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Combine Model Summary and E-Value Calculation for Various Causal Models

Source: R/margot_model_evalue.R
margot_model_evalue.Rd

This function creates a summary table from the output of various causal models, including `lmtp::lmtp_contrast()`, `grf::causal_forest()`, and `grf::multi_arm_causal_forest()`. It calculates E-values for the estimates to assess the potential impact of unmeasured confounding, appending these values to the summary table.

Usage

margot_model_evalue(
  model_output,
  scale = c("RD", "RR"),
  new_name = "character_string",
  delta = 1,
  sd = 1,
  subset = NULL
)

Arguments

model_output

The output from a supported causal model. Supported types include:

  • Output from `lmtp::lmtp_contrast()`

  • Output from `grf::causal_forest()`

  • Output from `grf::multi_arm_causal_forest()`

  • A data frame with columns 'estimate' and 'std.err'

scale

Character string specifying the scale of the estimate to be used in the summary table and E-value calculation. Valid options are "RD" (risk difference) or "RR" (risk ratio). Default is "RD". This parameter is ignored for causal forest models, which always use "RD".

new_name

Character string to name the row(s) in the output summary table, representing the treatment contrast(s). For multi-arm causal forests, this will be combined with the contrast information.

delta

The hypothesized increase in outcome for RD scale calculations. Used only when `scale` is "RD". Default value is 1.

sd

The standard deviation of the outcome for RD scale calculations. Used only when `scale` is "RD". Default value is 1.

subset

An optional logical vector for subsetting the data when the model is a `grf` model. Default is `NULL`.

Value

A data frame with the original estimates and their E-values. The table includes columns for the estimate (either RD or RR), its confidence interval, E-Value, and the E-Value lower bound. For multi-arm causal forests, multiple rows will be returned, one for each contrast.

Examples

if (FALSE) { # \dontrun{
# For lmtp_contrast output
summary_evalues <- margot_model_evalue(
  model_output = lmtp_contrast_output,
  scale = "RD",
  new_name = "Treatment Effect"
)

# For causal_forest output
cf_summary <- margot_model_evalue(
  model_output = causal_forest_output,
  new_name = "Causal Forest Effect"
)

# For multi_arm_causal_forest output
macf_summary <- margot_model_evalue(
  model_output = multi_arm_cf_output,
  new_name = "Multi-Arm Effect"
)

# For direct input of estimate and standard error
direct_summary <- margot_model_evalue(
  model_output = data.frame(estimate = 0.5, std.err = 0.1),
  new_name = "Direct Effect"
)
} # }