boot_ebmstate function

Bootstrap samples and bootstrap interval estimates

This function computes bootstrap samples of regression coefficients, cumulative hazard functions, and transition probability functions.

boot_ebmstate(
  mstate_data = NULL,
  which_group = NULL,
  min_nr_samples = NULL,
  patient_data = NULL,
  initial_state = NULL,
  tmat = NULL,
  time_model = NULL,
  backup_file = NULL,
  input_file = NULL,
  coxrfx_args = NULL,
  msfit_args = NULL,
  probtrans_args = NULL
)

Arguments

• mstate_data: A data frame with outcome and covariate data in long format.
• which_group: A character vector with the same meaning as the groups argument of the function CoxRFX but named (with the covariate names).
• min_nr_samples: The confidence interval of any coefficient is based on a number of bootstrap samples at least as high as this argument. See details.
• patient_data: The covariate data for which the estimates of cumulative hazards and transition probabilities are computed. Must contain: one row of data for each transition, all the covariate columns in the fitted model, and also the 'strata' column.
• initial_state: The initial state for which transition probability estimates should be computed
• tmat: Transition matrix for the multi-state model, as obtained by running mstate::transMat
• time_model: The model of time-dependency: either 'clockforward' or 'clockreset'.
• backup_file: Path to file. Objects generated while the present function is running are stored in this file. This avoids losing all estimates if and when the algorithm breaks down. See argument input_file.
• input_file: Path to backup_file (see argument backup_file). If this argument is given, all other arguments should be NULL.
• coxrfx_args: Named list with arguments to the CoxRFX function other than Z,surv and groups.
• msfit_args: Named list with arguments to the msfit_generic.coxrfx function other than object,newdata and trans.
• probtrans_args: Named list with arguments to the probtrans_ebmstate function other than initia_state,cumhaz and model.

Returns

A list with: 95% bootstrap intervals for each regression coefficient and for transition probabilities; bootstrap samples of regression coefficients, cumulative hazards and transition probabilities.

Details

In a given bootstrap sample there might not be enough information to generate estimates for all coefficients. If a covariate has little or no variation in a given bootstrap sample, no estimate of its coefficient will be computed. The present function will keep taking bootstrap samples until every coefficient has been estimated at least min_nr_samples times. covariate_df should only contain the covariates of the model one wishes to estimate.

Author(s)

Rui Costa