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EC-AIPW method

Source: R/ec_aipw.R
ec_aipw.Rd

Creates a method object for augmented IPW estimation with external control borrowing (Zhou et al., 2024). Augments the IPW estimator with an outcome regression model for improved efficiency. Pass to setup_analysis_primary and run_analysis.

Usage

ec_aipw(
  ps_formula,
  outcome_formula,
  weight = NULL,
  bootstrap = NULL,
  bootstrap_ci_type = NULL
)

Arguments

ps_formula

Formula string for the propensity score model predicting trial participation.

outcome_formula

Character vector of outcome model formulas, one per time point (e.g., c("y1 ~ x1 + x2", "y2 ~ x1 + x2")).

weight

Borrowing weight. NULL (default) for data-adaptive optimal weight, 0 for RCT-only, or a value in (0, 1].

bootstrap

Number of bootstrap replicates, or NULL (default) for sandwich variance with normal CIs.

bootstrap_ci_type

Bootstrap CI type. Defaults to "perc".

Value

An S4 object of class ec_aipw_method.

References

Zhou et al. (2024). Causal estimators for incorporating external controls in randomized trials with longitudinal outcomes. JRSS-A. doi:10.1093/jrsssa/qnae075

Examples

# optimal weight, sandwich SE
ec_aipw(
  ps_formula = "S ~ x1 + x2 + x3 + x4 + x5",
  outcome_formula = c(
    "y1 ~ x1 + x2 + x3 + x4 + x5",
    "y2 ~ x1 + x2 + x3 + x4 + x5"
  )
)
#> An object of class "ec_aipw_method"
#> Slot "ps_formula":
#> [1] "S ~ x1 + x2 + x3 + x4 + x5"
#> 
#> Slot "outcome_formula":
#> [1] "y1 ~ x1 + x2 + x3 + x4 + x5" "y2 ~ x1 + x2 + x3 + x4 + x5"
#> 
#> Slot "weight":
#> NULL
#> 
#> Slot "bootstrap":
#> NULL
#> 
#> Slot "bootstrap_ci_type":
#> NULL
#> 
#> Slot "optimal_weight_flag":
#> [1] FALSE
#> 
#> Slot "wt":
#> [1] 0
#> 
#> Slot "model_form_piS":
#> [1] ""
#> 
#> Slot "model_form_piA":
#> [1] ""
#> 
#> Slot "model_form_mu0_ext":
#> [1] ""
#> 
#> Slot "model_form_mu0_rct":
#> [1] ""
#> 
#> Slot "model_form_mu1_rct":
#> [1] ""
#> 
#> Slot "method_name":
#> [1] "EC-AIPW"
#> 
#> Slot "bootstrap_flag":
#> [1] FALSE
#> 
#> Slot "bootstrap_obj":
#> <bootstrap_obj>
#>   Replicates: 500 
#>   CI type: Percentile 
#> 

# no borrowing
ec_aipw(
  ps_formula = "S ~ x1 + x2 + x3 + x4 + x5",
  outcome_formula = c(
    "y1 ~ x1 + x2 + x3 + x4 + x5",
    "y2 ~ x1 + x2 + x3 + x4 + x5"
  ),
  weight = 0
)
#> An object of class "ec_aipw_method"
#> Slot "ps_formula":
#> [1] "S ~ x1 + x2 + x3 + x4 + x5"
#> 
#> Slot "outcome_formula":
#> [1] "y1 ~ x1 + x2 + x3 + x4 + x5" "y2 ~ x1 + x2 + x3 + x4 + x5"
#> 
#> Slot "weight":
#> [1] 0
#> 
#> Slot "bootstrap":
#> NULL
#> 
#> Slot "bootstrap_ci_type":
#> NULL
#> 
#> Slot "optimal_weight_flag":
#> [1] FALSE
#> 
#> Slot "wt":
#> [1] 0
#> 
#> Slot "model_form_piS":
#> [1] ""
#> 
#> Slot "model_form_piA":
#> [1] ""
#> 
#> Slot "model_form_mu0_ext":
#> [1] ""
#> 
#> Slot "model_form_mu0_rct":
#> [1] ""
#> 
#> Slot "model_form_mu1_rct":
#> [1] ""
#> 
#> Slot "method_name":
#> [1] "EC-AIPW"
#> 
#> Slot "bootstrap_flag":
#> [1] FALSE
#> 
#> Slot "bootstrap_obj":
#> <bootstrap_obj>
#>   Replicates: 500 
#>   CI type: Percentile 
#> 

# fixed weight with bootstrap
ec_aipw(
  ps_formula = "S ~ x1 + x2 + x3 + x4 + x5",
  outcome_formula = c(
    "y1 ~ x1 + x2 + x3 + x4 + x5",
    "y2 ~ x1 + x2 + x3 + x4 + x5"
  ),
  weight = 0.3,
  bootstrap = 500
)
#> An object of class "ec_aipw_method"
#> Slot "ps_formula":
#> [1] "S ~ x1 + x2 + x3 + x4 + x5"
#> 
#> Slot "outcome_formula":
#> [1] "y1 ~ x1 + x2 + x3 + x4 + x5" "y2 ~ x1 + x2 + x3 + x4 + x5"
#> 
#> Slot "weight":
#> [1] 0.3
#> 
#> Slot "bootstrap":
#> [1] 500
#> 
#> Slot "bootstrap_ci_type":
#> [1] "perc"
#> 
#> Slot "optimal_weight_flag":
#> [1] FALSE
#> 
#> Slot "wt":
#> [1] 0
#> 
#> Slot "model_form_piS":
#> [1] ""
#> 
#> Slot "model_form_piA":
#> [1] ""
#> 
#> Slot "model_form_mu0_ext":
#> [1] ""
#> 
#> Slot "model_form_mu0_rct":
#> [1] ""
#> 
#> Slot "model_form_mu1_rct":
#> [1] ""
#> 
#> Slot "method_name":
#> [1] "EC-AIPW"
#> 
#> Slot "bootstrap_flag":
#> [1] TRUE
#> 
#> Slot "bootstrap_obj":
#> <bootstrap_obj>
#>   Replicates: 500 
#>   CI type: Percentile 
#>