bivrec: Survival analysis for bivariate recurrent event processes...
In blupsurv: Recurrent events with BLUPs

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/bivmethods.r

Tools for fitting proportional hazards models to clustered bivariate recurrent events data. Nested frailties are modeled by their best linear unbiased predictors under an auxiliary Poisson model.

1 2	## S3 method for class 'formula' bivrec(formula, data = parent.frame(), ...)

`formula`	See details
`data`	See details
`...`	See details

Supported parameters are:

bivrec(formula, data = parent.frame(), K1 = 10, K2 = 10,
        excludevars1 = NULL, excludevars2 = NULL, verbose = 1, 
        alternating = FALSE, dispest = "pearson", correction = "none", 
        computesd = TRUE, fullS = TRUE, fixzero = NULL, smooth = FALSE, 
        maxiter = 200, convergence = 1e-3, initial = NULL)

formula: a formula object, similar to coxph. The response to the left of the ~ should be a survival object generated by Surv2. The right side must contain an id(x) term, where x is a variable that takes a unique value for each subject within a cluster. It may also contain a cluster(y) term if y is the variable that indicates cluster membership, and a strata(z) term.
data: a data.frame with columns corresponding to the terms in the formula.
K1: either an integer, a vector of integers, or a value between 0 and 1, to determine the level of discretization. If it is an integer, K1 gives the number of breakpoints in the baseline hazard for the first process. If it is a vector of integers, K1 should have length equal to the number of strata, and each value gives the number of breakpoints in the baseline hazard for each stratum. If it is a number between 0 and 1, it gives the ratio of the number of breakpoints relative to the maximum possible. Defaults to 10.
K2: analogous to K1, for the second process.
excludevars1: a vector of strings giving the names of variables or interactions that should be excluded from the model for the first process.
excludevars2: analogous to excludevars1 for the second process.
verbose: an integer from 0 to 3 that determines the quantity of output printed to the screen. Setting verbose=0 is completely silent.
alternating: logical, describing the at-risk function for the model. If FALSE, patients are assumed to be constantly at risk for both processes, if TRUE, they are only at-risk for one process at a time. Defaults to FALSE.
dispest: a string, determining the method used to estimate the dispersion parameters. Possible values are "pearson", "marginal", "ohlsson", defaults to "pearson".
correction: a string describing the degree-of-freedom correction proposed by Ma. It only applies when dispest="pearson". Possible values are "single", "double", "none", defaults to "none"
computesd: a boolean determining whether standard errors should be computed. Defaults to TRUE.
fullS: logical determining whether to use the full sensitivity matrix in computing standard errors, or only the covariate portion. Using fullS=TRUE leads to more accurate results but increases computer time. Defaults to TRUE.
fixzero: a vector of strings, listing any dispersion parameters that should be set to 0. Can contain any subset of "clust1", "clust2", "subj1", "subj2", "cov", all else is ignored. Defaults to NULL.
smooth: logical, determines whether the baseline hazard should be smoothed at each iteration. Defaults to FALSE.
maxiter: an integer giving the maximum number of iterations permitted.
convergence: double, determines the value of the convergence criterion required at termination.
initial: a list of initial values in the format used by the code internally.

An object of class bivrec with the following components:

`call`	the original call to the model-fitting function
`regression`	a list containing results from the regression fit in the last iteration. It has components `coefficients1` a vector of regression coefficients for the first process `coefficients2` a vector of regression coefficients for the second process `loglik1` conditional loglikelihood for the first process. `loglik2` conditional loglikelihood for the second process.
`frailty`	a list containing results from the frailty estimation in the last iteration. It has components `clust1` cluster frailties for process 1 `clust2` cluster frailties for process 2 `subj1` subject frailties for process 1 `subj2` subject frailties for process 2
`dispersion`	a list containing results from the dispersion parameter estimation in the last iteration. It has components `clust1` cluster frailty variance for process 1 `clust2` cluster frailty variance for process 2 `subj1` subject frailty variance for process 1 `subj2` subject frailty variance for process 2 `cov` subject frailty covariance
`hazard`	a list describing the baseline hazard. It has components `breaks1` matrix of breakpoints in the hazard for each stratum for process 1 `breaks2` matrix of breakpoints in the hazard for each stratum for process 2 `hazard1` matrix of hazards in each interval for each stratum for process 1 `hazard2` matrix of hazards in each interval for each stratum for process 1
`summaries`	a list of summary matrices. It has components `regression` summary for the regression coefficients `dispersion` summary for the dispersion parameters

Emmanuel Sharef ess28@cornell.edu

E. Sharef and R. Strawderman. “A nested frailty model for clustered bivariate recurrent events”, in preparation.

summary.bivrec, plot.bivrec

    data(m10Ji5)
    
    fit <- bivrec( Surv2(start, stop, delta, Delta) ~ 
                Z1 + cluster(i) + id(j), data = m10Ji5 )
                
    summary(fit)
    plot(fit)