describing depdendence in binomial data using marginals that are on logistic form using the binomial.twostage funcion, but call is different and easier and the data manipulation is build into the function. Useful in particular for family design data.

Fast reshape Simple reshape/tranpose of data

Prostate data set

Random effects model for competing risks data

Analysis of Multivariate Events

Dermal ridges data (families)

lifetable.matrix

simClaytonOakesWei

Simulate from the Clayton-Oakes frailty model

Additive Random effects model for competing risks data for polygenetic modelling

simAalenFrailty

Simulate from the Aalen Frailty model

Simulate twin data

Convert to timereg object

Fits Clayton-Oakes or bivariate Plackett models for bivariate survival data using marginals that are on Cox or addtive form. If clusters contain more than two times, the algoritm uses a compososite likelihood based on the pairwise bivariate models.

Liability model for twin data

Inverse Probability of Censoring Weights