recEvFit: Left-censored recurrent events survival models
In miRecSurv: Left-Censored Recurrent Events Survival Models

Description Usage Arguments Value Author(s) See Also Examples

View source: R/recEvFit.R

The function allows the user to fit recurrent events survival models.

1 2	recEvFit(formula, data, id, prevEp, riskBef, oldInd, frailty=FALSE, m=5, seed=NA, ...)

`formula`	a formula object, with the response on the left of a `~` operator, and the terms on the right. The response must be a survival object as returned by the Surv function.
`data`	a data.frame in which to interpret the variables named in the formula.
`id`	subject identifier.
`prevEp`	known previous episodes.
`riskBef`	indicator for new individual in the cohort (`riskBef==FALSE`) or subject who was at risk before the start of follow-up (`riskBef==TRUE`).
`oldInd`	time an individual has been at risk prior to the follow-up.
`frailty`	should the model include a frailty term. Defaults to `FALSE`.
`m`	number of multiple imputations. The default is `m=5.`
`seed`	an integer that is used as argument by the `set.seed` function for offsetting the random number generator. Default is to leave the random number generator alone.
`...`	extra arguments to pass to `coxph`.

A list with seven elements:

`fit`	a list with all the `coxph` objects fitted for each imputed dataset.
`coeff`	a list with the vectors of coefficients from the models fitted to each imputed dataset
`loglik`	a list with the loglikelihood for each model fitted.
`vcov`	a list with the variance-covariance matrices for the parameters fitted for each of the imputed datasets.
`AIC`	a list with the AIC of each of the models fitted.
`CMP`	summary tables of the fitted COMPoisson models used for imputing missing values
`data.impute`	the original dataset with the multiple imputed variables as final columns.

David Moriña (University of Barcelona), Gilma Hernández-Herrera (Universidad de Antioquía), Albert Navarro (Universitat Autònoma de Barcelona)

Mantainer: David Moriña <dmorina@ub.edu>

miRecSurv-package

data(sim.data)
fit <- recEvFit(Surv(start2, stop2, status)~x+x.1+x.2, data=sim.data,
                id="nid", prevEp = "obs.episode",
                riskBef = "risk.bef", oldInd = "old", frailty=FALSE, m=5)
summary(fit)