bootcov2: Boostrapping algorithm for 'coxed'

In coxed: Duration-Based Quantities of Interest for the Cox Proportional Hazards Model

Description

This function uses bootstrapping to create standard errors and confidence intervals for the quantities produced by the coxed() function. It is adapted from the bootcov function in the rms package. It is called by the coxed function and is not intended to be used by itself. Please refer to the original bootcov function for general bootstrapping applications.

Usage

bootcov2(fit, cluster, B = 200, fitter, coef.reps = TRUE,
  loglik = FALSE, pr = FALSE, maxit = 15, group = NULL,
  stat = NULL)

Arguments

fit	an estimated Cox proportional hazards model object with class "coxph" or "cph"
cluster	a variable indicating groupings. cluster may be any type of vector (factor, character, integer). Unique values of cluster indicate possibly correlated groupings of observations. Note the data used in the fit and stored in fit$x and fit$y may have had observations containing missing values deleted. It is assumed that if there were any NAs, an naresid function exists for the class of fit. This function restores NAs so that the rows of the design matrix coincide with cluster
B	Number of bootstrap simulation iterations
fitter	the name of a function with arguments (x,y) that will fit bootstrap samples. Default is taken from the class of fit if it is ols, lrm, cph, psm, Rq. If fitter="tvc" the function employs agreg.fit
coef.reps	set to TRUE if you want to store a matrix of all bootstrap regression coefficient estimates in the returned component boot.Coef.
loglik	set to TRUE to store -2 log likelihoods for each bootstrap model, evaluated against the original x and y data. The default is to do this when coef.reps is specified as TRUE. The use of loglik=TRUE assumes that an oos.loglik method exists for the type of model being analyzed, to calculate out-of-sample -2 log likelihoods (see rmsMisc). After the B -2 log likelihoods (stored in the element named boot.loglik in the returned fit object), the B+1 element is the -2 log likelihood for the original model fit
pr	set to TRUE to print the current sample number to monitor progress
maxit	maximum number of iterations, to pass to fitter
group	a grouping variable used to stratify the sample upon bootstrapping. This allows one to handle k-sample problems, i.e., each bootstrap sample will be forced to select the same number of observations from each level of group as the number appearing in the original dataset. You may specify both group and cluster
stat	a single character string specifying the name of a stats element produced by the fitting function to save over the bootstrap repetitions. The vector of saved statistics will be in the boot.stats part of the list returned by bootcov

Details

This function contains the same code as the bootcov function in the rms package, with a few alterations to work better with the coxed function. First, we output a result attribute b.ind, which contains the observation numbers from the estimation sample that are drawn with replacement to produce the bootstrap sample and takes into account clustering. Second, we program a new class, tvc, for fitter to use agreg.fit instead of coxph.fit when the data contain time-varying covariates.

Author(s)

Jonathan Kropko <[e-mail]> and Jeffrey J. Harden <[e-mail]>, based on the code for the bootcov function in the rms package by Frank Harrell and Bill Pikounis

See Also

coxed, coxph, cph, bootcov

coxed documentation built on Aug. 2, 2020, 9:07 a.m.