Run estimation for a method object

S4 generic that dispatches to the appropriate estimation logic based on the method class. Each method subclass (e.g., ec_ipw_method, did_ec_ipw_method) implements its own estimate() method containing the full estimation pipeline.

Usage

estimate(method, ...)

# S4 method for class 'ec_ipw_method'
estimate(
  method,
  data,
  outcomes,
  treatment,
  trial_status,
  covariates,
  alpha = 0.05,
  quiet = TRUE
)

# S4 method for class 'did_ec_ipw_method'
estimate(
  method,
  data,
  outcomes,
  treatment,
  trial_status,
  covariates,
  alpha = 0.05,
  quiet = TRUE,
  T_cross
)

# S4 method for class 'did_ec_aipw_method'
estimate(
  method,
  data,
  outcomes,
  treatment,
  trial_status,
  covariates,
  alpha = 0.05,
  quiet = TRUE,
  T_cross
)

# S4 method for class 'did_ec_or_method'
estimate(
  method,
  data,
  outcomes,
  treatment,
  trial_status,
  covariates,
  alpha = 0.05,
  quiet = TRUE,
  T_cross
)

# S4 method for class 'ec_aipw_method'
estimate(
  method,
  data,
  outcomes,
  treatment,
  trial_status,
  covariates,
  alpha = 0.05,
  quiet = TRUE
)

# S4 method for class 'scm_method'
estimate(
  method,
  data,
  outcomes,
  treatment,
  trial_status,
  covariates,
  alpha = 0.05,
  quiet = TRUE,
  T_cross
)

Arguments

method

An S4 method object (e.g., from ec_ipw).

...

Additional method-specific arguments.

data

Data frame with all subjects (RCT + external controls).

outcomes

Character vector of outcome column names.

treatment

Name of the treatment column.

trial_status

Name of the trial participation column.

covariates

Character vector of covariate column names.

alpha

Significance level, more than 0 and less than 1 (default 0.05).

quiet

Logical. Suppress output (default TRUE).

T_cross

Integer crossover time point (OLE methods only).

Value

For primary methods, a list with results and borrow_weight; for OLE methods, a data frame. See run_analysis for the columns and row names.

Examples

method <- ec_ipw(ps_formula = "S ~ x1 + x2 + x3 + x4 + x5")
estimate(
  method,
  data = SyntheticData,
  outcomes = c("y1", "y2"),
  treatment = "A",
  trial_status = "S",
  covariates = c("x1", "x2", "x3", "x4", "x5")
)
#> $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
#>