ForestSearch Bootstrap with doFuture Parallelization

Orchestrates bootstrap analysis for ForestSearch using doFuture parallelization. Implements bias correction methods to adjust for optimism in subgroup selection.

Usage

forestsearch_bootstrap_dofuture(
  fs.est,
  nb_boots,
  seed = 8316951L,
  details = FALSE,
  show_three = FALSE,
  parallel_args = list()
)

Arguments

fs.est

List. ForestSearch results object from forestsearch. Must contain df.est (data frame) and args_call_all (list of arguments).

nb_boots

Integer. Number of bootstrap samples (recommend 500-1000).

seed

Integer. Random seed for reproducibility of bootstrap sample generation. Default 8316951L. The value is passed to both bootstrap_ystar (which constructs the $n \times B$ bootstrap index matrix) and bootstrap_results (which re-runs the ForestSearch algorithm on each replicate); both calls must use the same seed to ensure bootstrap index alignment. Set to NULL for a non-reproducible run.

details

Logical. If TRUE, prints detailed progress information. Default: FALSE.

show_three

Logical. If TRUE, shows verbose output for first 3 bootstrap iterations for debugging. Default: FALSE.

parallel_args

List. Parallelization configuration with elements:

plan: Character. One of "multisession", "multicore", "callr", or "sequential"
workers: Integer. Number of parallel workers
show_message: Logical. Show parallel setup messages

If empty list, inherits settings from original forestsearch call.

Value

List with the following components:

results: Data.table with bias-corrected estimates for each bootstrap iteration
SG_CIs: List of confidence intervals for H and Hc (raw and bias-corrected)
FSsg_tab: Formatted table of subgroup estimates
Ystar_mat: Matrix (nb_boots x n) of bootstrap sample indicators
H_estimates: Detailed estimates for subgroup H
Hc_estimates: Detailed estimates for subgroup Hc
summary: (If create_summary=TRUE) Enhanced summary with tables and diagnostics

Bias Correction Methods

Two bias correction approaches are implemented:

Method 1 (Simple Optimism): $$H_{adj1} = H_{obs} - (H^*_{*} - H^*_{obs})$$ where $H^*_{*}$ is the new subgroup HR on bootstrap data and $H^*_{obs}$ is the new subgroup HR on original data.
Method 2 (Double Bootstrap): $$H_{adj2} = 2 \times H_{obs} - (H_{*} + H^*_{*} - H^*_{obs})$$ where $H_{*}$ is the original subgroup HR on bootstrap data.

Variable Naming Convention

H: Original subgroup (harm/questionable, treat.recommend == 0)
Hc: Complement subgroup (recommend, treat.recommend == 1)
_obs: Estimate from original data
_star: Estimate from bootstrap data
_biasadj_1: Bias correction method 1
_biasadj_2: Bias correction method 2

Performance

Typical runtime: 1-5 seconds per bootstrap iteration. For 1000 bootstraps with 6 workers, expect 3-10 minutes total. Memory usage scales with dataset size and number of workers.

Requirements

Original fs.est must have identified a valid subgroup
Requires packages: data.table, foreach, doFuture, survival
For plots: requires ggplot2