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mets (version 0.2.8)

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). Also contains two-stage binomial modelling that can do pairwise odds-ratio dependence modelling based marginal logistic regression models. This is an alternative to the alternating logistic regression approach (ALR).

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Version

Install

install.packages('mets')

Monthly Downloads

19,799

Version

0.2.8

License

GPL (>= 2)

Maintainer

Klaus K Holst

Last Published

May 13th, 2014

Functions in mets (0.2.8)

Aalen frailty model

Concordance Computes concordance and probandwise concordance

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

Prostate data set

Inverse Probability of Censoring Weights

Analysis of Multivariate Events

Menarche data set

Convert to timereg object

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

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

For internal use

Concordance test Compares two concordance estimates

Random effects model for competing risks data

Dermal ridges data (monozygotic twins)

Summary for dependence models for competing risks

Classic twin model for quantitative traits

Simulate twin data

lifetable.matrix

Stutter data set

printcasewisetest

prints Concordance test

Liability model for twin data

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.

Additive Random effects model for competing risks data for polygenetic modelling

simClaytonOakesWei

Simulate from the Clayton-Oakes frailty model

Fast approximation

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

Clayton-Oakes model with piece-wise constant hazards

simClaytonOakes

Simulate from the Clayton-Oakes frailty model

Derivatives of the bivariate normal cumulative distribution function

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

Dermal ridges data (families)

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.

simAalenFrailty

Simulate from the Aalen Frailty model

Fast Cox PH regression

Estimation of concordance in bivariate competing risks data

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.

Multivariate Cumulative Incidence Function example data set