Run an analysis with external control borrowing

Estimates treatment effects by combining randomized trial data with external controls. Choose a method, wrap it in an analysis object, and pass it here.

Usage

run_analysis(analysis_obj, quiet = TRUE)

Arguments

analysis_obj

An analysis object created by setup_analysis_primary or setup_analysis_OLE.

quiet

Logical. If TRUE, suppress printed output.

Value

For primary methods (ec_ipw(), ec_aipw()), a list with:

results

A data frame with one row for each outcome, named tau1, tau2, and so on, in the order of outcome_col_name. The columns are point_estimates, standard_deviation, and either lower_CI_normal and upper_CI_normal (sandwich variance, when bootstrap is NULL) or lower_CI_boot and upper_CI_boot. standard_deviation is the sandwich standard error, or the standard deviation of the bootstrap replicates.

borrow_weight

The borrowing weight that was used.

For OLE methods (did_ec_ipw(), did_ec_aipw(), did_ec_or(), scm()), a data frame with one row for each open-label visit and the columns point_estimates, standard_deviation (of the bootstrap replicates), lower_CI_boot, and upper_CI_boot. The rows are named tau<k> for k from T_cross + 1 to the number of outcomes, where k is the position of the outcome in outcome_col_name. List the outcomes in visit order.

Details

Six borrowing methods are available:

ec_ipw

Inverse probability weighting (primary analysis).

ec_aipw

Augmented inverse probability weighting (primary analysis).

did_ec_ipw

Difference-in-differences with IPW (open-label extension).

did_ec_aipw

Difference-in-differences with AIPW (open-label extension).

did_ec_or

Difference-in-differences with outcome regression (open-label extension).

scm

Synthetic control method (open-label extension).

See also

run_simulation for evaluating operating characteristics via Monte Carlo simulation.

Examples

method <- ec_ipw(ps_formula = "S ~ x1 + x2 + x3 + x4 + x5")
analysis <- setup_analysis_primary(
  data = SyntheticData,
  trial_status_col_name = "S",
  treatment_col_name = "A",
  outcome_col_name = c("y1", "y2"),
  covariates_col_name = c("x1", "x2", "x3", "x4", "x5"),
  method_weighting_obj = method
)
run_analysis(analysis)
#> $results
#>      point_estimates standard_deviation lower_CI_normal upper_CI_normal
#> tau1      -0.1971969          0.5134018       -1.203446        0.809052
#> tau2       0.4697209          0.5410007       -0.590621        1.530063
#> 
#> $borrow_weight
#> [1] 0.1475196
#>