Description Usage Arguments Value
This internal function bootstraps the observed data (i.e., resamples the observed data set with replacement to construct bootstrap confidence intervals and standard errors). Then, the function simulates data using the resampled dataset to estimate the survival outcome, binary endoffollowup outcome, or continuous endoffollowup outcome.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44  bootstrap_helper(
r,
time_points,
obs_data,
bootseeds,
outcome_type,
intvars,
interventions,
int_times,
ref_int,
covparams,
covnames,
covtypes,
covfits_custom,
covpredict_custom,
basecovs,
histvars,
histvals,
histories,
ymodel,
yrestrictions,
compevent_restrictions,
restrictions,
comprisk,
compevent_model,
time_name,
outcome_name,
compevent_name,
ranges,
yrange,
compevent_range,
parallel,
ncores,
max_visits,
hazardratio,
intcomp,
boot_diag,
nsimul,
baselags,
below_zero_indicator,
min_time,
show_progress,
pb
)

r 
Integer specifying the index of the current iteration of the bootstrap. 
time_points 
Number of time points to simulate. 
obs_data 
Data table containing the observed data. 
bootseeds 
Vector of integers specifying the seeds. One seed is used to initialize each bootstrap iteration. 
outcome_type 
Character string specifying the "type" of the outcome. The possible "types" are: 
intvars 
List, whose elements are vectors of character strings. The kth vector in 
interventions 
List, whose elements are lists of vectors. Each list in 
int_times 
List, whose elements are lists of vectors. The kth list in 
ref_int 
Integer denoting the intervention to be used as the
reference for calculating the risk ratio and risk difference. 0 denotes the
natural course, while subsequent integers denote userspecified
interventions in the order that they are
named in 
covparams 
List of vectors, where each vector contains information for
one parameter used in the modeling of the timevarying covariates (e.g.,
model statement, family, link function, etc.). Each vector
must be the same length as 
covnames 
Vector of character strings specifying the names of the timevarying covariates in 
covtypes 
Vector of character strings specifying the "type" of each timevarying covariate included in 
covfits_custom 
Vector containing custom fit functions for timevarying covariates that
do not fall within the predefined covariate types. It should be in
the same order 
covpredict_custom 
Vector containing custom prediction functions for timevarying
covariates that do not fall within the predefined covariate types.
It should be in the same order as 
basecovs 
Vector of character strings specifying the names of baseline covariates in 
histvars 
List of vectors. The kth vector specifies the names of the variables for which the kth history function
in 
histvals 
List of length two. The first element is a numeric vector specifying the lags used in the model statements (e.g., if 
histories 
Vector of history functions to apply to the variables specified in 
ymodel 
Model statement for the outcome variable. 
yrestrictions 
List of vectors. Each vector containins as its first entry
a condition and its second entry an integer. When the
condition is 
compevent_restrictions 
List of vectors. Each vector containins as its first entry
a condition and its second entry an integer. When the
condition is 
restrictions 
List of vectors. Each vector contains as its first entry a covariate for which
a priori knowledge of its distribution is available; its second entry a condition
under which no knowledge of its distribution is available and that must be 
comprisk 
Logical scalar indicating the presence of a competing event. 
compevent_model 
Model statement for the competing event variable. 
time_name 
Character string specifying the name of the time variable in 
outcome_name 
Character string specifying the name of the outcome variable in 
compevent_name 
Character string specifying the name of the competing event variable in 
ranges 
List of vectors. Each vector contains the minimum and
maximum values of one of the covariates in 
yrange 
Vector containing the minimum and maximum values of the outcome variable in the observed dataset. 
compevent_range 
Vector containing the minimum and maximum values of the competing event variable in the observed dataset. 
parallel 
Logical scalar indicating whether to parallelize simulations of different interventions to multiple cores. 
ncores 
Integer specifying the number of cores to use in parallel simulation. 
max_visits 
A vector of one or more values denoting the maximum number of times
a binary covariate representing a visit process may be missed before
the individual is censored from the data (in the observed data) or
a visit is forced (in the simulated data). Multiple values exist in the
vector when the modeling of more than covariate is attached to a visit
process. A value of 
hazardratio 
Logical scalar indicating whether the hazard ratio should be computed between two interventions. 
intcomp 
List of two numbers indicating a pair of interventions to be compared by a hazard ratio.
The default is 
boot_diag 
Logical scalar indicating whether to return the coefficients, standard errors, and variancecovariance matrices of the parameters of the fitted models in the bootstrap samples. The default is 
nsimul 
Number of subjects for whom to simulate data. By default, this argument is set
equal to the number of subjects in 
baselags 
Logical scalar for specifying the convention used for lagi and lag_cumavgi terms in the model statements when prebaseline times are not
included in 
below_zero_indicator 
Logical scalar indicating whether the observed data set contains rows for time t < 0. 
min_time 
Numeric scalar specifying lowest value of time t in the observed data set. 
show_progress 
Logical scalar indicating whether to print a progress bar for the number of bootstrap samples completed in the R console. This argument is only applicable when 
pb 
Progress bar R6 object. See 
A list with the following components:
Result 
Matrix containing risks over time under the natural course and under each userspecific intervention. 
ResultRatio 
Matrix containing risk ratios over time under the natural course and under each userspecific intervention. 
ResultDiff 
Matrix containing risk differences over time under the natural course and under each userspecific intervention. 
bootcoeffs 
List of the coefficients of the fitted models. If the argument 
bootstderrs 
List of the standard errors of the coefficients of the fitted models. If the argument 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.