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,
  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).

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:

1. 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.

2. 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

See also

forestsearch for initial subgroup identification bootstrap_results for the core bootstrap worker function build_cox_formula for Cox formula construction fit_cox_models for Cox model fitting

Examples

if (FALSE) { # \dontrun{
# Run ForestSearch
fs_result <- forestsearch(
  df.analysis = mydata,
  outcome.name = "time",
  event.name = "status",
  treat.name = "treatment",
  confounders.name = c("age", "sex", "stage")
)

# Run bootstrap with bias correction
boot_results <- forestsearch_bootstrap_dofuture(
  fs.est = fs_result,
  nb_boots = 1000,
  parallel_args = list(
    plan = "multisession",
    workers = 6,
    show_message = TRUE
  ),
  create_summary = TRUE,
  create_plots = TRUE
)

# View results
print(boot_results$FSsg_tab)
print(boot_results$summary$table)

# Check success rate
mean(!is.na(boot_results$results$H_biasadj_2))
} # }